Category Archives: Predictive Analytics

5 Hot Topics in Credit Scoring from Edinburgh

Analytics Hand 5 Hot Topics in Credit Scoring from Edinburgh

If you want to seek out the newest ideas in credit scoring — a field that advances more rapidly than many people may suspect — the best place is the annual Edinburgh Credit Scoring and Control conference (well, next to our own FICO World). At this year’s conference, I started thinking about what has changed in the world of credit scoring since my first visit to the conference almost 20 years ago.  My phone has certainly gotten smarter in that time – so what is smarter within credit scoring?

With around 70 presentations, the key questions remain the same:

  • What data is available and useful?
  • How do I best gain intelligence from the data?
  • How do I best action the intelligence?
  • How do I comply with the ever-increasing regulations?

Regarding the data, the answer is more and more! We all know that the digital age is creating vast quantities of data that is growing exponentially.  Compared to 20 years ago, there are many new and ‘alternative’ data sources that are now being used to better inform creditworthiness for certain consumer types, include psychometric data, telecoms data, social network data and transaction data.

In terms of turning this data into intelligence, artificial intelligence (AI) and specifically machine learning algorithms are being investigated and used on an increased scale.  With ever greater volume and variety of data coupled with vastly increased computer processing power, machine learning approaches to drive the value from the data are proving more and more useful.

This same processing power for the development of machine learning models is also helping with the ease of deployment of these types of models, which has been troublesome in the past in terms of both speed of deployment and speed of execution.

The evolution from predictive analytics (models that order by a single outcome of interest) to prescriptive analytics (also referred to as decision optimization, identifying the best action to take considering multiple outcome metrics or dimensions) is vastly improving the business outcomes of decisions across the credit lifecycle, from origination through to collection and recovery.  Prescriptive analytics provides the ability to make better, more informed decisions by taking account of multiple (often conflicting) objectives — for example, increasing accept rates whilst controlling losses.

Since the economic crisis there is also greater focus on modelling stressed situations, and how these stresses impact both the likely performance of individual consumers as well as total portfolios. Predictive models help lenders comply with regulations such as Basel and IFRS 9.  As compliance is gained and maintained, we are seeing these same models being used to drive business value through better insights and understanding of portfolios, acting as key inputs to both what-if scenario analysis and decision optimisation capabilities.

FICO data scientists and experts, all of whom blog here, presented no fewer than five sessions at Edinburgh this year on hot topics related to these areas I have described.

Gerald has already written a post on his talk, about new risk analytics to stress-test individual consumers – we’ll be sharing more insights on our topics here, and shining a spotlight on new trends in credit scoring.

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FICO

Connected Cars, Autonomous Vehicles, And The IoT

In the tech world in 2017, several trends emerged as signals amid the noise, signifying much larger changes to come.

As we noted in last year’s More Than Noise list, things are changing—and the changes are occurring in ways that don’t necessarily fit into the prevailing narrative.

While many of 2017’s signals have a dark tint to them, perhaps reflecting the times we live in, we have sought out some rays of light to illuminate the way forward. The following signals differ considerably, but understanding them can help guide businesses in the right direction for 2018 and beyond.

SAP Q417 DigitalDoubles Feature1 Image2 1024x572 Connected Cars, Autonomous Vehicles, And The IoT

When a team of psychologists, linguists, and software engineers created Woebot, an AI chatbot that helps people learn cognitive behavioral therapy techniques for managing mental health issues like anxiety and depression, they did something unusual, at least when it comes to chatbots: they submitted it for peer review.

Stanford University researchers recruited a sample group of 70 college-age participants on social media to take part in a randomized control study of Woebot. The researchers found that their creation was useful for improving anxiety and depression symptoms. A study of the user interaction with the bot was submitted for peer review and published in the Journal of Medical Internet Research Mental Health in June 2017.

While Woebot may not revolutionize the field of psychology, it could change the way we view AI development. Well-known figures such as Elon Musk and Bill Gates have expressed concerns that artificial intelligence is essentially ungovernable. Peer review, such as with the Stanford study, is one way to approach this challenge and figure out how to properly evaluate and find a place for these software programs.

The healthcare community could be onto something. We’ve already seen instances where AI chatbots have spun out of control, such as when internet trolls trained Microsoft’s Tay to become a hate-spewing misanthrope. Bots are only as good as their design; making sure they stay on message and don’t act in unexpected ways is crucial.

SAP Q417 DigitalDoubles Feature1 Image3 Connected Cars, Autonomous Vehicles, And The IoTThis is especially true in healthcare. When chatbots are offering therapeutic services, they must be properly designed, vetted, and tested to maintain patient safety.

It may be prudent to apply the same level of caution to a business setting. By treating chatbots as if they’re akin to medicine or drugs, we have a model for thorough vetting that, while not perfect, is generally effective and time tested.

It may seem like overkill to think of chatbots that manage pizza orders or help resolve parking tickets as potential health threats. But it’s already clear that AI can have unintended side effects that could extend far beyond Tay’s loathsome behavior.

For example, in July, Facebook shut down an experiment where it challenged two AIs to negotiate with each other over a trade. When the experiment began, the two chatbots quickly went rogue, developing linguistic shortcuts to reduce negotiating time and leaving their creators unable to understand what they were saying.

The implications are chilling. Do we want AIs interacting in a secret language because designers didn’t fully understand what they were designing?

In this context, the healthcare community’s conservative approach doesn’t seem so farfetched. Woebot could ultimately become an example of the kind of oversight that’s needed for all AIs.

Meanwhile, it’s clear that chatbots have great potential in healthcare—not just for treating mental health issues but for helping patients understand symptoms, build treatment regimens, and more. They could also help unclog barriers to healthcare, which is plagued worldwide by high prices, long wait times, and other challenges. While they are not a substitute for actual humans, chatbots can be used by anyone with a computer or smartphone, 24 hours a day, seven days a week, regardless of financial status.

Finding the right governance for AI development won’t happen overnight. But peer review, extensive internal quality analysis, and other processes will go a long way to ensuring bots function as expected. Otherwise, companies and their customers could pay a big price.

SAP Q417 DigitalDoubles Feature1 Image4 1024x572 Connected Cars, Autonomous Vehicles, And The IoT

Elon Musk is an expert at dominating the news cycle with his sci-fi premonitions about space travel and high-speed hyperloops. However, he captured media attention in Australia in April 2017 for something much more down to earth: how to deal with blackouts and power outages.

In 2016, a massive blackout hit the state of South Australia following a storm. Although power was restored quickly in Adelaide, the capital, people in the wide stretches of arid desert that surround it spent days waiting for the power to return. That hit South Australia’s wine and livestock industries especially hard.

South Australia’s electrical grid currently gets more than half of its energy from wind and solar, with coal and gas plants acting as backups for when the sun hides or the wind doesn’t blow, according to ABC News Australia. But this network is vulnerable to sudden loss of generation—which is exactly what happened in the storm that caused the 2016 blackout, when tornadoes ripped through some key transmission lines. Getting the system back on stable footing has been an issue ever since.

Displaying his usual talent for showmanship, Musk stepped in and promised to build the world’s largest battery to store backup energy for the network—and he pledged to complete it within 100 days of signing the contract or the battery would be free. Pen met paper with South Australia and French utility Neoen in September. As of press time in November, construction was underway.

For South Australia, the Tesla deal offers an easy and secure way to store renewable energy. Tesla’s 129 MWh battery will be the most powerful battery system in the world by 60% once completed, according to Gizmodo. The battery, which is stationed at a wind farm, will cover temporary drops in wind power and kick in to help conventional gas and coal plants balance generation with demand across the network. South Australian citizens and politicians largely support the project, which Tesla claims will be able to power 30,000 homes.

Until Musk made his bold promise, batteries did not figure much in renewable energy networks, mostly because they just aren’t that good. They have limited charges, are difficult to build, and are difficult to manage. Utilities also worry about relying on the same lithium-ion battery technology as cellphone makers like Samsung, whose Galaxy Note 7 had to be recalled in 2016 after some defective batteries burst into flames, according to CNET.

SAP Q417 DigitalDoubles Feature1 Image5 Connected Cars, Autonomous Vehicles, And The IoTHowever, when made right, the batteries are safe. It’s just that they’ve traditionally been too expensive for large-scale uses such as renewable power storage. But battery innovations such as Tesla’s could radically change how we power the economy. According to a study that appeared this year in Nature, the continued drop in the cost of battery storage has made renewable energy price-competitive with traditional fossil fuels.

This is a massive shift. Or, as David Roberts of news site Vox puts it, “Batteries are soon going to disrupt power markets at all scales.” Furthermore, if the cost of batteries continues to drop, supply chains could experience radical energy cost savings. This could disrupt energy utilities, manufacturing, transportation, and construction, to name just a few, and create many opportunities while changing established business models. (For more on how renewable energy will affect business, read the feature “Tick Tock” in this issue.)

Battery research and development has become big business. Thanks to electric cars and powerful smartphones, there has been incredible pressure to make more powerful batteries that last longer between charges.

The proof of this is in the R&D funding pudding. A Brookings Institution report notes that both the Chinese and U.S. governments offer generous subsidies for lithium-ion battery advancement. Automakers such as Daimler and BMW have established divisions marketing residential and commercial energy storage products. Boeing, Airbus, Rolls-Royce, and General Electric are all experimenting with various electric propulsion systems for aircraft—which means that hybrid airplanes are also a possibility.

Meanwhile, governments around the world are accelerating battery research investment by banning internal combustion vehicles. Britain, France, India, and Norway are seeking to go all electric as early as 2025 and by 2040 at the latest.

In the meantime, expect huge investment and new battery innovation from interested parties across industries that all share a stake in the outcome. This past September, for example, Volkswagen announced a €50 billion research investment in batteries to help bring 300 electric vehicle models to market by 2030.

SAP Q417 DigitalDoubles Feature1 Image6 1024x572 Connected Cars, Autonomous Vehicles, And The IoT

At first, it sounds like a narrative device from a science fiction novel or a particularly bad urban legend.

Powerful cameras in several Chinese cities capture photographs of jaywalkers as they cross the street and, several minutes later, display their photograph, name, and home address on a large screen posted at the intersection. Several days later, a summons appears in the offender’s mailbox demanding payment of a fine or fulfillment of community service.

As Orwellian as it seems, this technology is very real for residents of Jinan and several other Chinese cities. According to a Xinhua interview with Li Yong of the Jinan traffic police, “Since the new technology has been adopted, the cases of jaywalking have been reduced from 200 to 20 each day at the major intersection of Jingshi and Shungeng roads.”

The sophisticated cameras and facial recognition systems already used in China—and their near–real-time public shaming—are an example of how machine learning, mobile phone surveillance, and internet activity tracking are being used to censor and control populations. Most worryingly, the prospect of real-time surveillance makes running surveillance states such as the former East Germany and current North Korea much more financially efficient.

According to a 2015 discussion paper by the Institute for the Study of Labor, a German research center, by the 1980s almost 0.5% of the East German population was directly employed by the Stasi, the country’s state security service and secret police—1 for every 166 citizens. An additional 1.1% of the population (1 for every 66 citizens) were working as unofficial informers, which represented a massive economic drain. Automated, real-time, algorithm-driven monitoring could potentially drive the cost of controlling the population down substantially in police states—and elsewhere.

We could see a radical new era of censorship that is much more manipulative than anything that has come before. Previously, dissidents were identified when investigators manually combed through photos, read writings, or listened in on phone calls. Real-time algorithmic monitoring means that acts of perceived defiance can be identified and deleted in the moment and their perpetrators marked for swift judgment before they can make an impression on others.

SAP Q417 DigitalDoubles Feature1 Image7 Connected Cars, Autonomous Vehicles, And The IoTBusinesses need to be aware of the wider trend toward real-time, automated censorship and how it might be used in both commercial and governmental settings. These tools can easily be used in countries with unstable political dynamics and could become a real concern for businesses that operate across borders. Businesses must learn to educate and protect employees when technology can censor and punish in real time.

Indeed, the technologies used for this kind of repression could be easily adapted from those that have already been developed for businesses. For instance, both Facebook and Google use near–real-time facial identification algorithms that automatically identify people in images uploaded by users—which helps the companies build out their social graphs and target users with profitable advertisements. Automated algorithms also flag Facebook posts that potentially violate the company’s terms of service.

China is already using these technologies to control its own people in ways that are largely hidden to outsiders.

According to a report by the University of Toronto’s Citizen Lab, the popular Chinese social network WeChat operates under a policy its authors call “One App, Two Systems.” Users with Chinese phone numbers are subjected to dynamic keyword censorship that changes depending on current events and whether a user is in a private chat or in a group. Depending on the political winds, users are blocked from accessing a range of websites that report critically on China through WeChat’s internal browser. Non-Chinese users, however, are not subject to any of these restrictions.

The censorship is also designed to be invisible. Messages are blocked without any user notification, and China has intermittently blocked WhatsApp and other foreign social networks. As a result, Chinese users are steered toward national social networks, which are more compliant with government pressure.

China’s policies play into a larger global trend: the nationalization of the internet. China, Russia, the European Union, and the United States have all adopted different approaches to censorship, user privacy, and surveillance. Although there are social networks such as WeChat or Russia’s VKontakte that are popular in primarily one country, nationalizing the internet challenges users of multinational services such as Facebook and YouTube. These different approaches, which impact everything from data safe harbor laws to legal consequences for posting inflammatory material, have implications for businesses working in multiple countries, as well.

For instance, Twitter is legally obligated to hide Nazi and neo-fascist imagery and some tweets in Germany and France—but not elsewhere. YouTube was officially banned in Turkey for two years because of videos a Turkish court deemed “insulting to the memory of Mustafa Kemal Atatürk,” father of modern Turkey. In Russia, Google must keep Russian users’ personal data on servers located inside Russia to comply with government policy.

While China is a pioneer in the field of instant censorship, tech companies in the United States are matching China’s progress, which could potentially have a chilling effect on democracy. In 2016, Apple applied for a patent on technology that censors audio streams in real time—automating the previously manual process of censoring curse words in streaming audio.

SAP Q417 DigitalDoubles Feature1 Image8 1024x572 Connected Cars, Autonomous Vehicles, And The IoT

In March, after U.S. President Donald Trump told Fox News, “I think maybe I wouldn’t be [president] if it wasn’t for Twitter,” Twitter founder Evan “Ev” Williams did something highly unusual for the creator of a massive social network.

He apologized.

Speaking with David Streitfeld of The New York Times, Williams said, “It’s a very bad thing, Twitter’s role in that. If it’s true that he wouldn’t be president if it weren’t for Twitter, then yeah, I’m sorry.”

Entrepreneurs tend to be very proud of their innovations. Williams, however, offers a far more ambivalent response to his creation’s success. Much of the 2016 presidential election’s rancor was fueled by Twitter, and the instant gratification of Twitter attracts trolls, bullies, and bigots just as easily as it attracts politicians, celebrities, comedians, and sports fans.

Services such as Twitter, Facebook, YouTube, and Instagram are designed through a mix of look and feel, algorithmic wizardry, and psychological techniques to hang on to users for as long as possible—which helps the services sell more advertisements and make more money. Toxic political discourse and online harassment are unintended side effects of the economic-driven urge to keep users engaged no matter what.

Keeping users’ eyeballs on their screens requires endless hours of multivariate testing, user research, and algorithm refinement. For instance, Casey Newton of tech publication The Verge notes that Google Brain, Google’s AI division, plays a key part in generating YouTube’s video recommendations.

According to Jim McFadden, the technical lead for YouTube recommendations, “Before, if I watch this video from a comedian, our recommendations were pretty good at saying, here’s another one just like it,” he told Newton. “But the Google Brain model figures out other comedians who are similar but not exactly the same—even more adjacent relationships. It’s able to see patterns that are less obvious.”

SAP Q417 DigitalDoubles Feature1 Image9 Connected Cars, Autonomous Vehicles, And The IoTA never-ending flow of content that is interesting without being repetitive is harder to resist. With users glued to online services, addiction and other behavioral problems occur to an unhealthy degree. According to a 2016 poll by nonprofit research company Common Sense Media, 50% of American teenagers believe they are addicted to their smartphones.

This pattern is extending into the workplace. Seventy-five percent of companies told research company Harris Poll in 2016 that two or more hours a day are lost in productivity because employees are distracted. The number one reason? Cellphones and texting, according to 55% of those companies surveyed. Another 41% pointed to the internet.

Tristan Harris, a former design ethicist at Google, argues that many product designers for online services try to exploit psychological vulnerabilities in a bid to keep users engaged for longer periods. Harris refers to an iPhone as “a slot machine in my pocket” and argues that user interface (UI) and user experience (UX) designers need to adopt something akin to a Hippocratic Oath to stop exploiting users’ psychological vulnerabilities.

In fact, there is an entire school of study devoted to “dark UX”—small design tweaks to increase profits. These can be as innocuous as a “Buy Now” button in a visually pleasing color or as controversial as when Facebook tweaked its algorithm in 2012 to show a randomly selected group of almost 700,000 users (who had not given their permission) newsfeeds that skewed more positive to some users and more negative to others to gauge the impact on their respective emotional states, according to an article in Wired.

As computers, smartphones, and televisions come ever closer to convergence, these issues matter increasingly to businesses. Some of the universal side effects of addiction are lost productivity at work and poor health. Businesses should offer training and help for employees who can’t stop checking their smartphones.

Mindfulness-centered mobile apps such as Headspace, Calm, and Forest offer one way to break the habit. Users can also choose to break internet addiction by going for a walk, turning their computers off, or using tools like StayFocusd or Freedom to block addictive websites or apps.

Most importantly, companies in the business of creating tech products need to design software and hardware that discourages addictive behavior. This means avoiding bad designs that emphasize engagement metrics over human health. A world of advertising preroll showing up on smart refrigerator touchscreens at 2 a.m. benefits no one.

According to a 2014 study in Cyberpsychology, Behavior and Social Networking, approximately 6% of the world’s population suffers from internet addiction to one degree or another. As more users in emerging economies gain access to cheap data, smartphones, and laptops, that percentage will only increase. For businesses, getting a head start on stopping internet addiction will make employees happier and more productive. D!


About the Authors

Maurizio Cattaneo is Director, Delivery Execution, Energy, and Natural Resources, at SAP.

David Delaney is Global Vice President and Chief Medical Officer, SAP Health.

Volker Hildebrand is Global Vice President for SAP Hybris solutions.

Neal Ungerleider is a Los Angeles-based technology journalist and consultant.


Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.

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Let’s block ads! (Why?)

Digitalist Magazine

How CRM Software Can Save Time And Money

In the tech world in 2017, several trends emerged as signals amid the noise, signifying much larger changes to come.

As we noted in last year’s More Than Noise list, things are changing—and the changes are occurring in ways that don’t necessarily fit into the prevailing narrative.

While many of 2017’s signals have a dark tint to them, perhaps reflecting the times we live in, we have sought out some rays of light to illuminate the way forward. The following signals differ considerably, but understanding them can help guide businesses in the right direction for 2018 and beyond.

SAP Q417 DigitalDoubles Feature1 Image2 1024x572 How CRM Software Can Save Time And Money

When a team of psychologists, linguists, and software engineers created Woebot, an AI chatbot that helps people learn cognitive behavioral therapy techniques for managing mental health issues like anxiety and depression, they did something unusual, at least when it comes to chatbots: they submitted it for peer review.

Stanford University researchers recruited a sample group of 70 college-age participants on social media to take part in a randomized control study of Woebot. The researchers found that their creation was useful for improving anxiety and depression symptoms. A study of the user interaction with the bot was submitted for peer review and published in the Journal of Medical Internet Research Mental Health in June 2017.

While Woebot may not revolutionize the field of psychology, it could change the way we view AI development. Well-known figures such as Elon Musk and Bill Gates have expressed concerns that artificial intelligence is essentially ungovernable. Peer review, such as with the Stanford study, is one way to approach this challenge and figure out how to properly evaluate and find a place for these software programs.

The healthcare community could be onto something. We’ve already seen instances where AI chatbots have spun out of control, such as when internet trolls trained Microsoft’s Tay to become a hate-spewing misanthrope. Bots are only as good as their design; making sure they stay on message and don’t act in unexpected ways is crucial.

SAP Q417 DigitalDoubles Feature1 Image3 How CRM Software Can Save Time And MoneyThis is especially true in healthcare. When chatbots are offering therapeutic services, they must be properly designed, vetted, and tested to maintain patient safety.

It may be prudent to apply the same level of caution to a business setting. By treating chatbots as if they’re akin to medicine or drugs, we have a model for thorough vetting that, while not perfect, is generally effective and time tested.

It may seem like overkill to think of chatbots that manage pizza orders or help resolve parking tickets as potential health threats. But it’s already clear that AI can have unintended side effects that could extend far beyond Tay’s loathsome behavior.

For example, in July, Facebook shut down an experiment where it challenged two AIs to negotiate with each other over a trade. When the experiment began, the two chatbots quickly went rogue, developing linguistic shortcuts to reduce negotiating time and leaving their creators unable to understand what they were saying.

The implications are chilling. Do we want AIs interacting in a secret language because designers didn’t fully understand what they were designing?

In this context, the healthcare community’s conservative approach doesn’t seem so farfetched. Woebot could ultimately become an example of the kind of oversight that’s needed for all AIs.

Meanwhile, it’s clear that chatbots have great potential in healthcare—not just for treating mental health issues but for helping patients understand symptoms, build treatment regimens, and more. They could also help unclog barriers to healthcare, which is plagued worldwide by high prices, long wait times, and other challenges. While they are not a substitute for actual humans, chatbots can be used by anyone with a computer or smartphone, 24 hours a day, seven days a week, regardless of financial status.

Finding the right governance for AI development won’t happen overnight. But peer review, extensive internal quality analysis, and other processes will go a long way to ensuring bots function as expected. Otherwise, companies and their customers could pay a big price.

SAP Q417 DigitalDoubles Feature1 Image4 1024x572 How CRM Software Can Save Time And Money

Elon Musk is an expert at dominating the news cycle with his sci-fi premonitions about space travel and high-speed hyperloops. However, he captured media attention in Australia in April 2017 for something much more down to earth: how to deal with blackouts and power outages.

In 2016, a massive blackout hit the state of South Australia following a storm. Although power was restored quickly in Adelaide, the capital, people in the wide stretches of arid desert that surround it spent days waiting for the power to return. That hit South Australia’s wine and livestock industries especially hard.

South Australia’s electrical grid currently gets more than half of its energy from wind and solar, with coal and gas plants acting as backups for when the sun hides or the wind doesn’t blow, according to ABC News Australia. But this network is vulnerable to sudden loss of generation—which is exactly what happened in the storm that caused the 2016 blackout, when tornadoes ripped through some key transmission lines. Getting the system back on stable footing has been an issue ever since.

Displaying his usual talent for showmanship, Musk stepped in and promised to build the world’s largest battery to store backup energy for the network—and he pledged to complete it within 100 days of signing the contract or the battery would be free. Pen met paper with South Australia and French utility Neoen in September. As of press time in November, construction was underway.

For South Australia, the Tesla deal offers an easy and secure way to store renewable energy. Tesla’s 129 MWh battery will be the most powerful battery system in the world by 60% once completed, according to Gizmodo. The battery, which is stationed at a wind farm, will cover temporary drops in wind power and kick in to help conventional gas and coal plants balance generation with demand across the network. South Australian citizens and politicians largely support the project, which Tesla claims will be able to power 30,000 homes.

Until Musk made his bold promise, batteries did not figure much in renewable energy networks, mostly because they just aren’t that good. They have limited charges, are difficult to build, and are difficult to manage. Utilities also worry about relying on the same lithium-ion battery technology as cellphone makers like Samsung, whose Galaxy Note 7 had to be recalled in 2016 after some defective batteries burst into flames, according to CNET.

SAP Q417 DigitalDoubles Feature1 Image5 How CRM Software Can Save Time And MoneyHowever, when made right, the batteries are safe. It’s just that they’ve traditionally been too expensive for large-scale uses such as renewable power storage. But battery innovations such as Tesla’s could radically change how we power the economy. According to a study that appeared this year in Nature, the continued drop in the cost of battery storage has made renewable energy price-competitive with traditional fossil fuels.

This is a massive shift. Or, as David Roberts of news site Vox puts it, “Batteries are soon going to disrupt power markets at all scales.” Furthermore, if the cost of batteries continues to drop, supply chains could experience radical energy cost savings. This could disrupt energy utilities, manufacturing, transportation, and construction, to name just a few, and create many opportunities while changing established business models. (For more on how renewable energy will affect business, read the feature “Tick Tock” in this issue.)

Battery research and development has become big business. Thanks to electric cars and powerful smartphones, there has been incredible pressure to make more powerful batteries that last longer between charges.

The proof of this is in the R&D funding pudding. A Brookings Institution report notes that both the Chinese and U.S. governments offer generous subsidies for lithium-ion battery advancement. Automakers such as Daimler and BMW have established divisions marketing residential and commercial energy storage products. Boeing, Airbus, Rolls-Royce, and General Electric are all experimenting with various electric propulsion systems for aircraft—which means that hybrid airplanes are also a possibility.

Meanwhile, governments around the world are accelerating battery research investment by banning internal combustion vehicles. Britain, France, India, and Norway are seeking to go all electric as early as 2025 and by 2040 at the latest.

In the meantime, expect huge investment and new battery innovation from interested parties across industries that all share a stake in the outcome. This past September, for example, Volkswagen announced a €50 billion research investment in batteries to help bring 300 electric vehicle models to market by 2030.

SAP Q417 DigitalDoubles Feature1 Image6 1024x572 How CRM Software Can Save Time And Money

At first, it sounds like a narrative device from a science fiction novel or a particularly bad urban legend.

Powerful cameras in several Chinese cities capture photographs of jaywalkers as they cross the street and, several minutes later, display their photograph, name, and home address on a large screen posted at the intersection. Several days later, a summons appears in the offender’s mailbox demanding payment of a fine or fulfillment of community service.

As Orwellian as it seems, this technology is very real for residents of Jinan and several other Chinese cities. According to a Xinhua interview with Li Yong of the Jinan traffic police, “Since the new technology has been adopted, the cases of jaywalking have been reduced from 200 to 20 each day at the major intersection of Jingshi and Shungeng roads.”

The sophisticated cameras and facial recognition systems already used in China—and their near–real-time public shaming—are an example of how machine learning, mobile phone surveillance, and internet activity tracking are being used to censor and control populations. Most worryingly, the prospect of real-time surveillance makes running surveillance states such as the former East Germany and current North Korea much more financially efficient.

According to a 2015 discussion paper by the Institute for the Study of Labor, a German research center, by the 1980s almost 0.5% of the East German population was directly employed by the Stasi, the country’s state security service and secret police—1 for every 166 citizens. An additional 1.1% of the population (1 for every 66 citizens) were working as unofficial informers, which represented a massive economic drain. Automated, real-time, algorithm-driven monitoring could potentially drive the cost of controlling the population down substantially in police states—and elsewhere.

We could see a radical new era of censorship that is much more manipulative than anything that has come before. Previously, dissidents were identified when investigators manually combed through photos, read writings, or listened in on phone calls. Real-time algorithmic monitoring means that acts of perceived defiance can be identified and deleted in the moment and their perpetrators marked for swift judgment before they can make an impression on others.

SAP Q417 DigitalDoubles Feature1 Image7 How CRM Software Can Save Time And MoneyBusinesses need to be aware of the wider trend toward real-time, automated censorship and how it might be used in both commercial and governmental settings. These tools can easily be used in countries with unstable political dynamics and could become a real concern for businesses that operate across borders. Businesses must learn to educate and protect employees when technology can censor and punish in real time.

Indeed, the technologies used for this kind of repression could be easily adapted from those that have already been developed for businesses. For instance, both Facebook and Google use near–real-time facial identification algorithms that automatically identify people in images uploaded by users—which helps the companies build out their social graphs and target users with profitable advertisements. Automated algorithms also flag Facebook posts that potentially violate the company’s terms of service.

China is already using these technologies to control its own people in ways that are largely hidden to outsiders.

According to a report by the University of Toronto’s Citizen Lab, the popular Chinese social network WeChat operates under a policy its authors call “One App, Two Systems.” Users with Chinese phone numbers are subjected to dynamic keyword censorship that changes depending on current events and whether a user is in a private chat or in a group. Depending on the political winds, users are blocked from accessing a range of websites that report critically on China through WeChat’s internal browser. Non-Chinese users, however, are not subject to any of these restrictions.

The censorship is also designed to be invisible. Messages are blocked without any user notification, and China has intermittently blocked WhatsApp and other foreign social networks. As a result, Chinese users are steered toward national social networks, which are more compliant with government pressure.

China’s policies play into a larger global trend: the nationalization of the internet. China, Russia, the European Union, and the United States have all adopted different approaches to censorship, user privacy, and surveillance. Although there are social networks such as WeChat or Russia’s VKontakte that are popular in primarily one country, nationalizing the internet challenges users of multinational services such as Facebook and YouTube. These different approaches, which impact everything from data safe harbor laws to legal consequences for posting inflammatory material, have implications for businesses working in multiple countries, as well.

For instance, Twitter is legally obligated to hide Nazi and neo-fascist imagery and some tweets in Germany and France—but not elsewhere. YouTube was officially banned in Turkey for two years because of videos a Turkish court deemed “insulting to the memory of Mustafa Kemal Atatürk,” father of modern Turkey. In Russia, Google must keep Russian users’ personal data on servers located inside Russia to comply with government policy.

While China is a pioneer in the field of instant censorship, tech companies in the United States are matching China’s progress, which could potentially have a chilling effect on democracy. In 2016, Apple applied for a patent on technology that censors audio streams in real time—automating the previously manual process of censoring curse words in streaming audio.

SAP Q417 DigitalDoubles Feature1 Image8 1024x572 How CRM Software Can Save Time And Money

In March, after U.S. President Donald Trump told Fox News, “I think maybe I wouldn’t be [president] if it wasn’t for Twitter,” Twitter founder Evan “Ev” Williams did something highly unusual for the creator of a massive social network.

He apologized.

Speaking with David Streitfeld of The New York Times, Williams said, “It’s a very bad thing, Twitter’s role in that. If it’s true that he wouldn’t be president if it weren’t for Twitter, then yeah, I’m sorry.”

Entrepreneurs tend to be very proud of their innovations. Williams, however, offers a far more ambivalent response to his creation’s success. Much of the 2016 presidential election’s rancor was fueled by Twitter, and the instant gratification of Twitter attracts trolls, bullies, and bigots just as easily as it attracts politicians, celebrities, comedians, and sports fans.

Services such as Twitter, Facebook, YouTube, and Instagram are designed through a mix of look and feel, algorithmic wizardry, and psychological techniques to hang on to users for as long as possible—which helps the services sell more advertisements and make more money. Toxic political discourse and online harassment are unintended side effects of the economic-driven urge to keep users engaged no matter what.

Keeping users’ eyeballs on their screens requires endless hours of multivariate testing, user research, and algorithm refinement. For instance, Casey Newton of tech publication The Verge notes that Google Brain, Google’s AI division, plays a key part in generating YouTube’s video recommendations.

According to Jim McFadden, the technical lead for YouTube recommendations, “Before, if I watch this video from a comedian, our recommendations were pretty good at saying, here’s another one just like it,” he told Newton. “But the Google Brain model figures out other comedians who are similar but not exactly the same—even more adjacent relationships. It’s able to see patterns that are less obvious.”

SAP Q417 DigitalDoubles Feature1 Image9 How CRM Software Can Save Time And MoneyA never-ending flow of content that is interesting without being repetitive is harder to resist. With users glued to online services, addiction and other behavioral problems occur to an unhealthy degree. According to a 2016 poll by nonprofit research company Common Sense Media, 50% of American teenagers believe they are addicted to their smartphones.

This pattern is extending into the workplace. Seventy-five percent of companies told research company Harris Poll in 2016 that two or more hours a day are lost in productivity because employees are distracted. The number one reason? Cellphones and texting, according to 55% of those companies surveyed. Another 41% pointed to the internet.

Tristan Harris, a former design ethicist at Google, argues that many product designers for online services try to exploit psychological vulnerabilities in a bid to keep users engaged for longer periods. Harris refers to an iPhone as “a slot machine in my pocket” and argues that user interface (UI) and user experience (UX) designers need to adopt something akin to a Hippocratic Oath to stop exploiting users’ psychological vulnerabilities.

In fact, there is an entire school of study devoted to “dark UX”—small design tweaks to increase profits. These can be as innocuous as a “Buy Now” button in a visually pleasing color or as controversial as when Facebook tweaked its algorithm in 2012 to show a randomly selected group of almost 700,000 users (who had not given their permission) newsfeeds that skewed more positive to some users and more negative to others to gauge the impact on their respective emotional states, according to an article in Wired.

As computers, smartphones, and televisions come ever closer to convergence, these issues matter increasingly to businesses. Some of the universal side effects of addiction are lost productivity at work and poor health. Businesses should offer training and help for employees who can’t stop checking their smartphones.

Mindfulness-centered mobile apps such as Headspace, Calm, and Forest offer one way to break the habit. Users can also choose to break internet addiction by going for a walk, turning their computers off, or using tools like StayFocusd or Freedom to block addictive websites or apps.

Most importantly, companies in the business of creating tech products need to design software and hardware that discourages addictive behavior. This means avoiding bad designs that emphasize engagement metrics over human health. A world of advertising preroll showing up on smart refrigerator touchscreens at 2 a.m. benefits no one.

According to a 2014 study in Cyberpsychology, Behavior and Social Networking, approximately 6% of the world’s population suffers from internet addiction to one degree or another. As more users in emerging economies gain access to cheap data, smartphones, and laptops, that percentage will only increase. For businesses, getting a head start on stopping internet addiction will make employees happier and more productive. D!


About the Authors

Maurizio Cattaneo is Director, Delivery Execution, Energy, and Natural Resources, at SAP.

David Delaney is Global Vice President and Chief Medical Officer, SAP Health.

Volker Hildebrand is Global Vice President for SAP Hybris solutions.

Neal Ungerleider is a Los Angeles-based technology journalist and consultant.


Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.

Comments

Let’s block ads! (Why?)

Digitalist Magazine

Business Intelligence Emboldens Digital Transformation

In May 2017, a computational social scientist from The Psychometrics Centre at the University of Cambridge stood before an audience at the Linux Foundation’s Apache Big Data conference and revealed how close we’ve come to the ultimate goal of marketing: an easily scalable, highly accurate way to predict customer preferences using minimal data.

When she was still a PhD candidate, Sandra Matz created a Facebook ad campaign targeting people based on nothing more than how extroverted their Facebook Likes indicated they were. People with Likes associated with extroverts saw ads for a party game played in a group. People with more introverted Likes saw ads for a quiet game meant to be played solo.

The campaign required only simple algorithms and no advanced analytics. Yet over seven days of testing, the targeted ads generated up to 15 times higher click-through and conversion rates—and significantly more purchases and revenue for the game company.

SAP Q317 DigitalDoubles Feature3 Image2 Business Intelligence Emboldens Digital Transformation“We developed this approach to show that you can achieve highly accurate behavioral and psychological targeting with a minimal amount of data and relatively simple machine learning tools,” says Matz, who is now an assistant professor of management at Columbia University’s business school.

As effective as this experiment was, Matz suggests that it’s still rudimentary compared to what could be done with more and richer data from more sources. And it’s downright primitive given the possibilities of applying more sophisticated Big Data analytics.

These possibilities have created a watershed moment for marketing and its role in the business.

Spiraling Down the Marketing Funnel

Tension has always simmered over marketing’s contribution to business success. The business knows it can’t sell products or services if it doesn’t make customers aware of them, but the impact of marketing strategy on sales and revenue is hard to quantify and reliably replicate—which, in the age of the data-driven enterprise, often leaves some business leaders not just undervaluing marketing but actively mistrusting it. No wonder human resources consultancy Russell Reynolds reports that the 2016 turnover rate among CMOs was the highest it has seen since it began tracking the statistic in 2012.

Most companies still determine customers’ readiness to buy by using a primitive model known as the marketing funnel, which sorts customers into increasingly smaller groups as they progress from first becoming aware of a company to buying, using, and finally advocating for the company’s products. Different versions have different definitions and numbers of stages, and some approaches see the model as a circle, but they all have one thing in common: their ability to sort customers into various stages is limited by the amount of knowledge the company has about each customer.

As a result, the marketing funnel ends up leaking. Some customers back away because they feel harassed by campaigns that don’t apply to their needs, while some of those who are interested fall through the cracks from a lack of attention. Many data-hungry business leaders think of the marketing funnel as no more than a variation of “throw something against the wall and see if it sticks,” and with the proliferation of digital channels and diffusion of customer attention, they have less patience than ever with that approach.

The silver lining is that a more precise, quantifiable way to build customer relationships is emerging. Done properly, it promises to defuse the tension between marketing and the rest of the business, too.

SAP Q317 DigitalDoubles Feature3 Image3 1024x572 Business Intelligence Emboldens Digital Transformation

The Defining Moment

The Cambridge University experiment is one more step toward the long-held marketing dream of the “segment of one.” This concept of marketing messages that are highly granular, even individually tailored, has been around since the late 1980s. Over the last 15 to 20 years, as customer behavior has become digitalized as never before, marketers have been optimistic that they could capture this data and use it to tailor their messaging with laser-like precision.

Yet what’s achievable in theory has been impossible in practice. We’re still struggling to find the right tools to move beyond the basics of demographic targeting. The rise of the internet, smartphones, and social media has generated more types of information about customer behavior in larger amounts than ever before. But using digitally expressed sentiment about everything from toys to turbines as the basis for accurately disseminating highly individualized marketing messages is still time consuming and cost prohibitive.

However, experiments like Matz’s are bringing us closer to creating highly personalized customer experiences—perhaps not always at the individual level but certainly at a level of granularity that will let us unequivocally determine how to best target and measure marketing programs.

Liking Lady Gaga

Between 2007 and 2012, Psychometrics Centre researchers gathered seven million responses to a simple questionnaire for Facebook users. The carefully designed questions measured respondents’ levels of extroversion, agreeableness, openness, conscientiousness, and neuroticism, a constellation of basic personality traits known as the Big Five.

With the respondents’ permission, the researchers used simple machine learning tools to correlate each person’s responses with the official Facebook Pages that the person had liked, such as Pages for books, movies, bands, hobbies, organizations, and foods. They soon saw that certain personality traits and certain Likes went hand in hand.

For example, most people who liked Lady Gaga’s Page tested as extroverts, which made liking the Lady Gaga Page a relevant data point indicating that someone was probably an extrovert. By 2016, Matz was able to create a lively Facebook ad to be shown only to people who had liked a significant number of official Pages that seemed to be linked to extroversion. A more serene ad was shown only to those whose Likes suggested that they were introverts.

SAP Q317 DigitalDoubles Feature3 Image4 Business Intelligence Emboldens Digital TransformationDespite the large size of the Psychometric Centre’s data set, what’s most remarkable about its work is how few data points within that data set were necessary to build a reliable profile that could model useful predictions. Matz told EnterpriseTech that the algorithm the Centre developed needs, on average, just 65 liked Pages to understand someone’s Big Five personality traits better than their friends do, 120 to understand them better than their family members, and 250 to understand them better than a partner or spouse. This may be the first sign that the era of true behavioral marketing is upon us.

Of course, most marketers want to know far more about customers than how outgoing or reserved they are. Scraping Facebook Likes isn’t enough to give them the holistic customer understanding they crave—not when they have an entire universe of other data to consider. The race is on to identify from the vast spectrum of available customer data not only which specific online behaviors have a predictive element such as extroversion or introversion but also which ones will drive the most potent response to specific product or service messaging.

Complicated? Yes—but we are within reach of the algorithms we need to connect the dots for greater customer insight. By reaching out over new channels with more accurate behavior-based messaging, companies could transform the entire customer journey.

A Customized Journey for Each Customer

Attribution, the ability to know the source of a sales lead, is key to behavioral targeting. The more details a business knows about what its customers have already done, the more accurately it can predict what they will do next.

In the past, developing a customer profile relied on last-touch attribution analysis, that is, evaluating the impact of the last interaction a prospective customer had with a brand before becoming a lead. The problem was that companies could rarely be certain what that last touch was, given how much activity still takes place offline and isn’t captured or quantified.

Companies also couldn’t be certain how, or even if, a last touch—be it downloading a white paper, visiting a store, or getting a word-of-mouth recommendation—accelerated the customer through the marketing funnel. They could only predict revenue by looking at how many people were deemed to be at a specific stage and extrapolating from past data what percentage of them were likely to move ahead.

SAP Q317 DigitalDoubles Feature3 Image5 Business Intelligence Emboldens Digital TransformationToday, we’re capturing so much more information about people’s activities that we have a far more accurate idea of both what the last touch was and how influential it was. Behavioral targeting makes any content a customer interacts with valuable in analyzing the customer’s journey. A company can use hard data about those interactions to see where each individual prospect is in the customer journey and predict how likely each one is to continue moving forward.

The company can then generate a tailored offer or other event to nudge individuals along based on what has been successful with other customers who buy the same things and behave in the same ways. For example, a large grocer may send out two million individualized offers each week based on loyalty card activity. This may not strictly create a segment of one, but it creates many small segments of customers with similar behaviors based on what the grocer knows to be effective.

As Cambridge University’s experiment in creating an algorithm to identify and target introverts and extroverts proves, more precise messaging is more effective. By using more complex machine learning algorithms to further filter and refine successful messages to target smaller groups, companies could boost their conversion rates to as high as 50%—an exponential increase beyond today’s average rates.

By using machine learning to speed up the testing of different campaigns and to continuously compare results, companies could rapidly create a dataset about every potential customer’s responses and then benchmark it against others’ responses. This would let them determine individual prospects’ likely responses based on concrete actions rather than assumptions.

For super-luxury brands with a limited number of customers and the ability to capture a vast amount of information about each one, this could lead to true segment-of-one marketing. For other brands, the challenge is not just to figure out who the customer is and what messages to send but also how to scale that personalization to segments of tens of thousands (or hundreds of thousands) of customers at a time. To do that both effectively and quickly, companies will need to leverage machine learning, the Internet of Things, and other advanced technologies that enable accurate predictive models. Companies can then benchmark their projected hit rates against their actual results and refine their algorithms for even greater agility and responsiveness.

The Next Steps of Predictive Marketing

Effective behavioral targeting requires companies to identify all the relevant data points, including external data points that indicate which information is valuable. This calls for data scientists who can spot and remove the irrelevant data points that are at the far ends of the curve and distill what remains into meaningful algorithms. It also requires machine learning tools capable of high-volume, high-speed listening, assessing, learning, and making recommendations to improve the algorithm over time.

Once you’ve created a baseline of primary criteria, you can determine the important criteria by which to segment your customer base. To use an oversimplified example, imagine that you own a coffee shop and you want to increase sales of high-margin bakery items. You need to look not at the customers who always get a muffin with their coffee or at those who never do but at those who buy a muffin sometimes, so that you can start to identify the triggers that make them choose to indulge.

To scale this process, look at both user-based and item-based affinities. User-based affinities link customers who have similar interests and shopping patterns. Item-based affinities link customers based on what they buy, individually or in groups of items. Using machine learning to pair and cross-reference these two factors will enable you to create messages that are personalized enough to seem individualized, even though they’re actually targeting small, multi-person segments.

SAP Q317 DigitalDoubles Feature3 Image6 Business Intelligence Emboldens Digital TransformationRetailers of all types collect data about individuals, down to location, date, time, and SKU of the sale. They may experiment with behavioral targeting by making in-the-moment offers based on what they already know about their customers. For example, they may use a mobile app with geofencing to be alerted when a customer using the app is in the store. The alert triggers back-end systems to look up the customer’s purchase history, generate a relevant offer, and deliver that offer to the customer’s smartphone while the customer is still in the store.

The Line Between Marketing and Manipulation

Just the idea of receiving marketing messages influenced by their behavior will disturb some customers. When marketing is designed, as behavioral targeting is, to maximize engagement, the value of the content depends less on whether it’s useful to the audience or even true and more on whether it gets the target audience to engage and reveal another piece of the behavioral puzzle. As a result, companies considering behavioral marketing must consider a question as old as marketing itself: where is the line between advertising and propaganda?

Creating personal profiles of customers based on their actions and personalities will become inexpensive and easy, for better or worse. Better will lead to more relevant and compelling offers based on predictive models of what customers would like to buy next. Worse will create (or at least look like) scalable, granular manipulation.

If companies hope to apply this level of targeted marketing without coming across as intrusive or invasive, they will need to be completely transparent about what they’re doing and how—and with whom they’re sharing the information. Most shoppers say they’re willing to give up data about themselves if it leads to a better shopping experience and more relevant recommendations.

Numerous studies show that customers are comfortable sharing their buying patterns and preferences as long as it doesn’t compromise their personally identifiable information. Nonetheless, they may decide otherwise if they believe that by welcoming you into their lives, they’re throwing open the doors to strangers as well.

SAP Q317 DigitalDoubles Feature3 Image7 1024x572 Business Intelligence Emboldens Digital Transformation

As data mining for behavioral targeting becomes more common, companies will have to offer customers the opportunity to opt in and out at varying levels of detail. They will also need to identify and flag the significant minority of customers who prefer not to be profiled in such depth (or at all). Machine learning will be invaluable in responding to complaints on social media, tracking the relevant details of offers that were ignored or got negative reactions, and otherwise ensuring that companies don’t misuse customer data or misunderstand consumer wants and needs.

“The entire paradigm of targeting and campaign implies a vendor doing something to customers,” says Mark Bonchek, founder and “chief epiphany officer” at Shift Thinking, a Boston-based consulting firm that helps companies pursue digital transformation. “It implies getting people to do what you want them to do rather than helping them do what they want to do,” he says. “Be clear on the mental model behind your behavioral targeting. Is it more like a friend figuring out the right gift for a friend or a salesperson trying to close a deal with a prospect? People don’t want to be targets.”

Instead, Bonchek suggests, think of behavioral targeting as a way to build a reciprocal relationship that lets you enhance the customer experience at multiple touch points, not all of them actual transactions. Utility companies send customers information about their own and their neighbors’ energy use so they can benchmark themselves. The utilities often follow up with suggestions about how to save both power and money. Meanwhile, a credit card issuer could help customers understand their purchasing patterns and discover new stores or service providers.

“Loyalty is an emotion first and behavior second,” Bonchek says. “It’s the difference between pushing customers through a funnel and helping them achieve a shared purpose.”

The Art of Scientific Marketing

In mid-20th century New York City, a small local chain of markets developed a national reputation for customer service. It let favored customers call in orders and pay for them at pickup. Managers kept lists—handwritten lists, no less—of their best customers’ preferred products and called those customers with special offers. People were happy to pay slightly higher prices overall in exchange for exclusive bargains and highly customized service.

Although it leverages new technologies like machine learning and Big Data, behavioral targeting will in many ways bring us full circle to that hands-on era in which companies created relevant offers that made customers feel valued and understood. Matz believes it would be a competitive advantage for companies to let customers interact with their profiles and even correct them to ensure that they only receive offers that meet their needs and preferences.

As more situational data pours in from smartphones and wearables to be analyzed by AI, she adds, behavioral targeting could become something more immersive than mere marketing. “If you know from that data that someone is not just an extrovert with specific preferences but that they’re currently in a good mood, you can start fine-tuning messages for that particular point in time,” she says. “We’ll move beyond static profiles to interactions based on characteristics that fluctuate.”

With enough data to work with, she suggests, behavioral targeting could become less about making offers and more about informing customers about their options at any given moment, in real time. D!


About the Authors

Denise Champion is Vice President of Strategy, Research, and Insights for Global Marketing at SAP.

Jeff Harvey is Global COO, SAP Analytics & Insight at SAP.

Lori Mitchell-Keller is Global General Manager, Consumer Industries at SAP.

Jeff Woods is Global COO, SAP Leonardo | Data and Analytics.

Fawn Fitter is a freelance writer specializing in business and technology.


Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.

Comments

Let’s block ads! (Why?)

Digitalist Magazine

Video: What Are the Big Card Fraud Trends in Europe?

European Fraud Map 2016 Video: What Are the Big Card Fraud Trends in Europe?

What card fraud trends are bothering fraud managers in Europe? In this video, Gabriel Hopkins of FICO discusses the rise in card-not-present (CNP) fraud, and what card issuers are doing about it.

As shown in the 2016 European Fraud Map published by FICO and based on Euromonitor International data, CNP fraud accounts for around 70% of card fraud, and the percentage is rising. CNP fraud includes online and phone-based purchases, and as ecommerce grows it becomes a more attractive target for criminals, especially given improved verification in a card-present environment.

The challenge in fighting CNP fraud, Hopkins notes, is that retailers, etailers, issuers and banks want to maintain a smooth online shopping experience for customers. That’s why innovations in CNP fraud detection are so vital. FICO has developed new CNP fraud detection models that are focused in catching CNP fraud at the first occurrence.

Let’s block ads! (Why?)

FICO

Five Ways To Boost CX And Provide Superior Customer Service

In May 2017, a computational social scientist from The Psychometrics Centre at the University of Cambridge stood before an audience at the Linux Foundation’s Apache Big Data conference and revealed how close we’ve come to the ultimate goal of marketing: an easily scalable, highly accurate way to predict customer preferences using minimal data.

When she was still a PhD candidate, Sandra Matz created a Facebook ad campaign targeting people based on nothing more than how extroverted their Facebook Likes indicated they were. People with Likes associated with extroverts saw ads for a party game played in a group. People with more introverted Likes saw ads for a quiet game meant to be played solo.

The campaign required only simple algorithms and no advanced analytics. Yet over seven days of testing, the targeted ads generated up to 15 times higher click-through and conversion rates—and significantly more purchases and revenue for the game company.

SAP Q317 DigitalDoubles Feature3 Image2 Five Ways To Boost CX And Provide Superior Customer Service“We developed this approach to show that you can achieve highly accurate behavioral and psychological targeting with a minimal amount of data and relatively simple machine learning tools,” says Matz, who is now an assistant professor of management at Columbia University’s business school.

As effective as this experiment was, Matz suggests that it’s still rudimentary compared to what could be done with more and richer data from more sources. And it’s downright primitive given the possibilities of applying more sophisticated Big Data analytics.

These possibilities have created a watershed moment for marketing and its role in the business.

Spiraling Down the Marketing Funnel

Tension has always simmered over marketing’s contribution to business success. The business knows it can’t sell products or services if it doesn’t make customers aware of them, but the impact of marketing strategy on sales and revenue is hard to quantify and reliably replicate—which, in the age of the data-driven enterprise, often leaves some business leaders not just undervaluing marketing but actively mistrusting it. No wonder human resources consultancy Russell Reynolds reports that the 2016 turnover rate among CMOs was the highest it has seen since it began tracking the statistic in 2012.

Most companies still determine customers’ readiness to buy by using a primitive model known as the marketing funnel, which sorts customers into increasingly smaller groups as they progress from first becoming aware of a company to buying, using, and finally advocating for the company’s products. Different versions have different definitions and numbers of stages, and some approaches see the model as a circle, but they all have one thing in common: their ability to sort customers into various stages is limited by the amount of knowledge the company has about each customer.

As a result, the marketing funnel ends up leaking. Some customers back away because they feel harassed by campaigns that don’t apply to their needs, while some of those who are interested fall through the cracks from a lack of attention. Many data-hungry business leaders think of the marketing funnel as no more than a variation of “throw something against the wall and see if it sticks,” and with the proliferation of digital channels and diffusion of customer attention, they have less patience than ever with that approach.

The silver lining is that a more precise, quantifiable way to build customer relationships is emerging. Done properly, it promises to defuse the tension between marketing and the rest of the business, too.

SAP Q317 DigitalDoubles Feature3 Image3 1024x572 Five Ways To Boost CX And Provide Superior Customer Service

The Defining Moment

The Cambridge University experiment is one more step toward the long-held marketing dream of the “segment of one.” This concept of marketing messages that are highly granular, even individually tailored, has been around since the late 1980s. Over the last 15 to 20 years, as customer behavior has become digitalized as never before, marketers have been optimistic that they could capture this data and use it to tailor their messaging with laser-like precision.

Yet what’s achievable in theory has been impossible in practice. We’re still struggling to find the right tools to move beyond the basics of demographic targeting. The rise of the internet, smartphones, and social media has generated more types of information about customer behavior in larger amounts than ever before. But using digitally expressed sentiment about everything from toys to turbines as the basis for accurately disseminating highly individualized marketing messages is still time consuming and cost prohibitive.

However, experiments like Matz’s are bringing us closer to creating highly personalized customer experiences—perhaps not always at the individual level but certainly at a level of granularity that will let us unequivocally determine how to best target and measure marketing programs.

Liking Lady Gaga

Between 2007 and 2012, Psychometrics Centre researchers gathered seven million responses to a simple questionnaire for Facebook users. The carefully designed questions measured respondents’ levels of extroversion, agreeableness, openness, conscientiousness, and neuroticism, a constellation of basic personality traits known as the Big Five.

With the respondents’ permission, the researchers used simple machine learning tools to correlate each person’s responses with the official Facebook Pages that the person had liked, such as Pages for books, movies, bands, hobbies, organizations, and foods. They soon saw that certain personality traits and certain Likes went hand in hand.

For example, most people who liked Lady Gaga’s Page tested as extroverts, which made liking the Lady Gaga Page a relevant data point indicating that someone was probably an extrovert. By 2016, Matz was able to create a lively Facebook ad to be shown only to people who had liked a significant number of official Pages that seemed to be linked to extroversion. A more serene ad was shown only to those whose Likes suggested that they were introverts.

SAP Q317 DigitalDoubles Feature3 Image4 Five Ways To Boost CX And Provide Superior Customer ServiceDespite the large size of the Psychometric Centre’s data set, what’s most remarkable about its work is how few data points within that data set were necessary to build a reliable profile that could model useful predictions. Matz told EnterpriseTech that the algorithm the Centre developed needs, on average, just 65 liked Pages to understand someone’s Big Five personality traits better than their friends do, 120 to understand them better than their family members, and 250 to understand them better than a partner or spouse. This may be the first sign that the era of true behavioral marketing is upon us.

Of course, most marketers want to know far more about customers than how outgoing or reserved they are. Scraping Facebook Likes isn’t enough to give them the holistic customer understanding they crave—not when they have an entire universe of other data to consider. The race is on to identify from the vast spectrum of available customer data not only which specific online behaviors have a predictive element such as extroversion or introversion but also which ones will drive the most potent response to specific product or service messaging.

Complicated? Yes—but we are within reach of the algorithms we need to connect the dots for greater customer insight. By reaching out over new channels with more accurate behavior-based messaging, companies could transform the entire customer journey.

A Customized Journey for Each Customer

Attribution, the ability to know the source of a sales lead, is key to behavioral targeting. The more details a business knows about what its customers have already done, the more accurately it can predict what they will do next.

In the past, developing a customer profile relied on last-touch attribution analysis, that is, evaluating the impact of the last interaction a prospective customer had with a brand before becoming a lead. The problem was that companies could rarely be certain what that last touch was, given how much activity still takes place offline and isn’t captured or quantified.

Companies also couldn’t be certain how, or even if, a last touch—be it downloading a white paper, visiting a store, or getting a word-of-mouth recommendation—accelerated the customer through the marketing funnel. They could only predict revenue by looking at how many people were deemed to be at a specific stage and extrapolating from past data what percentage of them were likely to move ahead.

SAP Q317 DigitalDoubles Feature3 Image5 Five Ways To Boost CX And Provide Superior Customer ServiceToday, we’re capturing so much more information about people’s activities that we have a far more accurate idea of both what the last touch was and how influential it was. Behavioral targeting makes any content a customer interacts with valuable in analyzing the customer’s journey. A company can use hard data about those interactions to see where each individual prospect is in the customer journey and predict how likely each one is to continue moving forward.

The company can then generate a tailored offer or other event to nudge individuals along based on what has been successful with other customers who buy the same things and behave in the same ways. For example, a large grocer may send out two million individualized offers each week based on loyalty card activity. This may not strictly create a segment of one, but it creates many small segments of customers with similar behaviors based on what the grocer knows to be effective.

As Cambridge University’s experiment in creating an algorithm to identify and target introverts and extroverts proves, more precise messaging is more effective. By using more complex machine learning algorithms to further filter and refine successful messages to target smaller groups, companies could boost their conversion rates to as high as 50%—an exponential increase beyond today’s average rates.

By using machine learning to speed up the testing of different campaigns and to continuously compare results, companies could rapidly create a dataset about every potential customer’s responses and then benchmark it against others’ responses. This would let them determine individual prospects’ likely responses based on concrete actions rather than assumptions.

For super-luxury brands with a limited number of customers and the ability to capture a vast amount of information about each one, this could lead to true segment-of-one marketing. For other brands, the challenge is not just to figure out who the customer is and what messages to send but also how to scale that personalization to segments of tens of thousands (or hundreds of thousands) of customers at a time. To do that both effectively and quickly, companies will need to leverage machine learning, the Internet of Things, and other advanced technologies that enable accurate predictive models. Companies can then benchmark their projected hit rates against their actual results and refine their algorithms for even greater agility and responsiveness.

The Next Steps of Predictive Marketing

Effective behavioral targeting requires companies to identify all the relevant data points, including external data points that indicate which information is valuable. This calls for data scientists who can spot and remove the irrelevant data points that are at the far ends of the curve and distill what remains into meaningful algorithms. It also requires machine learning tools capable of high-volume, high-speed listening, assessing, learning, and making recommendations to improve the algorithm over time.

Once you’ve created a baseline of primary criteria, you can determine the important criteria by which to segment your customer base. To use an oversimplified example, imagine that you own a coffee shop and you want to increase sales of high-margin bakery items. You need to look not at the customers who always get a muffin with their coffee or at those who never do but at those who buy a muffin sometimes, so that you can start to identify the triggers that make them choose to indulge.

To scale this process, look at both user-based and item-based affinities. User-based affinities link customers who have similar interests and shopping patterns. Item-based affinities link customers based on what they buy, individually or in groups of items. Using machine learning to pair and cross-reference these two factors will enable you to create messages that are personalized enough to seem individualized, even though they’re actually targeting small, multi-person segments.

SAP Q317 DigitalDoubles Feature3 Image6 Five Ways To Boost CX And Provide Superior Customer ServiceRetailers of all types collect data about individuals, down to location, date, time, and SKU of the sale. They may experiment with behavioral targeting by making in-the-moment offers based on what they already know about their customers. For example, they may use a mobile app with geofencing to be alerted when a customer using the app is in the store. The alert triggers back-end systems to look up the customer’s purchase history, generate a relevant offer, and deliver that offer to the customer’s smartphone while the customer is still in the store.

The Line Between Marketing and Manipulation

Just the idea of receiving marketing messages influenced by their behavior will disturb some customers. When marketing is designed, as behavioral targeting is, to maximize engagement, the value of the content depends less on whether it’s useful to the audience or even true and more on whether it gets the target audience to engage and reveal another piece of the behavioral puzzle. As a result, companies considering behavioral marketing must consider a question as old as marketing itself: where is the line between advertising and propaganda?

Creating personal profiles of customers based on their actions and personalities will become inexpensive and easy, for better or worse. Better will lead to more relevant and compelling offers based on predictive models of what customers would like to buy next. Worse will create (or at least look like) scalable, granular manipulation.

If companies hope to apply this level of targeted marketing without coming across as intrusive or invasive, they will need to be completely transparent about what they’re doing and how—and with whom they’re sharing the information. Most shoppers say they’re willing to give up data about themselves if it leads to a better shopping experience and more relevant recommendations.

Numerous studies show that customers are comfortable sharing their buying patterns and preferences as long as it doesn’t compromise their personally identifiable information. Nonetheless, they may decide otherwise if they believe that by welcoming you into their lives, they’re throwing open the doors to strangers as well.

SAP Q317 DigitalDoubles Feature3 Image7 1024x572 Five Ways To Boost CX And Provide Superior Customer Service

As data mining for behavioral targeting becomes more common, companies will have to offer customers the opportunity to opt in and out at varying levels of detail. They will also need to identify and flag the significant minority of customers who prefer not to be profiled in such depth (or at all). Machine learning will be invaluable in responding to complaints on social media, tracking the relevant details of offers that were ignored or got negative reactions, and otherwise ensuring that companies don’t misuse customer data or misunderstand consumer wants and needs.

“The entire paradigm of targeting and campaign implies a vendor doing something to customers,” says Mark Bonchek, founder and “chief epiphany officer” at Shift Thinking, a Boston-based consulting firm that helps companies pursue digital transformation. “It implies getting people to do what you want them to do rather than helping them do what they want to do,” he says. “Be clear on the mental model behind your behavioral targeting. Is it more like a friend figuring out the right gift for a friend or a salesperson trying to close a deal with a prospect? People don’t want to be targets.”

Instead, Bonchek suggests, think of behavioral targeting as a way to build a reciprocal relationship that lets you enhance the customer experience at multiple touch points, not all of them actual transactions. Utility companies send customers information about their own and their neighbors’ energy use so they can benchmark themselves. The utilities often follow up with suggestions about how to save both power and money. Meanwhile, a credit card issuer could help customers understand their purchasing patterns and discover new stores or service providers.

“Loyalty is an emotion first and behavior second,” Bonchek says. “It’s the difference between pushing customers through a funnel and helping them achieve a shared purpose.”

The Art of Scientific Marketing

In mid-20th century New York City, a small local chain of markets developed a national reputation for customer service. It let favored customers call in orders and pay for them at pickup. Managers kept lists—handwritten lists, no less—of their best customers’ preferred products and called those customers with special offers. People were happy to pay slightly higher prices overall in exchange for exclusive bargains and highly customized service.

Although it leverages new technologies like machine learning and Big Data, behavioral targeting will in many ways bring us full circle to that hands-on era in which companies created relevant offers that made customers feel valued and understood. Matz believes it would be a competitive advantage for companies to let customers interact with their profiles and even correct them to ensure that they only receive offers that meet their needs and preferences.

As more situational data pours in from smartphones and wearables to be analyzed by AI, she adds, behavioral targeting could become something more immersive than mere marketing. “If you know from that data that someone is not just an extrovert with specific preferences but that they’re currently in a good mood, you can start fine-tuning messages for that particular point in time,” she says. “We’ll move beyond static profiles to interactions based on characteristics that fluctuate.”

With enough data to work with, she suggests, behavioral targeting could become less about making offers and more about informing customers about their options at any given moment, in real time. D!


About the Authors

Denise Champion is Vice President of Strategy, Research, and Insights for Global Marketing at SAP.

Jeff Harvey is Global COO, SAP Analytics & Insight at SAP.

Lori Mitchell-Keller is Global General Manager, Consumer Industries at SAP.

Jeff Woods is Global COO, SAP Leonardo | Data and Analytics.

Fawn Fitter is a freelance writer specializing in business and technology.


Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.

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future of business – Digitalist Magazine

guidebook1 e1441045006485 300x200 future of business – Digitalist MagazineAnyone who has the (mis?)fortune to work with me knows that I hold myself to a high standard. I absolutely hate disappointing people, and I take criticism quite seriously. My superiors can take this in two ways: They can just hope that I will find the correct resources on my own to fix my own flaws, or what I perceive to be my flaws; or they can use this drive and give me resources/training to perfect my flaws.

You get what you give us

I am not the only Millennial in the workplace. I might be more critical of myself than others, and there are definitely Millennials out there who think they have all of the answers. What we do have in common is:

a) we do not have all of the answers

b) unless someone helps us find the answers, they aren’t going to magically appear in front of us.

We all know that taking a shot in the dark relies on luck to hit the correct target. Expecting me to find the right resources to help improve my work on my own is going to produce the same results as taking a shot in the dark.

Maps and guidebooks

Mentorship is like giving someone a map and a guidebook; you show starting and end points. You point out all of the cute cafes, museums, and the best hotels. You share helpful reviews on places and tips to avoid traffic.

At the same time, you let the person you are sharing these tools with choose the path that works best for them. You might dog-ear a few pages or even pencil in a route, but you allow them to make decisions based on these tools and your advice.

Direction, not dictatorship

You see, mentorship does not equate dictatorship. It is a relationship built on sharing learned lessons, fears and concerns, and a genuine drive to create the best employee.

How can you have a team of fantastic employees unless you give them the tools to be the best possible versions of themselves?

Mapping out Millennial mentorship needs

I have been fortunate enough to work largely in management roles after securing my undergraduate degree. I’ve always been a problem-solver and a people-lover. I can say that I’ve been a coordinator, a manager, and a director. I’ve managed large teams and small teams. If I have my way, mentorship will always be a major component in my future jobs. Having mentored Millennials and knowing what I desperately want in my employment experiences, I’ve made a list (a guide, if you will):

  • No one likes feeling lost. Giving maps and guides to projects includes making it clear that asking for directions is not a sign of weakness, but of strength.
  • If they look lost, they are probably lost. Send them a compass and if they still can’t find north, gently nudge them in the right direction.
  • Show your team your own wrong turns and ask them to help give you directions in your journey.
  • “Fatigue Kills” is a sign found on any highway. It’s true. Take breaks from the task of getting from point A to B by enjoying the scenery. Get to know your team. Take the time to celebrate their differences and learn their tricks.
  • Treat each person like a different trip. You wouldn’t give someone who hates trains a route that only involves trains. You are only truly mentoring when you building up each person individually and uniquely.
  • Make the trip. You can’t complain about not seeing the world if you don’t ever leave your house. You cannot complain about having mediocre employees if you haven’t put the effort to cultivate the best in them.
  • Know when to turn around. It’s always hard to see that someone is getting roadblock after roadblock and the detours are not working.

We’re so used to planning long journeys. When you expected a person to stay in a company for 40 years, mentorship was a long trip. You could cultivate the best in employees because the timeframe and the amount of distance to cover could grow and expand over decades. With contracts lasting from a few months to a couple of years, mentorship means thinking about small day trips and giving guides to help in their overall journey. But, as we all know, even the smallest of trips needs direction to be successful.

For more HR best practices, see 6 Managing Skills That Companies Take for Granted.

This post originated at ARCOMPANY

The post Give Me a Guidebook and a Map: Mentorship and Millennials appeared first on Millennial CEO.

Photo credit: Guide Book via photopin(license).

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http://www.digitalistmag.com/industries/banking/2015/09/01/digitize-or-die-tabb-tells-financial-services-03365983#respond Tue, 01 Sep 2015 15:00:00 +0000http://blogs.forbes.com/tomgroenfeldt/?p=3740276341 l srgb s gl 300x200 future of business – Digitalist MagazineThe financial services industry, especially capital markets, must become digital or it will lose to innovators, says a new report from TABB Group.

“By our estimation (and with very few exceptions), banking and capital markets have remained largely impervious to the digital revolution,” writes Paul Rowady, director of data and analytics research, in a July report “Digitized Markets: Opening the Door on Human Latency.”

“Financial services – and most conspicuously, the institutional capital markets segment – has been notable for its perceived attachment to all things analog…” he added, comparing it — unfavorably — to publishing and music.

He points to three areas where technical disruption is threatening incumbents — payments, lending, and investment advisory. A second driver is regulation, which is pressing financial firms to do more with less.

Rowady sets the bar high, with high-frequency trading as his example.

“By our estimation, high frequency trading (HFT) is the optimal – and perhaps one of the only – example where a market-facing firm achieved an order-of-magnitude, revolutionary outcome relative to historical precedent…On a revenue-per-employee (RPE) basis – and at the height of HFT strategy profitability – this translated into operating leverage of more than $ 3 million in gross revenue per year per employee or more.” By comparison, a sample of ten Tier-1 players showed an RPE of approximately $ 500,000.

Larry Tabb, founder and CEO of the TABB Group, said the industry is challenged as more and more information is collected and analyzed.

“Folks are trying to create data sets out of everything and signals out of everything, including unstructured information,” he said.

“Increasingly, as you wind up with the Internet of Things digitizing more and more stuff, you wind up with more and more data sets and signals to analyze. It’s not just about HFT, it is really about trying to find, clean, aggregate, analyze, and put into context all sort of different streams of information,” he added.

“I think virtually every area will increasingly be as digitized as HFT is. Money managers are becoming very, very quantitative — looking at and trying to understand what are the driving forces that influence a stock price and what effects does the supply chain have.”

The TABB study does not include two areas of digitalization in capital markets  — index funds and ETFs, both of which operate largely free of human intervention.

A new paper, “Command Centres of the Asset Management Industry,” by Duncan MacDonald-Korth and Professor Dariusz Wojcik, of Oxford, notes that while traditional asset management firms are mostly in big cities where they can easily meet with clients, the top ten ETFs are more scattered — in San Francisco, Philadelphia, Washington D.C., and Chicago, as well as New York and Boston.

“They [ETFs] differ from the traditional asset management model in how they are bought, sold, and distributed,” the paper notes. “Unlike the traditional model, ETF products can be bought and sold entirely digitally, with no need for face to face interaction, hence their name of ‘exchange-traded products’… Because the funds are managed digitally and take little work to set up, startup costs and market penetration are significantly easier to achieve for young firms… the success of ETFs, their low-cost operation, and their ability to be managed outside of financial centers has had, and will continue to have, important effects on the geographic concentration of the Asset Management industry.”

(The paper will be available this autumn from the Social Science Research Network and published next year in the Journal of Economic Geography.)

Rowady expects the digitization of finance will require more experts in parallel programming, human-computer interface design, user-experience design, process redesign, collaborative project management, and data science. Since many of these skills will be obtained through contractors, financial services will also need to develop better supply chain management skills.

Analytics will be important, he added, because existing systems often have problems with bottlenecks at their core. The finance industry needs to automate more of the processes, from analytics to execution, to reduce costs.

“TABB Group recently estimated that the human capital component of the total cost of ownership (TCO) for financial market participants is about 76 percent, or over 3x the combined TCO of hardware, software and data.”

Want more on how our increasingly digital economy is changing the way business runs? See Networks that drive net worth.

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http://www.digitalistmag.com/future-of-work/2015/09/01/revamping-hr-status-quo-in-2015-03367946#comments Tue, 01 Sep 2015 14:00:00 +0000http://blogs.forbes.com/meghanbiro/?p=7571bigstock New Trends 74277445 e1440779335718 future of business – Digitalist MagazineWhat are the hot trends in HR technology? It’s a rhetorical question; put another way, what aren’t the trends? HR technology in itself, having profoundly changed the game, is the hot trend: It’s heated up our field in ways that allow us to leverage talent on an entirely different level, regardless of the size or scope of an organization and irrespective of the end goals, from short-term to future-casting.

We’ll see further evidence of just how far we’ve come in October, when the HR Technology Conference and Exposition descends on Las Vegas. Once, we might have all discussed the concept of tech for HR. Now the conference will witness the rollout of out new HR tech products by the 60-fold. There is no more etcetera, just a shared understanding in just how critical tech is in terms of pushing the boundaries. HR’s best practices now include a far larger sense of infinite functions. And a key difference now is that we’re not future converts to this brave new world, we’re the creators and the consumers.   

If I had to pick them, here are the top four hottest trends:

Cloud computing: expanding innovation

Shifting information and HR applications to the cloud has changed our perspective in myriad ways, allowing us increased flexibility, far greater innovation and agility, the opportunity to consolidate and better control costs via a focused management system, and more. It’s a practical paradigm shift with an ambitiously border-free frontier. It prompts a far more inclusive sense of intelligence about the world of work.

 Big Data: enabling objectivity

We’re using Big Data to attain a new objectivity in terms of talent management, redefining the questions we ask ourselves — and the answers we can create. Tapping into a singular aggregate that can be parsed in endless directions and variations enables us to replace that old-fashioned amorphous hunch with a far more objective, full-spectrum view. It’s the sheer scale that pushes us into that objectivity — and pushes innovations to handle it more precisely, more fluidly.

Predictive analytics: pushing the future

Trending: Our graduation from smoke and mirrors to mathematically based future-casting. What keeps the human factor front and center is combining this new objectivity with a very real sense of human behavior and patterns: We can make decisions based on a broad range of predictors, fill gaps before they happen, and maintain fluidity in the workplace and productivity as well.

Best-of-breed and integrated software: customizing tasks without losing options

Whatever the particular merits of best-of-breed versus integrated software in talent management, I think the debate is a bit too candy-shop at this point. Here the trend needs to hew a more intentional course of convergence rather than further separation (software innovators, take note). Whether a best-of-breed spectrum or an integrated application, the key is being able to focus and function. That’s the grand takeaway of this shift: accounting or recruiting, succession planning or training, the tools are about talent and about people, not about numbers.

We can’t assume that just because we’ve now innovated our way into infinity that an agile wisdom is built in. Nor can we assume the bells and whistles are intelligent enough to know our best intentions. And I haven’t even mentioned mobile / social and readiness. Whatever we do, it must live on mobile and social or it’s overlooking a substantial part of the workforce — not to mention how we work these days.

In terms of readiness, when we adopt HR tech may seem to be related to size. It’s easier to consider massive changes on a smaller scale, but that’s a fallacy. Actionable insights, pipeline building, whatever, it all needs to be a shift across the board, and another trend is going to be that we change the very status quo of talent management.

Already, a 2014 survey of some 270 companies by PriceWaterhouseCoopers found that 70% of companies surveyed with HR and payroll in the cloud had fewer than 5,000 employees — small and medium scale is leading the shift. But 57% of larger companies with more than 5,000 employees are already enabling performance management with cloud-based software, and 32% of all companies were planning on shifting recruiting strategies to the cloud by 2016. And companies of extreme size and scale are already leveraging the tech power of serious (and social) recruiting capabilities to source the best and brightest.

We all know the old homily: The future is now. Modern organizations thrive on what HR tech is becoming; HR tech’s trajectory depends on being able to respond and meet the demands we’re just learning to shape. So long as we can keep the human factor involved, as ubiquitous as tech is becoming, we’ll be golden. Shine on.

Want more on how HR is changing the way business works? See The Future of Human Resources.

Photo Credit: Big Stock Images

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http://www.digitalistmag.com/digital-economy/2015/08/31/digitized-core-heart-of-reimagined-business-03361177#respond Mon, 31 Aug 2015 16:00:24 +0000http://news.sap.com/?p=122488The pace of digital innovation and transformation is increasing. Entire markets, including transportation, logistics and e-commerce, are being disrupted and reinvented everywhere, and each new innovation multiplies the potential for more.

Digital disruptors are not only changing targeted Digital Core shutterstock F e1440766185851 300x200 future of business – Digitalist Magazineindustries, they’re also sending ripple effects of innovation throughout the world. “Digital technologies are doing for human brainpower what the steam engine and related technologies did for human muscle power during the Industrial Revolution,” says IT researcher and author Andrew McAfee in a Harvard Business Review interview.

Most companies believe they are already reinventing their businesses through digital transformation. In a survey conducted by Altimeter Group last year, 88% of executives said as much. Yet most companies lack the basics for a successful transformation. Seventy-five percent of those surveyed by Altimeter Group admitted major strategic gaps, such as leaving the role of the customer out of their digital transformation strategy.

The four pressure points

Four inescapable trends are creating the pressures that shape today’s digital transformation:

1. The empowered customer

Whether your customers are Generation Z consumers or multi-national conglomerates, they all share one vitally important characteristic: each demands to be treated as a unique segment of one. You have no choice but to meet that expectation. Digital and mobile technologies mean that no matter where your customers are, your competition is always one tap away. Enterprises must be capable of delivering rich, real-time interactions and intelligently personalized products and services to each distinct customer, and do so efficiently at scale.

2. Competitive and regulatory pressures

Transparency is a necessary part of business today, and that means competitors and regulators alike can dissect any business process. Staying ahead of the former and meeting the standards of the latter requires operational excellence and accountability at every step in the value cycle. To keep pace, enterprises must develop a varied arsenal of low-touch and automated processes that can make intelligent business decisions based on customer demand and real-time market conditions.

3. Globalization

More businesses today must be prepared to go global in order to remain relevant. Expanding into new markets can no longer be done effectively with costly, infrastructure-heavy international buildouts. Enterprises need a pay-as-you-go strategy with scalable capacity, which can be adjusted rapidly to meet market conditions in any region.

4. Technological progress

The tide of innovations and discoveries is unrelenting. Businesses must be agile enough to quickly adopt new strategies, and be steered by insightful, knowledgeable leadership that can sort winning inventions from dead-end novelties.

What a flexible digitized core can do

Withstanding these four inescapable pressures requires a flexible digitized core at the heart of every organization — one that can reinvent business processes not just every generation, but every day, if necessary. A flexible core, ready for the demands of today’s disruption, can be identified by three key characteristics: efficiency, effectiveness, and agility.

A digitized core increases efficiency by automating processes and distributing responsibility for customer insights across an intelligent business network. Consider how 3D printing is completely reinventing the concept of inventory. The digitized core makes it possible to move manufacturing much closer to the time and place of purchase.

The digitized core increases effectiveness by converting signals in business data into tangible action. That can mean anything from real-time demand forecasting that sends new production orders automatically, to intelligent financing that takes full advantage of global capital markets. A digitized core brings Big Data down to the size and scale needed to deliver valuable insights for everyday business practitioners. Since every enterprise must be ready to create value from data, the digital core itself must be rooted in data.

Finally, the digitized core increases enterprise agility by enhancing every stakeholder’s understanding of the entire business, elevating each employee’s view of the organization. This can empower sales to close deals with higher margin, and R&D to focus on projects with the greatest market potential.

Think like a startup

Today’s enterprises must be agile in order to survive. That means taking advantage of digital opportunities at every turn, even if it flies in the face of established convention. For instance, augmented reality can deliver a virtual showroom or guided maintenance instructions directly to a client site. If that sounds like something only a startup would do, that is not far from the truth. Digital transformation means every business must think and act like a venture capitalist, and a digital core is the surest way to adapt to these modern realities.

Even the most storied enterprises must be able to move with alacrity. “In a world of more data and less certainty, companies have to make decisions and respond to disrupters all the earlier and the more decisively,” McKinsey warns. Without a modern digitized core, even competing on the edges of a market will no longer be an option, as disruption squeezes out those with processes to inflexible to adapt. With a digitized core, an enterprise can disrupt an industry from the inside out.

In future posts, we will explain how to design a digital transformation and how to put that plan into action. For a sneak preview, take a look at how the digital business foundation of SAP S/4HANA is already delivering results.

Learn more how SAP can transform your digitized core and follow me via @SDenecken.

This story originally appeared on SAP Business Trends.

Photo: Shutterstock

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http://www.digitalistmag.com/customer-experience/customer-engagement/2015/08/31/digital-transformation-next-generation-customer-service-03154061#comments Mon, 31 Aug 2015 12:00:56 +0000http://blogs.sap.com/innovation/?p=3154061Technology continues to revolutionize the ways we live, conduct business, and function as a road sign keep it simple 300x200 future of business – Digitalist Magazinesociety. One of the most recent, and pervasive, changes is the rise of the Internet of Things – the web of connections linking the myriad physical objects and digital systems that surround us. The result is a massive set of real-time information designed to help people make better, more informed decisions on everything from how to bundle and sell more products to finding the best intercity parking spot to saving energy at home.

However, it’s easy to get bogged down in the size and complexity of this new information set. If businesses are to take advantage of its power and, just as important, serve their customers using it, they must focus on innovation. But that’s easier said than done, especially when they’re dealing with overly complex business systems and processes.

In a Harvard Business Review study, sponsored by SAP, 60% of business managers reported that complexity increased their operational costs by at least 11%. Plus, the average business spends two-thirds of its IT budget only maintaining current systems, which can encompass hundreds or even thousands of applications.

Just maintaining such a complex status quo leaves little time, energy, or resources for innovation. What if those resources could be freed up? Could IT and lines of business come together and dream? What new business models could you adapt to grow the company? Which product lines or services have growth potential? How can you support new channels, service levels, or geographical expansion? What if you could simplify inherent complexities, often rooted in IT, and apply efforts toward re-imagining your business?

Tackling business and IT complexity

To address such challenges and help enterprises simplify in today’s digital economy, businesses are creating value across industries by generating instant insight from real-time connections to Big Data, the Internet of

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Modeling Deposit Price Elasticity: Where’s the Value?

Deposit Price Elasticity Modeling FICO Modeling Deposit Price Elasticity: Where’s the Value?

The ability to model deposit price elasticity is becoming a core component of deposit portfolio management. In my previous posts on this topic, I discussed:

This post focuses on benefits once models have been completed and are in use. What should you expect to gain from deposit price elasticity models and what can you do with them to maximize benefit to the business?

The main function of a deposit pricing team is to forecast the future performance of their portfolio and a substantial amount of time is spent answering questions such as: How much new money will this rate bring in? How will this cannibalize my more profitable back book? What is the impact of the new rate on my portfolio P&L? What if the market changes rates?

Scientifically derived deposit price elasticity models streamline the answering of these and other questions. Moreover, as the core of a deposit forecasting solution, these models improve the accuracy, efficiency and accountability of the entire pricing process.

Accuracy

Price elasticity models are not affected by qualitative bias and provide a level of accuracy not achievable by gut instinct alone. Rather than focusing on individual products, the modeling suite should have the ability to create simulations of the entire portfolio, incorporating balance movements into and out of the bank as well as the effect price changes have on other products and the resulting impact on the bottom line. Simulations of future behavior should have the ability to predict the impact of price changes and allow the user to flex assumptions around competitor pricing, changes to the macroeconomic environment and internal profit assumptions.

Using this approach we achieve a better understanding of customer behaviors and the associated sensitivities of the bank’s liquidity as a whole. The best system of models tracks the impact of price changes so that previous decisions can be reviewed, appraised and the results fed back into the model calibration. This closed-loop process ensures models are continuously learning and adapting to changing market sentiment.

Efficiency

Another significant benefit of pricing analytics is that they accelerate the decision making process. Pricing managers can rapidly generate a number of different scenarios to study alternative pricing strategies, changes in competitor pricing assumptions or wider market factors. Providing the business with appropriate forecasting levers allows them to focus their expertise on pricing. Generation of evidence to justify pricing decisions becomes an automated process and this makes it possible to quickly iterate towards a pricing strategy that achieves the desired portfolio outcome.

Accountability

When recommendations are based on transparent model drivers, conversations with internal stakeholders or senior management become easier as forecasted balances can be directly related back to internal or external modeling factors. The demonstrable action-effect behavior of pricing models also extends beyond the organization as they facilitate conversations with regulators.

In recent times, the regulatory burden on bank executives has grown such that transparency of the underlying pricing models is paramount. Pricing decisions must be explainable and the inputs and assumptions that sit behind them thoroughly documented. An ideal price-elasticity solution provides transparency to the underlying models, automates much of this governance process and provides an auditable structure for the entire pricing process.

Towards Optimization & Beyond

As I have discussed, price elasticity models that predict flows into, out of and between products are key to gaining a full understanding of a deposits portfolio. They serve as part of a broader analytic infrastructure underpinned by a strong data management system and a highly skilled analytic workforce. Ultimately, they empower the pricing team to make better decisions that are more accurate, quickly identified, and easily explained. These improved, data-driven processes have myriad benefits for everyone from the pricing analyst through senior management, partner stakeholders such as treasury, finance, marketing, and even external auditors.

However, the full value of a deposits portfolio can only be completely unlocked through the application of optimization, which is the final frontier in the construction of a comprehensive pricing solution. Full price optimization has the ability to discover revenue where a simple forecasting tool cannot. It optimally trades off balances and revenue across every product in the portfolio and finds the most efficient path to achieving the desired business outcome.

In my next post, I’ll discuss price optimization in more detail and how it can directly generate value to the deposits business.

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How Technology Enhances The Customer Experience

Mention the word fintech to veteran financial services executives and watch the hairs on the backs of their necks stand up.

Fintech is a broad term that applies to new digital financial technologies, from cryptocurrencies to mobile wallets, as well as the startups attempting to use those new technologies to blast centuries-old financial institutions out of the water.

Recognizing the existential threat, leaders of 233-year-old U.S. financial giant Bank of New York Mellon (BNY Mellon) became convinced that continuous IT-enabled innovation was essential. To do that right, the IT team reorganized around specific capabilities—190 so far. Each capability has an owner who serves as a kind of CEO of that service and who is free to make any changes deemed necessary for success.

Like any radical change, BNY Mellon’s effort has seen its share of growing pains. For example, some take to the ownership roles better than others. And employees have required significant coaching throughout.

Several years in, however, a fundamental shift has taken place at the bank established by U.S. founding father Alexander Hamilton. “Change is no longer some big project,” says Jeanne Ross, principal research scientist at MIT’s Center for Information Systems Research, who has studied BNY Mellon’s efforts. “Change is what you do every morning when you get out of bed.”

Just about every industry is facing its own version of fintech these days, forcing organizations to disrupt their established ways of doing business or face disruption by an upstart unburdened by legacy processes and technology. It’s the age of digital transformation, which business consultancy Capgemini calls “the ultimate challenge in change management because it affects not only industry structures and strategic positioning, but also all levels of an organization (every task, activity, process) as well as the extended supply chain.” Dramatic increases in connectivity and improvements in technologies such as artificial intelligence, cloud computing, and advanced analytics let companies optimize their processes continuously, but usually not without making enormous changes first.

SAP Q317 DigitalDoubles Feature2 Image2 How Technology Enhances The Customer ExperienceTo make the most of frequent and successive waves of technology innovation, organizations must build adaptability into their structures, their functions, and their individual employees. That calls for new approaches designed to make transformation real and continuous. “The ability to develop a culture of change where people rely less on habits and more on imagining what’s possible every day is going to be part and parcel of being a great company,” says Ross.

Unfortunately, the traditional command-and-control architecture of most businesses was not built for continuous adaptation. “The speed with which we need to take a good idea and get it in place is so much faster than before, which is why we are having this moment of truth,” Ross says. “Traditional approaches that rely on a lot of hierarchy to make changes are too slow.”

For years, most change efforts have been top-down, episodic, all-encompassing “big bang” attempts to alter systems, processes, and cultures. Executives announced a restructuring or an acquisition or the implementation of new technology and brought in external change management consultants to try to get people to adapt to new ways of working. It rarely succeeded.

Despite significant investment in the change management discipline and a library of books on the subject, just a quarter of change management initiatives succeed long term, according to a 2013 survey by consultancy Willis Towers Watson.

Digital transformation isn’t going much better. Worldwide spending on digital transformation technologies will grow to US$ 1.2 trillion in 2017, up 17.8% over 2016, according to IDC. But fewer than 2 in 10 respondents to a recent survey by the SAP Center for Business Insight and Oxford Economics have seen substantial or transformational value from their technology investments so far. And just 12% say that digitalization has affected their organizational structure in a meaningful way.

Furthermore, even though 84% of the C-level executives surveyed ranked digital transformation as “critically important” to the survival of their businesses, just 3% have completed transformation efforts that span the entire organization.

For digital transformation to deliver value, an entire organization needs to buy into new ways not just of working, but also of thinking. “It’s not about bringing consultants in. It’s about really designing systems that enable an organization to adapt innately,” says Pravir Malik, founder of organizational change development firm Deep Order Technologies and author of Connecting Inner Power with Global Change: The Fractal Ladder and The Fractal Organization: Creating Enterprises of Tomorrow.

Companies are experimenting with new approaches that encourage and support the flexibility required to embrace continuous transformation. Some are rethinking how they operate. Others are investing in helping employees become more adaptable. Still others are clarifying their mission in a way that makes room for individuals to drive change themselves.

Ultimately, gaining the ability to change constantly will help both organizations and employees over the long term. Change becomes less episodic, less massive, and less jarring; there is no end state, no go-live. Instead, the organization is always moving, but at a step-by-step pace that makes it easier for employees to adapt.

However, evolving into this state of constant, fluid change isn’t easy. It only works if you have the right approach and methodologies.

SAP Q317 DigitalDoubles Feature2 Image3 1024x572 How Technology Enhances The Customer Experience

Changing Mindsets

Indeed, as companies tackle digital transformation, traditional highly structured change management programs can actually do more harm than good, says Tom Weeks, senior consultant with The Arbinger Institute, a consultancy that works with organizations to encourage change from within. “The change program becomes the change rather than the results you’re trying to achieve,” he says.

Such change efforts can create a short-term view. As a result, says Weeks, “they drive short-term change, but they don’t change people’s minds. You can force the issues and try to make change happen for change’s sake. But eventually the effort loses energy.”

“Everyone is surprised by that,” adds Weeks. “But it’s just nature at play. We’re hardwired to resist change. If you’re not shifting fundamental mindsets, it doesn’t matter how much money or how many resources you put behind it.”

In her behavioral research, Stanford University psychologist Carol Dweck has focused on two types of mindsets that she sees in most organizations: a fixed mindset and a growth mindset. People with fixed mindsets believe that their basic qualities, like intelligence or talent, are static.

Those with a growth mindset think that talents and capabilities develop over time through effort—a way of thinking that Dweck says creates more individual resilience and adaptability. People in the latter group tend to be better at collaboration, problem solving, and, naturally, continuing change.

The good news, according to Dweck, is that the growth mindset can be a learned behavior. She points to Microsoft as a company attempting to do just that. Microsoft CEO Satya Nadella has publicly stated that the corporate mission “starts with a belief that everyone can grow and develop; that potential is nurtured, not predetermined; and that anyone can change their mindset.”

SAP Q317 DigitalDoubles Feature2 Image4 How Technology Enhances The Customer ExperienceMicrosoft’s leaders are emphasizing learning and creativity with programs like hackathons in which the best projects are funded and their originators rewarded. The company is more explicitly rewarding risk-taking and the pursuit of stretch goals. When Microsoft’s foray into artificial intelligence, the chatbot Tay, was hacked, the CEO sent the team an e-mail of encouragement rather than rebuke.

Rather than limiting leadership development programs to those easily identified as having innate management potential, Microsoft says it is moving a broader swath of employees up and across teams, augmenting their skills, and expanding their work experiences. The most valuable employees are not necessarily the smartest people in the room, as in the past, but those who are the most adaptable—and capable of bringing that out in others.

While Dweck’s mindset work focuses on peoples’ ability to learn and grow, at The Arbinger Institute, consultants focus on an individual’s ability to work productively and with others. Arbinger’s methodology differentiates between an inward mindset, which causes people to be self-centered—seeing other people as objects or tools to either help or hurt them—and an outward mindset, which engenders more connection with and understanding of others as human beings.

Those with an outward mindset can work more collaboratively and productively. That’s incredibly important in an environment of change, such as when Raytheon Missile Systems was trying to integrate a series of mergers that were rife with infighting.

The company overcame the battles by working with all 12,000 employees on shifting their mindsets. Employees worked to uncover their part in company problems and devised ways to work collaboratively with others to solve them and hold themselves accountable for results. When tasked by company leaders to cut $ 100 million in expenses in two months or face layoffs, employees worked together to uncover alternatives.

They began to look beyond their own individual roles and needs, and focused instead on the needs of their colleagues and of the organization as a whole, says Weeks. That resulted in some big, organization-wide changes that went far beyond cost savings and helped increase sales dramatically.

Typically, companies like Raytheon come to Arbinger for help changing mindsets after they’ve struggled with failed change for a while. But that’s beginning to change, says Weeks, and that’s the ideal.

One company is offering employees training on the outward mindset approach before the launch of its six-year transformation effort. “If employees don’t have the right mindset, you can push change as much as you want, but eventually there will be a snap back. What’s required is people who want to hold themselves accountable at a higher level.”

Flexibility by Design

Neuroscientists are not surprised by the shift toward employee-centric rather than top-down change. They have proven that a brain’s “plasticity”—its ability to restructure and learn new things—is enduring. An old dog can learn new tricks. But when change is forced upon people, they quickly become overwhelmed, which activates the fight-or-flight response in the primitive emotional center of the brain, the amygdala.

They bottle up that instinctive response and it reemerges as anxiety, depression, and poor health if not managed. And not only are those potentially toxic emotions harmful to the individual, they are contagious in the organization.

The secret is to create conditions in which people direct more of the change themselves. When individuals solve a problem on their own, for example, their brain releases a rush of neurotransmitters that can create good feelings associated with the change.

One way to create this kind of personal change ownership is by taking a design thinking approach. The iterative, human-centric design concept that was first developed in the early 1970s has become a popular approach to developing products and services for customers. But design thinking principles can also bring new systems and processes to an organization.

SAP Q317 DigitalDoubles Feature2 Image5 How Technology Enhances The Customer ExperienceThat was the case when furniture maker Herman Miller began exploring the potential of an office chair connected to the Internet of Things (IoT) three years ago. Instead of designing a new chair, Herman Miller came away with the foundation for an organizational transformation from hard goods maker to service provider. This is the latest fundamental shift in a company that has evolved from traditional Queen Anne-style furniture maker in the 1930s to office designer in the 1970s to ergonomics innovator in the 1980s and 1990s, says Chris Hoyt, design exploration leader at Herman Miller.

Taking a design thinking approach meant interviewing a wide cross section of stakeholders. The interviews revealed that simply putting a sensor into a desk chair did not make business sense, but putting one into the company’s sit-to-stand desk—and creating a series of IoT-enabled services around it—did. The exercise turned out to be an entry point into an entirely new business model.

“Design thinking wasn’t new to Herman Miller, but there was a lot of skepticism about whether integrating technology into its furniture made business sense,” explains Kurt Dykema, co-founder and director of technology at product innovation and business strategy consultancy Twisthink, which worked with Herman Miller. “This process guided them through a transformation where they have to think about selling a digital experience and monetizing that instead of just selling a capital good and then being done with it.”

For example, none of Herman Miller’s back office operations was built to support the IoT subscription models it planned to offer with the desk. But the design thinking approach created consensus around IoT business value and helped to clarify the organizational changes required to capitalize on the new opportunity.

“It forced them through the process of retooling the business to sell and maintain digital experiences,” Dykema says. Herman Miller launched its Live OS furniture line in June, with the smart desk as the first product, and plans for more to follow.

Getting Agile

Like many companies that incorporate a design thinking approach to organizational change, the performance car division of Daimler AG, Mercedes-AMG, married its process with agile development methods.

Agile turns conventional change management on its head. Rather than making big changes all at once, agile uses an incremental approach to creating software that gives users a chance to use and react to new functionality as it is developed and to validate its value (as opposed to the more traditional waterfall approach where users don’t experience a solution until it is finished).

With agile, there is no predetermined end state. Instead, change is constant, but never so rapid that it becomes overwhelming.

At Mercedes-AMG, clickable prototypes were produced and tested with users weekly and their feedback was funneled back into development streams, continuously improving the resulting system. Based on early success at Mercedes-AMG, Daimler’s enterprise IT organization launched a similar program to develop new digital services for the enterprise.

SAP Q317 DigitalDoubles Feature2 Image6 How Technology Enhances The Customer ExperienceAt BNY Mellon, the adoption of agile development methods has enabled the company to introduce an incredible amount of systems change—but two weeks at a time.

The product of years of mergers and acquisitions, BNY Mellon had operated in product silos, each with their own systems and processes. The company wanted to develop a digital platform from which it could orchestrate a more unified and innovative customer experience. The goal was to put one of America’s oldest financial institutions on equal footing with some of the newest and most nimble newcomers in fintech.

Agile was a new way of working for the IT organization, which was accustomed to introducing releases a couple of times a year rather than a couple of times a month. So IT leaders invested significant time and money helping employees adopt new skills and adapt to the changes.

Eventually, agile enabled the bank to introduce new systems to its 52,000 employees in phases for their ongoing input, fine-tuning the systems over time to best meet employees’ needs and better ensure their adoption. It’s led to the creation—and ongoing enhancement—of an open-source, cloud-based platform that serves as a portal for both internal employees and customers. This app store will provide access to all BNY Mellon’s products and services as well as capabilities from select fintech and established financial services partners.

Increasing Autonomy

Though making change constant relies heavily on individual employees, leaders still have an important role to play. They need to provide the alignment with organizational principles that, when combined with individual autonomy, can create the kind of fluid and adaptive organization required for digital transformation, according to Mark Bonchek, CEO of Shift Thinking, a consultancy that works with leaders and organizations to update their thinking for a digital age.

The U.S. military takes this kind of approach on the battlefield, putting in place a doctrine that authoritatively guides soldiers but gives them autonomy and requires judgment in action to respond to rapidly changing conditions.

In business, organizations are adapting this principle by giving employees guidance on how to take action without requiring them to first seek approval. For example, when Suresh Kumar took over as CIO of BNY Mellon, he reorganized IT around end-to-end IT and business services. IT leaders subdivided each service into smaller components, each with its own leader. These hundreds of services leaders maintain their own service strategy document that covers the current state as well as a one- to three-year improvement plan.

Each service leader is measured on user experience. And because the services are highly interdependent, leaders are also judged on the experience of other service leaders who depend on their service.

As a result, BNY Mellon’s top IT leadership no longer directs team members, but coaches them. Early on, only about a third of the service leaders were successful. The IT group ultimately developed a maturity model for the approach to foster leader development.

Leading a service is as much a mindset as it is a job, says Kumar. The goal of the new approaches—agile software development, physical reorganization, increased autonomy and responsibility—is to create a digital foundation of services linking the bank to its customers and external partners and fostering ongoing digital transformation. The shift began in the IT organization, but the plan is to expand it enterprise-wide and to bring partners and customers into the loop as well.

The Power of Language

In the digital transformation era, companies need a new strategic narrative to help drive a mindset of constant change. A strategic narrative describes the shared purpose that all stakeholders are working toward, says Bonchek. That creates a shared purpose that everyone can wrap their minds—and ultimately their behaviors—around.

SAP Q317 DigitalDoubles Feature2 Image7 1024x572 How Technology Enhances The Customer Experience

For example, BNY Mellon’s working narrative is that “we believe each of us has the power to improve lives through investing.” And that applies not only to the investment of capital, but investing in people, in ideas, and in the future. At a high level, the theme helps reorient employees’ thinking and behaviors as they consider new ways the bank might differentiate itself.

The Importance of Being Resilient

If an organization is going to adapt itself to constant change, employees need tools to manage the psychological stress that comes with it.

Luckily, personal adaptability is something that you can teach. That’s just what Wendy Quan, a former in-house change management professional, does. As the founder of The Calm Monkey, she’s working with organizations from Google to the government of Dubai, helping them implement self-sustaining mindfulness meditation programs.

Quan used mindfulness and meditation practices to increase her own resilience during cancer treatment. “It alters your experience of a change,” she explains, “even when things around you aren’t changing the way you want them to.”

In 2011, she began conducting mindfulness training for a handful of executives working on a seven-year business and technology transformation project at Pacific Blue Cross. The leaders found the training so valuable that they made it available to the entire workforce.

Quan used the sessions to help employees experience the change on their own terms rather than feeling victimized. She focused change-specific meditations on becoming aware of one’s own perceptions about change, recognizing emotions and their impact on behaviors, learning how to mindfully choose reactions, and cultivating calm and clarity.

Quan surveyed employees after the training. The percentage of employees who rated their personal resiliency as low at the beginning decreased from 40% to just 2% while those who characterized themselves as highly resilient increased by a factor of 600% to 72%. And 83% said that meditation has moderately to significantly helped them through a significant transition.

“Change management methodologies favor the corporate perspective,” says Quan. “But it’s really important to focus on helping people be more self-aware of how they’re journeying through the change.”

Deep Order Technologies’ Malik also focuses his approach to resiliency training on self-awareness. He built a mobile app that enables employees to register what they’re feeling throughout the day. Recording emotional states gives employees a better understanding of what drives their own behaviors and how to cope with their feelings.

Leaders can then look at the aggregated, anonymized readings to identify patterns across the organization. Those patterns give leaders a good idea of the overall orientation of employees going through a change at a given point in time and whether they are poised to go along with it or resist.

Change the Ways of Changing

There is no simple solution to making change easier. A combination of new approaches at the organizational and individual level will be required to adapt to the constant change demanded by the digital future.

These approaches are all in the early adoption phases in most companies. Ironically, they are, in and of themselves, significant changes that must be absorbed. But the speed of digital change is relentless. “It’s just getting faster and faster,” says Quan. “And what companies are seeing is that stress and the inability to adapt to change cause reduced performance and increased absenteeism and disability rates. Leaders who see these trends know they need to pay attention,” says Quan.

Those that don’t? “They’ll go away. They’ll be history,” says Ross. “I don’t think this is an issue they can ignore.” D!


About the Authors

Andreas Hauser is Senior Vice President, Strategic Design Services and AppHaus Network, at SAP.

Paul Kurchina is a community builder with the Americas’ SAP Users’ Group (ASUG) who focuses on digital transformation and change.

Stephanie Overby is a Boston-based business and technology journalist.


Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.

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Digitalist Magazine

GDPR-Compliant Data Protection: For Consumers, It’s Personal

Mention the word fintech to veteran financial services executives and watch the hairs on the backs of their necks stand up.

Fintech is a broad term that applies to new digital financial technologies, from cryptocurrencies to mobile wallets, as well as the startups attempting to use those new technologies to blast centuries-old financial institutions out of the water.

Recognizing the existential threat, leaders of 233-year-old U.S. financial giant Bank of New York Mellon (BNY Mellon) became convinced that continuous IT-enabled innovation was essential. To do that right, the IT team reorganized around specific capabilities—190 so far. Each capability has an owner who serves as a kind of CEO of that service and who is free to make any changes deemed necessary for success.

Like any radical change, BNY Mellon’s effort has seen its share of growing pains. For example, some take to the ownership roles better than others. And employees have required significant coaching throughout.

Several years in, however, a fundamental shift has taken place at the bank established by U.S. founding father Alexander Hamilton. “Change is no longer some big project,” says Jeanne Ross, principal research scientist at MIT’s Center for Information Systems Research, who has studied BNY Mellon’s efforts. “Change is what you do every morning when you get out of bed.”

Just about every industry is facing its own version of fintech these days, forcing organizations to disrupt their established ways of doing business or face disruption by an upstart unburdened by legacy processes and technology. It’s the age of digital transformation, which business consultancy Capgemini calls “the ultimate challenge in change management because it affects not only industry structures and strategic positioning, but also all levels of an organization (every task, activity, process) as well as the extended supply chain.” Dramatic increases in connectivity and improvements in technologies such as artificial intelligence, cloud computing, and advanced analytics let companies optimize their processes continuously, but usually not without making enormous changes first.

SAP Q317 DigitalDoubles Feature2 Image2 GDPR Compliant Data Protection: For Consumers, It’s PersonalTo make the most of frequent and successive waves of technology innovation, organizations must build adaptability into their structures, their functions, and their individual employees. That calls for new approaches designed to make transformation real and continuous. “The ability to develop a culture of change where people rely less on habits and more on imagining what’s possible every day is going to be part and parcel of being a great company,” says Ross.

Unfortunately, the traditional command-and-control architecture of most businesses was not built for continuous adaptation. “The speed with which we need to take a good idea and get it in place is so much faster than before, which is why we are having this moment of truth,” Ross says. “Traditional approaches that rely on a lot of hierarchy to make changes are too slow.”

For years, most change efforts have been top-down, episodic, all-encompassing “big bang” attempts to alter systems, processes, and cultures. Executives announced a restructuring or an acquisition or the implementation of new technology and brought in external change management consultants to try to get people to adapt to new ways of working. It rarely succeeded.

Despite significant investment in the change management discipline and a library of books on the subject, just a quarter of change management initiatives succeed long term, according to a 2013 survey by consultancy Willis Towers Watson.

Digital transformation isn’t going much better. Worldwide spending on digital transformation technologies will grow to US$ 1.2 trillion in 2017, up 17.8% over 2016, according to IDC. But fewer than 2 in 10 respondents to a recent survey by the SAP Center for Business Insight and Oxford Economics have seen substantial or transformational value from their technology investments so far. And just 12% say that digitalization has affected their organizational structure in a meaningful way.

Furthermore, even though 84% of the C-level executives surveyed ranked digital transformation as “critically important” to the survival of their businesses, just 3% have completed transformation efforts that span the entire organization.

For digital transformation to deliver value, an entire organization needs to buy into new ways not just of working, but also of thinking. “It’s not about bringing consultants in. It’s about really designing systems that enable an organization to adapt innately,” says Pravir Malik, founder of organizational change development firm Deep Order Technologies and author of Connecting Inner Power with Global Change: The Fractal Ladder and The Fractal Organization: Creating Enterprises of Tomorrow.

Companies are experimenting with new approaches that encourage and support the flexibility required to embrace continuous transformation. Some are rethinking how they operate. Others are investing in helping employees become more adaptable. Still others are clarifying their mission in a way that makes room for individuals to drive change themselves.

Ultimately, gaining the ability to change constantly will help both organizations and employees over the long term. Change becomes less episodic, less massive, and less jarring; there is no end state, no go-live. Instead, the organization is always moving, but at a step-by-step pace that makes it easier for employees to adapt.

However, evolving into this state of constant, fluid change isn’t easy. It only works if you have the right approach and methodologies.

SAP Q317 DigitalDoubles Feature2 Image3 1024x572 GDPR Compliant Data Protection: For Consumers, It’s Personal

Changing Mindsets

Indeed, as companies tackle digital transformation, traditional highly structured change management programs can actually do more harm than good, says Tom Weeks, senior consultant with The Arbinger Institute, a consultancy that works with organizations to encourage change from within. “The change program becomes the change rather than the results you’re trying to achieve,” he says.

Such change efforts can create a short-term view. As a result, says Weeks, “they drive short-term change, but they don’t change people’s minds. You can force the issues and try to make change happen for change’s sake. But eventually the effort loses energy.”

“Everyone is surprised by that,” adds Weeks. “But it’s just nature at play. We’re hardwired to resist change. If you’re not shifting fundamental mindsets, it doesn’t matter how much money or how many resources you put behind it.”

In her behavioral research, Stanford University psychologist Carol Dweck has focused on two types of mindsets that she sees in most organizations: a fixed mindset and a growth mindset. People with fixed mindsets believe that their basic qualities, like intelligence or talent, are static.

Those with a growth mindset think that talents and capabilities develop over time through effort—a way of thinking that Dweck says creates more individual resilience and adaptability. People in the latter group tend to be better at collaboration, problem solving, and, naturally, continuing change.

The good news, according to Dweck, is that the growth mindset can be a learned behavior. She points to Microsoft as a company attempting to do just that. Microsoft CEO Satya Nadella has publicly stated that the corporate mission “starts with a belief that everyone can grow and develop; that potential is nurtured, not predetermined; and that anyone can change their mindset.”

SAP Q317 DigitalDoubles Feature2 Image4 GDPR Compliant Data Protection: For Consumers, It’s PersonalMicrosoft’s leaders are emphasizing learning and creativity with programs like hackathons in which the best projects are funded and their originators rewarded. The company is more explicitly rewarding risk-taking and the pursuit of stretch goals. When Microsoft’s foray into artificial intelligence, the chatbot Tay, was hacked, the CEO sent the team an e-mail of encouragement rather than rebuke.

Rather than limiting leadership development programs to those easily identified as having innate management potential, Microsoft says it is moving a broader swath of employees up and across teams, augmenting their skills, and expanding their work experiences. The most valuable employees are not necessarily the smartest people in the room, as in the past, but those who are the most adaptable—and capable of bringing that out in others.

While Dweck’s mindset work focuses on peoples’ ability to learn and grow, at The Arbinger Institute, consultants focus on an individual’s ability to work productively and with others. Arbinger’s methodology differentiates between an inward mindset, which causes people to be self-centered—seeing other people as objects or tools to either help or hurt them—and an outward mindset, which engenders more connection with and understanding of others as human beings.

Those with an outward mindset can work more collaboratively and productively. That’s incredibly important in an environment of change, such as when Raytheon Missile Systems was trying to integrate a series of mergers that were rife with infighting.

The company overcame the battles by working with all 12,000 employees on shifting their mindsets. Employees worked to uncover their part in company problems and devised ways to work collaboratively with others to solve them and hold themselves accountable for results. When tasked by company leaders to cut $ 100 million in expenses in two months or face layoffs, employees worked together to uncover alternatives.

They began to look beyond their own individual roles and needs, and focused instead on the needs of their colleagues and of the organization as a whole, says Weeks. That resulted in some big, organization-wide changes that went far beyond cost savings and helped increase sales dramatically.

Typically, companies like Raytheon come to Arbinger for help changing mindsets after they’ve struggled with failed change for a while. But that’s beginning to change, says Weeks, and that’s the ideal.

One company is offering employees training on the outward mindset approach before the launch of its six-year transformation effort. “If employees don’t have the right mindset, you can push change as much as you want, but eventually there will be a snap back. What’s required is people who want to hold themselves accountable at a higher level.”

Flexibility by Design

Neuroscientists are not surprised by the shift toward employee-centric rather than top-down change. They have proven that a brain’s “plasticity”—its ability to restructure and learn new things—is enduring. An old dog can learn new tricks. But when change is forced upon people, they quickly become overwhelmed, which activates the fight-or-flight response in the primitive emotional center of the brain, the amygdala.

They bottle up that instinctive response and it reemerges as anxiety, depression, and poor health if not managed. And not only are those potentially toxic emotions harmful to the individual, they are contagious in the organization.

The secret is to create conditions in which people direct more of the change themselves. When individuals solve a problem on their own, for example, their brain releases a rush of neurotransmitters that can create good feelings associated with the change.

One way to create this kind of personal change ownership is by taking a design thinking approach. The iterative, human-centric design concept that was first developed in the early 1970s has become a popular approach to developing products and services for customers. But design thinking principles can also bring new systems and processes to an organization.

SAP Q317 DigitalDoubles Feature2 Image5 GDPR Compliant Data Protection: For Consumers, It’s PersonalThat was the case when furniture maker Herman Miller began exploring the potential of an office chair connected to the Internet of Things (IoT) three years ago. Instead of designing a new chair, Herman Miller came away with the foundation for an organizational transformation from hard goods maker to service provider. This is the latest fundamental shift in a company that has evolved from traditional Queen Anne-style furniture maker in the 1930s to office designer in the 1970s to ergonomics innovator in the 1980s and 1990s, says Chris Hoyt, design exploration leader at Herman Miller.

Taking a design thinking approach meant interviewing a wide cross section of stakeholders. The interviews revealed that simply putting a sensor into a desk chair did not make business sense, but putting one into the company’s sit-to-stand desk—and creating a series of IoT-enabled services around it—did. The exercise turned out to be an entry point into an entirely new business model.

“Design thinking wasn’t new to Herman Miller, but there was a lot of skepticism about whether integrating technology into its furniture made business sense,” explains Kurt Dykema, co-founder and director of technology at product innovation and business strategy consultancy Twisthink, which worked with Herman Miller. “This process guided them through a transformation where they have to think about selling a digital experience and monetizing that instead of just selling a capital good and then being done with it.”

For example, none of Herman Miller’s back office operations was built to support the IoT subscription models it planned to offer with the desk. But the design thinking approach created consensus around IoT business value and helped to clarify the organizational changes required to capitalize on the new opportunity.

“It forced them through the process of retooling the business to sell and maintain digital experiences,” Dykema says. Herman Miller launched its Live OS furniture line in June, with the smart desk as the first product, and plans for more to follow.

Getting Agile

Like many companies that incorporate a design thinking approach to organizational change, the performance car division of Daimler AG, Mercedes-AMG, married its process with agile development methods.

Agile turns conventional change management on its head. Rather than making big changes all at once, agile uses an incremental approach to creating software that gives users a chance to use and react to new functionality as it is developed and to validate its value (as opposed to the more traditional waterfall approach where users don’t experience a solution until it is finished).

With agile, there is no predetermined end state. Instead, change is constant, but never so rapid that it becomes overwhelming.

At Mercedes-AMG, clickable prototypes were produced and tested with users weekly and their feedback was funneled back into development streams, continuously improving the resulting system. Based on early success at Mercedes-AMG, Daimler’s enterprise IT organization launched a similar program to develop new digital services for the enterprise.

SAP Q317 DigitalDoubles Feature2 Image6 GDPR Compliant Data Protection: For Consumers, It’s PersonalAt BNY Mellon, the adoption of agile development methods has enabled the company to introduce an incredible amount of systems change—but two weeks at a time.

The product of years of mergers and acquisitions, BNY Mellon had operated in product silos, each with their own systems and processes. The company wanted to develop a digital platform from which it could orchestrate a more unified and innovative customer experience. The goal was to put one of America’s oldest financial institutions on equal footing with some of the newest and most nimble newcomers in fintech.

Agile was a new way of working for the IT organization, which was accustomed to introducing releases a couple of times a year rather than a couple of times a month. So IT leaders invested significant time and money helping employees adopt new skills and adapt to the changes.

Eventually, agile enabled the bank to introduce new systems to its 52,000 employees in phases for their ongoing input, fine-tuning the systems over time to best meet employees’ needs and better ensure their adoption. It’s led to the creation—and ongoing enhancement—of an open-source, cloud-based platform that serves as a portal for both internal employees and customers. This app store will provide access to all BNY Mellon’s products and services as well as capabilities from select fintech and established financial services partners.

Increasing Autonomy

Though making change constant relies heavily on individual employees, leaders still have an important role to play. They need to provide the alignment with organizational principles that, when combined with individual autonomy, can create the kind of fluid and adaptive organization required for digital transformation, according to Mark Bonchek, CEO of Shift Thinking, a consultancy that works with leaders and organizations to update their thinking for a digital age.

The U.S. military takes this kind of approach on the battlefield, putting in place a doctrine that authoritatively guides soldiers but gives them autonomy and requires judgment in action to respond to rapidly changing conditions.

In business, organizations are adapting this principle by giving employees guidance on how to take action without requiring them to first seek approval. For example, when Suresh Kumar took over as CIO of BNY Mellon, he reorganized IT around end-to-end IT and business services. IT leaders subdivided each service into smaller components, each with its own leader. These hundreds of services leaders maintain their own service strategy document that covers the current state as well as a one- to three-year improvement plan.

Each service leader is measured on user experience. And because the services are highly interdependent, leaders are also judged on the experience of other service leaders who depend on their service.

As a result, BNY Mellon’s top IT leadership no longer directs team members, but coaches them. Early on, only about a third of the service leaders were successful. The IT group ultimately developed a maturity model for the approach to foster leader development.

Leading a service is as much a mindset as it is a job, says Kumar. The goal of the new approaches—agile software development, physical reorganization, increased autonomy and responsibility—is to create a digital foundation of services linking the bank to its customers and external partners and fostering ongoing digital transformation. The shift began in the IT organization, but the plan is to expand it enterprise-wide and to bring partners and customers into the loop as well.

The Power of Language

In the digital transformation era, companies need a new strategic narrative to help drive a mindset of constant change. A strategic narrative describes the shared purpose that all stakeholders are working toward, says Bonchek. That creates a shared purpose that everyone can wrap their minds—and ultimately their behaviors—around.

SAP Q317 DigitalDoubles Feature2 Image7 1024x572 GDPR Compliant Data Protection: For Consumers, It’s Personal

For example, BNY Mellon’s working narrative is that “we believe each of us has the power to improve lives through investing.” And that applies not only to the investment of capital, but investing in people, in ideas, and in the future. At a high level, the theme helps reorient employees’ thinking and behaviors as they consider new ways the bank might differentiate itself.

The Importance of Being Resilient

If an organization is going to adapt itself to constant change, employees need tools to manage the psychological stress that comes with it.

Luckily, personal adaptability is something that you can teach. That’s just what Wendy Quan, a former in-house change management professional, does. As the founder of The Calm Monkey, she’s working with organizations from Google to the government of Dubai, helping them implement self-sustaining mindfulness meditation programs.

Quan used mindfulness and meditation practices to increase her own resilience during cancer treatment. “It alters your experience of a change,” she explains, “even when things around you aren’t changing the way you want them to.”

In 2011, she began conducting mindfulness training for a handful of executives working on a seven-year business and technology transformation project at Pacific Blue Cross. The leaders found the training so valuable that they made it available to the entire workforce.

Quan used the sessions to help employees experience the change on their own terms rather than feeling victimized. She focused change-specific meditations on becoming aware of one’s own perceptions about change, recognizing emotions and their impact on behaviors, learning how to mindfully choose reactions, and cultivating calm and clarity.

Quan surveyed employees after the training. The percentage of employees who rated their personal resiliency as low at the beginning decreased from 40% to just 2% while those who characterized themselves as highly resilient increased by a factor of 600% to 72%. And 83% said that meditation has moderately to significantly helped them through a significant transition.

“Change management methodologies favor the corporate perspective,” says Quan. “But it’s really important to focus on helping people be more self-aware of how they’re journeying through the change.”

Deep Order Technologies’ Malik also focuses his approach to resiliency training on self-awareness. He built a mobile app that enables employees to register what they’re feeling throughout the day. Recording emotional states gives employees a better understanding of what drives their own behaviors and how to cope with their feelings.

Leaders can then look at the aggregated, anonymized readings to identify patterns across the organization. Those patterns give leaders a good idea of the overall orientation of employees going through a change at a given point in time and whether they are poised to go along with it or resist.

Change the Ways of Changing

There is no simple solution to making change easier. A combination of new approaches at the organizational and individual level will be required to adapt to the constant change demanded by the digital future.

These approaches are all in the early adoption phases in most companies. Ironically, they are, in and of themselves, significant changes that must be absorbed. But the speed of digital change is relentless. “It’s just getting faster and faster,” says Quan. “And what companies are seeing is that stress and the inability to adapt to change cause reduced performance and increased absenteeism and disability rates. Leaders who see these trends know they need to pay attention,” says Quan.

Those that don’t? “They’ll go away. They’ll be history,” says Ross. “I don’t think this is an issue they can ignore.” D!


About the Authors

Andreas Hauser is Senior Vice President, Strategic Design Services and AppHaus Network, at SAP.

Paul Kurchina is a community builder with the Americas’ SAP Users’ Group (ASUG) who focuses on digital transformation and change.

Stephanie Overby is a Boston-based business and technology journalist.


Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.

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