Tag Archives: Things

Five Things I Learned From “The Last Jedi” Trailer

277357 l srgb s gl 300x200 Five Things I Learned From “The Last Jedi” Trailer“Innovation distinguishes between a leader and a follower.” – Steve Jobs

As a part of the last wave of Millennials joining the workforce, I have been inspired by Jobs’ definition of innovation. For years, Millennials like me have been told that we need to be faster, better, and smarter than our peers. With this thought in mind and the endless possibilities of the Internet, it’s easy to see that the digital economy is here, and it is defining my generation.

Lately we’ve all read articles proclaiming that “the digital economy and the economy are becoming one in the same. The lines are being blurred.” While this may be true, Millennials do not see this distinction. To us, it’s just the economy. Everything we do happens in the abstract digital economy – we shop digitally, get our news digitally, communicate digitally, and we take pictures digitally. In fact, the things that we don’t do digitally are few and far between.

Millennial disruption: How to get our attention in the digital economy

In this fast-moving, highly technical era, innovation and technology are ubiquitous, forcing companies to deliver immediate value to consumers. This principle is ingrained in us – it’s stark reality. One day, a brand is a world leader, promising incredible change. Then just a few weeks later, it disappears. Millennials view leaders of the emerging (digital) economy as scrappy, agile, and comfortable making decisions that disrupt the norm, and that may or may not pan out.

What does it take to earn the attention of Millennials? Here are three things you should consider:

1. Millennials appreciate innovations that reinvent product delivery and service to make life better and simpler.

Uber, Vimeo, ASOS, and Apple are some of the most successful disruptors in the current digital economy. Why? They took an already mature market and used technology to make valuable connections with their Millennial customers. These companies did not invent a new product – they reinvented the way business is done within the economy. They knew what their consumers wanted before they realized it.

Millennials thrive on these companies. In fact, we seek them out and expect them to create rapid, digital changes to our daily lives. We want to use the products they developed. We adapt quickly to the changes powered by their new ideas or technologies. With that being said, it’s not astonishing that Millennials feel the need to connect regularly and digitally.

2. It’s not technology that captures us – it’s the simplicity that technology enables.

Recently, McKinsey & Company revealed that “CEOs expect 15%–50% of their companies’ future earnings to come from disruptive technology.” Considering this statistic, it may come as a surprise to these executives that buzzwords – including cloud, diversity, innovation, the Internet of Things, and future of work – does not resonate with us. Sure, we were raised on these terms, but it’s such a part of our culture that we do not think about it. We expect companies to deeply embed this technology now.

What we really crave is technology-enabled simplicity in every aspect of our lives. If something is too complicated to navigate, most of us stop using the product. And why not? It does not add value if we cannot use it immediately.

Many experts claim that this is unique to Millennials, but it truly isn’t. It might just be more obvious and prevalent with us. Some might translate our never-ending desire for simplicity into laziness. Yet striving to make daily activities simpler with the use of technology has been seen throughout history. Millennials just happen to be the first generation to be completely reliant on technology, simplicity, and digitally powered “personal” connections.

3. Millennials keep an eye on where and how the next technology revolution will begin.

Within the next few years Millennials will be the largest generation in the workforce. As a result, the onslaught of coverage on the evolution of technology will most likely be phased out. While the history of technology is significant for our predecessors, this not an overly important story for Millennials because we have not seen the technology evolution ourselves. For us, the digital revolution is a fact of life.

Companies like SAP, Amazon, and Apple did not invent the wheel. Rather, they were able to create a new digital future. For a company to be successful, senior leaders must demonstrate a talent for R&D genius as well as fortune-telling. They need to develop easy-to-use, brilliantly designed products, market them effectively to the masses, and maintain their product elite. It’s not easy, but the companies that upend an entire industry are successfully balancing these tasks.

Disruption can happen anywhere and at any time. Get ready!

Across every industry, big players are threatened — not only by well-known competitors, but by small teams sitting in a garage drafting new ideas that could turn the market upside down. In reality, anyone, anywhere, at any time can cause disruption and bring an idea to life.

Take my employer SAP, for example. With the creation of SAP S/4HANA, we are disrupting the tech market as we help our customers engage in digital transformation. By removing data warehousing and enabling real-time operations, companies are reimagining their future. Organizations such as La Trobe University, the NFL, and Adidas have made it easy to understand and conceptualize the effects using data in real time. But only time will tell whether Millennials will ever realize how much disruption was needed to get where we are today.

Find out how SAP Services & Support you can minimize the impact of disruption and maximize the success of your business. Read SAP S/4HANA customer success stories, visit the SAP Services HUB, or visit the customer testimonial page on SAP.com.


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Digitalist Magazine

4 Sexy Things You Need to Know About Machine Learning

I consider myself a sensible person with good time management skills, so it’s always beguiling to get to the bottom of a news story on a respectable site to be presented with an array of clickbait with alluring titles like “20 ways to stop hangovers your doctor won’t tell you” (made up by me) and “Architects took this water park too far” (real and no they didn’t – it’s just a big slide).

It got me wondering what a clickbait article for machine learning might look like – and then I realised that many machine learning terms are pure clickbait without any augmentation. Test yourself. Would you click on these headlines?

Sensational Details of Model Over-Training in Action

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No, not that sort of model!

Legendary running coach Arthur Lydiard famously taught “Train don’t strain” and it is important to avoid the temptation to over-train or “overfit” a machine learning model.

It can be intuitive to train a model to the point where it exactly fits with the training data, but that will actually make it less predictive. In real terms, if you are building a supervised machine learning model to try to detect fraud, it is important to avoid the temptation to tweak it until it finds every tagged fraud in your data set.

You want your model to be effective on transactions it hasn’t seen. Overtraining gives too much focus on the noise in a particular sampled data set and not enough on the pattern you are trying to detect.

Knowing how far to train a model requires skill and experience. Done right it produces fantastic models which deliver great results both on paper and in production when they’re truly tested. Be wary of systems that advise retraining models all the time. They’re almost guaranteed to learn unimportant and irrelevant patterns and block swathes of good activity.

You Won’t Believe Details of these Unsupervised Relationships

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No, not that sort of relationship!

One area of machine learning we at FICO find particularly exciting and have been researching for more than a decade is unsupervised techniques. They help solve a range or problems where prior “tagged” data is not available, or where algorithms need to find new patterns, sometimes on data that an algorithm or model might never had seen before.

Unsupervised techniques such as multi-layer self-calibrating outlier models — used in FICO’s Cyber Security solution — give enterprises the tools to monitor their networks and detect hacking attempts before data or IP are compromised. And if a compromise has happened, unsupervised techniques help protect the organisations that are then assaulted with the stolen identities.

PSD2 — arriving over the next two years — is another great opportunity, particularly while there is a lack of training data.

The Secret Reality of Living in a Random Forest

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No, not that sort of forest!

There’s a lot of hype specifically about the efficacy of random forest models. These models work by growing a forest of slightly different decision trees and then seeing which responds better to your training data.

Whole academic theses (printed on actual forests) have and will be written about how good or bad the random forest approach is compared to other machine learning models. It has emerged as a popular technique — maybe partly because it is easier to comprehend than some of the other methods.

One problem is that random forests are inherently not explainable and that presents a real challenge with the introduction of GDPR next year. The legislation dictates, among other things, that enterprises must be able to explain their decisions. FICO have a number of approaches to add explainability to random forest and other machine learning models.

There is no single machine learning approach that will solve all problems, so it’s important to choose the right technique or set of techniques. Explainability is and will be important in many applications. Other considerations are speed, computational processing needs, storage needs and maintainability. FICO has experience of deploying machine learning at scale with applications such as FICO Falcon Fraud Manager capable of processing thousands of transactions a second with 10-20 ms latency.

The Sexy Features They Didn’t Want You to See

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No, not those sorts of features!

Features are the lifeblood of predictive machine learning. They’re the parameters in the algorithm. When designing a model, the data scientist identifies features which might help solve the problem at hand and then they test their theory — again and again. Features can be simple, like the amount of a transaction, or they can be complex, such as composite calculations of network and device information.

Andrew Ng put it powerfully: “Coming up with features is difficult, time-consuming, requires expert knowledge. ‘Applied machine learning’ is basically feature engineering.”

So maybe features aren’t sexy, but machine learning and data science definitely are. The Harvard Business Review thinks it’s the sexiest job of the 21st century. The reason for that is that machine learning is transformational and the data scientists who are part of the transformation will be those that combine domain expertise, precision and determination with creativity and originality.

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3 Things a Well-Architected Salesforce Integration Can Do for Your Organization

rsz bigstock missing jigsaw puzzle piece wi 88882046 3 Things a Well Architected Salesforce Integration Can Do for Your Organization

Did you know that with a great Salesforce integration you can automate business processes, cut down on human error, and get a full view of your customers? All you need is a well integrated Salesforce system that enables data sharing across your entire network.  

The reality is, not all integration approaches are created equal. Some only enable data to be moved in one direction between systems. Some appear easy at the onset, but require considerable coding. Others only offer point-to-point integration and can only tie one system to another. And still others don’t act as a platform where data can be easily shared across the organization.

Businesses that choose the right integration solution will be rewarded with speedier, cost-effective integrations that can create unique competitive advantages to put them ahead of the curve. In our new whitepaper “Nine Strategies for a Successful Salesforce Integration”, we’ll walk you through how to pick the best integration solution for your company.

The great thing is, you don’t have to be a technical expert to understand and execute Salesforce integration. As long as you can grasp the concept of information moving from one system to another, you’ve got it.

In today’s digital world, customers, employees, and partners expect anytime, anywhere communications, instant responses, and up-to-date/real-time information. The battle for business will be won by being able to support business requests faster and better. To surpass the competition, enable your systems to work together at top speed. Read our paper to get started today.

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The TIBCO Blog

The U.S. government should be making better use of the internet of things

 The U.S. government should be making better use of the internet of things

The U.S. government acknowledges that the Internet of things (IoT) has advanced past the research and development stage. However, even with this acknowledgement, there is still a gap in how the public sector is connecting to the IoT and how it should be connecting.

Imagine if agencies could arm flood zones with sensors able to detect rising water levels and provide early warning to citizens. Or deploy sensors on highways that alert the necessary authorities when they are iced over. Or connect irrigation systems in national parks to smartphone applications that stream scheduling and maintenance data.

Policy makers, for their part, should be taking steps to update security, data governance, and regulations that enable the swift, full adoption of IoT and associated technology.

However, while some agencies, like NIST, already have established programs in place, IoT adoption among public sector agencies is in the very early stages due to extreme shortages in security, talent and logistics. Here are a few ways the public sector can swiftly overcome these hurdles:

Build secure endpoints and consider blockchain integration

A new bill introduced in early August by Senator Mark Warner (D-Virginia) and Senator Cory Gardner (R-Colorado) attempts to set standards for both public and private sector IoT-linked devices. Meanwhile, government technologists are taking extra time to explore secure hardware options and survey connectable platforms, from the Pentagon to the famously breached Office of Personnel Management. For good reason, the public sector must take a security-first mindset when designing solutions that employ the IoT. We all remember the botnet that broke the Internet, so we must design systems and solutions with military-grade cyber-defense capabilities.

To fully address IoT security concerns, as well as ensure completeness and integrity amongst entities, agencies need to be thinking about blockchain integration. Just as blockchain can be used in the healthcare sector to track medical records securely and in real-time so that everyone involved in a patient’s care has the same accurate view of the patient’s situation, government agencies that famously manage data records, like OPM, could use blockchain to avoid future breaches.

Recruit talent that is familiar with the technology

The public sector needs to complement IoT research and development with targeted talent recruitment. Specifically, agencies need to develop IoT-centered programs and incentives that attract data scientists to analyze trends and guide implementation, UX teams that can match a complex network with a seamless experience for people working with data, and field service professionals who can keep tabs on the performance of sensors attached to millions of points in the public sector’s network.

In practice, a typical IoT deployment requires engineers who install and activate sensors in warehouses, farms, and vehicles. McKinsey points out that there are also many sensors in place that could be connected to the IoT but are inactive. As a first step, the public sector will need to onboard trained engineers who can take stock of the government’s connectivity potential. Once the sensors are deployed, data from the sensors needs to be collected and analyzed in a central data repository by software developers and data scientists. Finally, the results of the data analysis need to be presented to government stakeholders so they can take action on whatever the IoT devices are telling them. Hence, the need for user experience experts.

Lay the foundation for a connected public sector

Large-scale government IoT projects will require significant Other Direct Costs (ODC) to procure sensors and activate (or reactivate) infrastructure. And current regulations pertaining to data capture and information assurance need to be refocused to provide firm controls and clear guidelines for IoT deployment. Agencies also need to recruit – and retain – the types of staff I’ve mentioned above.

Despite these roadblocks, the benefits of public sector IoT are substantial, particularly as it relates to emergency management and planning.

IoT, when implemented correctly, has the power to solve problems before they even happen at all levels of government.

Kris Tremaine is a Senior Vice President at ICF, leading the firm’s federal digital practice. She has 25 years of experience providing strategic and digital counsel to government, nonprofit, and private sector organizations.

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Big Data – VentureBeat

Robots And AI In Retail: 8 Things You Must Know

Last August, a woman arrived at a Reno, Nevada, hospital and told the attending doctors that she had recently returned from an extended trip to India, where she had broken her right thighbone two years ago. The woman, who was in her 70s, had subsequently developed an infection in her thigh and hip for which she was hospitalized in India several times. The Reno doctors recognized that the infection was serious—and the visit to India, where antibiotic-resistant bacteria runs rampant, raised red flags.

When none of the 14 antibiotics the physicians used to treat the woman worked, they sent a sample of the bacterium to the U.S. Centers for Disease Control (CDC) for testing. The CDC confirmed the doctors’ worst fears: the woman had a class of microbe called carbapenem-resistant Enterobacteriaceae (CRE). Carbapenems are a powerful class of antibiotics used as last-resort treatment for multidrug-resistant infections. The CDC further found that, in this patient’s case, the pathogen was impervious to all 26 antibiotics approved by the U.S. Food and Drug Administration (FDA).

In other words, there was no cure.

This is just the latest alarming development signaling the end of the road for antibiotics as we know them. In September, the woman died from septic shock, in which an infection takes over and shuts down the body’s systems, according to the CDC’s Morbidity and Mortality Weekly Report.

Other antibiotic options, had they been available, might have saved the Nevada woman. But the solution to the larger problem won’t be a new drug. It will have to be an entirely new approach to the diagnosis of infectious disease, to the use of antibiotics, and to the monitoring of antimicrobial resistance (AMR)—all enabled by new technology.

sap Q217 digital double feature2 images2 Robots And AI In Retail: 8 Things You Must KnowBut that new technology is not being implemented fast enough to prevent what former CDC director Tom Frieden has nicknamed nightmare bacteria. And the nightmare is becoming scarier by the year. A 2014 British study calculated that 700,000 people die globally each year because of AMR. By 2050, the global cost of antibiotic resistance could grow to 10 million deaths and US$ 100 trillion a year, according to a 2014 estimate. And the rate of AMR is growing exponentially, thanks to the speed with which humans serving as hosts for these nasty bugs can move among healthcare facilities—or countries. In the United States, for example, CRE had been seen only in North Carolina in 2000; today it’s nationwide.

Abuse and overuse of antibiotics in healthcare and livestock production have enabled bacteria to both mutate and acquire resistant genes from other organisms, resulting in truly pan-drug resistant organisms. As ever-more powerful superbugs continue to proliferate, we are potentially facing the deadliest and most costly human-made catastrophe in modern times.

“Without urgent, coordinated action by many stakeholders, the world is headed for a post-antibiotic era, in which common infections and minor injuries which have been treatable for decades can once again kill,” said Dr. Keiji Fukuda, assistant director-general for health security for the World Health Organization (WHO).

Even if new antibiotics could solve the problem, there are obstacles to their development. For one thing, antibiotics have complex molecular structures, which slows the discovery process. Further, they aren’t terribly lucrative for pharmaceutical manufacturers: public health concerns call for new antimicrobials to be financially accessible to patients and used conservatively precisely because of the AMR issue, which reduces the financial incentives to create new compounds. The last entirely new class of antibiotic was introduced 30 year ago. Finally, bacteria will develop resistance to new antibiotics as well if we don’t adopt new approaches to using them.

Technology can play the lead role in heading off this disaster. Vast amounts of data from multiple sources are required for better decision making at all points in the process, from tracking or predicting antibiotic-resistant disease outbreaks to speeding the potential discovery of new antibiotic compounds. However, microbes will quickly adapt and resist new medications, too, if we don’t also employ systems that help doctors diagnose and treat infection in a more targeted and judicious way.

Indeed, digital tools can help in all four actions that the CDC recommends for combating AMR: preventing infections and their spread, tracking resistance patterns, improving antibiotic use, and developing new diagnostics and treatment.

Meanwhile, individuals who understand both the complexities of AMR and the value of technologies like machine learning, human-computer interaction (HCI), and mobile applications are working to develop and advocate for solutions that could save millions of lives.

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Keeping an Eye Out for Outbreaks

Like others who are leading the fight against AMR, Dr. Steven Solomon has no illusions about the difficulty of the challenge. “It is the single most complex problem in all of medicine and public health—far outpacing the complexity and the difficulty of any other problem that we face,” says Solomon, who is a global health consultant and former director of the CDC’s Office of Antimicrobial Resistance.

Solomon wants to take the battle against AMR beyond the laboratory. In his view, surveillance—tracking and analyzing various data on AMR—is critical, particularly given how quickly and widely it spreads. But surveillance efforts are currently fraught with shortcomings. The available data is fragmented and often not comparable. Hospitals fail to collect the representative samples necessary for surveillance analytics, collecting data only on those patients who experience resistance and not on those who get better. Laboratories use a wide variety of testing methods, and reporting is not always consistent or complete.

Surveillance can serve as an early warning system. But weaknesses in these systems have caused public health officials to consistently underestimate the impact of AMR in loss of lives and financial costs. That’s why improving surveillance must be a top priority, says Solomon, who previously served as chair of the U.S. Federal Interagency Task Force on AMR and has been tracking the advance of AMR since he joined the U.S. Public Health Service in 1981.

A Collaborative Diagnosis

Ineffective surveillance has also contributed to huge growth in the use of antibiotics when they aren’t warranted. Strong patient demand and financial incentives for prescribing physicians are blamed for antibiotics abuse in China. India has become the largest consumer of antibiotics on the planet, in part because they are prescribed or sold for diarrheal diseases and upper respiratory infections for which they have limited value. And many countries allow individuals to purchase antibiotics over the counter, exacerbating misuse and overuse.

In the United States, antibiotics are improperly prescribed 50% of the time, according to CDC estimates. One study of adult patients visiting U.S. doctors to treat respiratory problems found that more than two-thirds of antibiotics were prescribed for conditions that were not infections at all or for infections caused by viruses—for which an antibiotic would do nothing. That’s 27 million courses of antibiotics wasted a year—just for respiratory problems—in the United States alone.

And even in countries where there are national guidelines for prescribing antibiotics, those guidelines aren’t always followed. A study published in medical journal Family Practice showed that Swedish doctors, both those trained in Sweden and those trained abroad, inconsistently followed rules for prescribing antibiotics.

Solomon strongly believes that, worldwide, doctors need to expand their use of technology in their offices or at the bedside to guide them through a more rational approach to antibiotic use. Doctors have traditionally been reluctant to adopt digital technologies, but Solomon thinks that the AMR crisis could change that. New digital tools could help doctors and hospitals integrate guidelines for optimal antibiotic prescribing into their everyday treatment routines.

“Human-computer interactions are critical, as the amount of information available on antibiotic resistance far exceeds the ability of humans to process it,” says Solomon. “It offers the possibility of greatly enhancing the utility of computer-assisted physician order entry (CPOE), combined with clinical decision support.” Healthcare facilities could embed relevant information and protocols at the point of care, guiding the physician through diagnosis and prescription and, as a byproduct, facilitating the collection and reporting of antibiotic use.

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Cincinnati Children’s Hospital’s antibiotic stewardship division has deployed a software program that gathers information from electronic medical records, order entries, computerized laboratory and pathology reports, and more. The system measures baseline antimicrobial use, dosing, duration, costs, and use patterns. It also analyzes bacteria and trends in their susceptibilities and helps with clinical decision making and prescription choices. The goal, says Dr. David Haslam, who heads the program, is to decrease the use of “big gun” super antibiotics in favor of more targeted treatment.

While this approach is not yet widespread, there is consensus that incorporating such clinical-decision support into electronic health records will help improve quality of care, contain costs, and reduce overtreatment in healthcare overall—not just in AMR. A 2013 randomized clinical trial finds that doctors who used decision-support tools were significantly less likely to order antibiotics than those in the control group and prescribed 50% fewer broad-spectrum antibiotics.

Putting mobile devices into doctors’ hands could also help them accept decision support, believes Solomon. Last summer, Scotland’s National Health Service developed an antimicrobial companion app to give practitioners nationwide mobile access to clinical guidance, as well as an audit tool to support boards in gathering data for local and national use.

“The immediacy and the consistency of the input to physicians at the time of ordering antibiotics may significantly help address the problem of overprescribing in ways that less-immediate interventions have failed to do,” Solomon says. In addition, handheld devices with so-called lab-on-a-chip  technology could be used to test clinical specimens at the bedside and transmit the data across cellular or satellite networks in areas where infrastructure is more limited.

Artificial intelligence (AI) and machine learning can also become invaluable technology collaborators to help doctors more precisely diagnose and treat infection. In such a system, “the physician and the AI program are really ‘co-prescribing,’” says Solomon. “The AI can handle so much more information than the physician and make recommendations that can incorporate more input on the type of infection, the patient’s physiologic status and history, and resistance patterns of recent isolates in that ward, in that hospital, and in the community.”

Speed Is Everything

Growing bacteria in a dish has never appealed to Dr. James Davis, a computational biologist with joint appointments at Argonne National Laboratory and the University of Chicago Computation Institute. The first of a growing breed of computational biologists, Davis chose a PhD advisor in 2004 who was steeped in bioinformatics technology “because you could see that things were starting to change,” he says. He was one of the first in his microbiology department to submit a completely “dry” dissertation—that is, one that was all digital with nothing grown in a lab.

Upon graduation, Davis wanted to see if it was possible to predict whether an organism would be susceptible or resistant to a given antibiotic, leading him to explore the potential of machine learning to predict AMR.

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As the availability of cheap computing power has gone up and the cost of genome sequencing has gone down, it has become possible to sequence a pathogen sample in order to detect its AMR resistance mechanisms. This could allow doctors to identify the nature of an infection in minutes instead of hours or days, says Davis.

Davis is part of a team creating a giant database of bacterial genomes with AMR metadata for the Pathosystems Resource Integration Center (PATRIC), funded by the U.S. National Institute of Allergy and Infectious Diseases to collect data on priority pathogens, such as tuberculosis and gonorrhea.

Because the current inability to identify microbes quickly is one of the biggest roadblocks to making an accurate diagnosis, the team’s work is critically important. The standard method for identifying drug resistance is to take a sample from a wound, blood, or urine and expose the resident bacteria to various antibiotics. If the bacterial colony continues to divide and thrive despite the presence of a normally effective drug, it indicates resistance. The process typically takes between 16 and 20 hours, itself an inordinate amount of time in matters of life and death. For certain strains of antibiotic-resistant tuberculosis, though, such testing can take a week. While physicians are waiting for test results, they often prescribe broad-spectrum antibiotics or make a best guess about what drug will work based on their knowledge of what’s happening in their hospital, “and in the meantime, you either get better,” says Davis, “or you don’t.”

At PATRIC, researchers are using machine-learning classifiers to identify regions of the genome involved in antibiotic resistance that could form the foundation for a “laboratory free” process for predicting resistance. Being able to identify the genetic mechanisms of AMR and predict the behavior of bacterial pathogens without petri dishes could inform clinical decision making and improve reaction time. Thus far, the researchers have developed machine-learning classifiers for identifying antibiotic resistance in Acinetobacter baumannii (a big player in hospital-acquired infection), methicillin-resistant Staphylococcus aureus (a.k.a. MRSA, a worldwide problem), and Streptococcus pneumoniae (a leading cause of bacterial meningitis), with accuracies ranging from 88% to 99%.

Houston Methodist Hospital, which uses the PATRIC database, is researching multidrug-resistant bacteria, specifically MRSA. Not only does resistance increase the cost of care, but people with MRSA are 64% more likely to die than people with a nonresistant form of the infection, according to WHO. Houston Methodist is investigating the molecular genetic causes of drug resistance in MRSA in order to identify new treatment approaches and help develop novel antimicrobial agents.

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The Hunt for a New Class of Antibiotics

There are antibiotic-resistant bacteria, and then there’s Clostridium difficile—a.k.a. C. difficile—a bacterium that attacks the intestines even in young and healthy patients in hospitals after the use of antibiotics.

It is because of C. difficile that Dr. L. Clifford McDonald jumped into the AMR fight. The epidemiologist was finishing his work analyzing the spread of SARS in Toronto hospitals in 2004 when he turned his attention to C. difficile, convinced that the bacteria would become more common and more deadly. He was right, and today he’s at the forefront of treating the infection and preventing the spread of AMR as senior advisor for science and integrity in the CDC’s Division of Healthcare Quality Promotion. “[AMR] is an area that we’re funding heavily…insofar as the CDC budget can fund anything heavily,” says McDonald, whose group has awarded $ 14 million in contracts for innovative anti-AMR approaches.

Developing new antibiotics is a major part of the AMR battle. The majority of new antibiotics developed in recent years have been variations of existing drug classes. It’s been three decades since the last new class of antibiotics was introduced. Less than 5% of venture capital in pharmaceutical R&D is focused on antimicrobial development. A 2008 study found that less than 10% of the 167 antibiotics in development at the time had a new “mechanism of action” to deal with multidrug resistance. “The low-hanging fruit [of antibiotic development] has been picked,” noted a WHO report.

Researchers will have to dig much deeper to develop novel medicines. Machine learning could help drug developers sort through much larger data sets and go about the capital-intensive drug development process in a more prescriptive fashion, synthesizing those molecules most likely to have an impact.

McDonald believes that it will become easier to find new antibiotics if we gain a better understanding of the communities of bacteria living in each of us—as many as 1,000 different types of microbes live in our intestines, for example. Disruption to those microbial communities—our “microbiome”—can herald AMR. McDonald says that Big Data and machine learning will be needed to unlock our microbiomes, and that’s where much of the medical community’s investment is going.

He predicts that within five years, hospitals will take fecal samples or skin swabs and sequence the microorganisms in them as a kind of pulse check on antibiotic resistance. “Just doing the bioinformatics to sort out what’s there and the types of antibiotic resistance that might be in that microbiome is a Big Data challenge,” McDonald says. “The only way to make sense of it, going forward, will be advanced analytic techniques, which will no doubt include machine learning.”

Reducing Resistance on the Farm

Bringing information closer to where it’s needed could also help reduce agriculture’s contribution to the antibiotic resistance problem. Antibiotics are widely given to livestock to promote growth or prevent disease. In the United States, more kilograms of antibiotics are administered to animals than to people, according to data from the FDA.

One company has developed a rapid, on-farm diagnostics tool to provide livestock producers with more accurate disease detection to make more informed management and treatment decisions, which it says has demonstrated a 47% to 59% reduction in antibiotic usage. Such systems, combined with pressure or regulations to reduce antibiotic use in meat production, could also help turn the AMR tide.

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Breaking Down Data Silos Is the First Step

Adding to the complexity of the fight against AMR is the structure and culture of the global healthcare system itself. Historically, healthcare has been a siloed industry, notorious for its scattered approach focused on transactions rather than healthy outcomes or the true value of treatment. There’s no definitive data on the impact of AMR worldwide; the best we can do is infer estimates from the information that does exist.

The biggest issue is the availability of good data to share through mobile solutions, to drive HCI clinical-decision support tools, and to feed supercomputers and machine-learning platforms. “We have a fragmented healthcare delivery system and therefore we have fragmented information. Getting these sources of data all into one place and then enabling them all to talk to each other has been problematic,” McDonald says.

Collecting, integrating, and sharing AMR-related data on a national and ultimately global scale will be necessary to better understand the issue. HCI and mobile tools can help doctors, hospitals, and public health authorities collect more information while advanced analytics, machine learning, and in-memory computing can enable them to analyze that data in close to real time. As a result, we’ll better understand patterns of resistance from the bedside to the community and up to national and international levels, says Solomon. The good news is that new technology capabilities like AI and new potential streams of data are coming online as an era of data sharing in healthcare is beginning to dawn, adds McDonald.

The ideal goal is a digitally enabled virtuous cycle of information and treatment that could save millions of dollars, lives, and perhaps even civilization if we can get there. D!

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

About the Authors:

Dr. David Delaney is Chief Medical Officer for SAP.

Joseph Miles is Global Vice President, Life Sciences, for SAP.

Walt Ellenberger is Senior Director Business Development, Healthcare Transformation and Innovation, for SAP.

Saravana Chandran is Senior Director, Advanced Analytics, for SAP.

Stephanie Overby is an independent writer and editor focused on the intersection of business and technology.


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Digitalist Magazine

Your Customers Called. They Want You to Know These 6 Things

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Businesses are changing how they think.

For years ― decades even ― companies have been focused on their products. The customer experience has always been top of mind, of course. But in a more passive role. If someone wasn’t happy with a product or service, usually they just stopped using it. Maybe they might complain to a couple of people, but probably not too many.

Flash forward to now, when one person who has a poor customer experience can post a bad review on just one site and cause all kinds of havoc. In May, BrightLocal released research that found that if a company in the Google Local Pack can improve its rating from 3 stars to 5, they’ll get 25% more clicks.

Even those review stats look like peanuts compared to what a truly angry and vocal customer can do.

Just consider the case of Dave Carroll.

Dave is a musician. A few years ago he was seated on a United Airlines plane as United personnel loaded up the aircraft on the tarmac below. Dave saw his guitar thrown about and badly broken by staff who seemed to think it was as much a volleyball as someone’s primary earning tool.

Outraged, Dave petitioned multiple United customer service reps for justice … only to be dismissed. Now even more outraged, and still heartbroken over the loss of his beloved guitar, Dave made a video.

“United Breaks Guitars” now has more than 17 million views.

I’m not sure all of United’s ads, cumulatively, have 17 million views. (At least there were no broken bones, right?)

But the power to damage your brand isn’t the only power The Customer now wields.

And just trying to avoid making them angry isn’t enough.

The idea is to make them happy.

As we’ve written about before, happy customers stick around. It’s called loyalty. And if you can improve loyalty by even a paltry 5%, your profits can go up by 25% to 95%.

Happy customers stay longer, spend more ― and also pull in new customers.

How? Their referrals, reviews, comments, and social shares all amplify your brand’s message. All this user-generated content is basically the new word-of-mouth marketing. And as some of you old-time marketers know, word of mouth is the most effective type of marketing for actual ROI.

In short, customers have become so powerful, in so many different ways, that they are basically running the show.

Businesses will please their customers … or die. And when they do, there’s a herd of competitors behind them, waiting to take their place.

Even lucky companies, like Comcast, who all but own their industries, risk losing their customers to competing technologies. Alternative technologies that customers might embrace are always lurking on the edges of the market. They’re just waiting for an opportunity to burst through, like a young tree straining for light in the jungle.

The Customer-Centric Company

Customers are now so powerful, in fact, that many marketing wizards (like Joe Pulizzi, in his book “Content Inc.”) recommend a 180-degree change from the old way of doing things. You start a company by assembling an audience first, then figure out what to sell to them, these experts say. Design the products and the business model around the customer – not the other way around.

That sure flips the old business mindset on its head.

But it works. Companies that delight their customers now rule the business world. Witness Apple, which has more cash on hand than the US government.

So if customers are so important, so able to build empires ― or tear them down ― how can we work with them? How can we leverage their power to keep our businesses thriving?

Well, there are plenty of ways to do that. But you might want to start by finding out what they want you to know.

And they want you to know these things:

1. Your customer service is part of your marketing.

Existing customers experience our brands without the silos we define in our businesses. To them, in a sense, messaging is messaging. That reply about a product issue is a message, and so is that ad they saw about your new product.

If you define marketing as “how we promote ourselves,” your customer service efforts are all marketing for your customers. You promote yourself (or not) by doing a good job for them (or not).

And, like any human interaction, what you say (your advertising, even your content), is less important than what you do (your customer support and other customer-touch activities).

2. I’m the customer. You’re supposed to put my needs ahead of your own.

This is the expectation. And boy, do a lot of companies fall short. Maybe that’s why so many consumers have become jaded. The advertising says: “We put you first” … but then the actual service does not.

There is a particular moment when the customer’s trust breaks. If you’re ever been in sales or customer service, you may have actually been with a customer, in person, to witness that moment.

It happens when the person gets a very clear look on their face, like: “Oh yeah, of course. Now I know you’re only out for yourselves. I should have known it all along.”

After a customer knows you put your own needs ahead of theirs, the romance is over. They may stick around out of habit or convenience, but they will try out one of your competitors as soon as they get the chance.

Marketing Sherpa documented this quite clearly in a recent survey they did for their “Customer Satisfaction Research Study”. It was one of the clearest differences between satisfied and unsatisfied customers.

3. I don’t necessarily want to talk to someone if I need help.

Here’s one for those of you who would rather not have the overhead of a large customer-service staff. Many customers, especially Millennials, would prefer to figure things out for themselves. They want “self-serve” customer service.

The Aspect Consumer Experience Index study found that “73 percent of US consumers said that they should have the ability to solve most product and service issues on their own.”

They really want it, too. From that same study:

How strongly do Americans like or dislike interacting with customer service?

Almost a third of America ― that’s over 100 million people ― would rather clean a toilet than interact with customer service. A quarter of America would rather change a dirty diaper than interact with customer service.

Those are some pretty strong feelings. And, they might well justify the cost of building a useful, detailed, self-serve help section, complete with short video tutorials and maybe even a forum, so customers could help each other and see how other customers’ issues were resolved.

Start building a hub like this by answering the 30 most common questions that your business receives. Then provide the answers via video, text, emails, on your site, via an app ― in every channel possible.

Next, expand to the top 50 answers. Then to the top 100.

4. Loyalty programs are a way for you to be loyal to me, not the other way around.

According to KiteWheel’s “The State of the Customer Journey” report:

“73% of consumers feel loyalty programs ‘should be a way for brands to show how loyal they are to them as a customer.’ However marketing executives disagree; 66% believe loyalty programs are still a way for consumers to show how loyal they are to their business.“

I hate to break it to you, CMOs … but you’re not going to win that argument.

5. I expect you to reply within an hour on social media. Really.

You may have heard this one before. But it bears repeating: According to research from Twitter, 71% of Twitter users expect a brand to respond within an hour of when the customer contacts them on social media.

Not only that, but a third of consumers expect a response within 30 minutes.

That’s internet time for you.

6. Irrelevant marketing is spam.

Here’s how the email tool Litmus describes the situation:

Your customers view any irrelevant or unwanted email as spam. It doesn’t matter how long they’ve been a customer or if they’ve given you permission ― if your email is repeatedly of little to no relevance to them, it’s spam.

Trouble is, consumers ― and customers ― get a lot of irrelevant messages. In fact, 50% of consumers who receive marketing materials on the web or over the phone say this content is irrelevant to them.

A study from Janrain illustrates the problem:

This graphic is assembled from pieces of the 2015 Janrain “US Consumer Research Consumer Identity and Mistargeting” report.


Of course, these aren’t the only things your customers wish you knew. Every company is different. While your customers want all the things we’ve mentioned above, they also want “smaller,” more specific things from your particular products and services.

You need to get that information. Whether you do it through surveys or interviews or behavior tracking, the important thing is to find out what they want.

Of course, just talking to people is often best course. Giving them an open, prioritized voice in your business certainly helps, too.

You may have heard about how Amazon does this. They leave an empty chair at every meeting. It’s to represent the customer.

Perhaps it’s time we pushed that even further, and actually put a customer in the seat.

What do you think?

What do you think is the #1 thing your customers wish you knew ― and actually acted on? Tell us about it in the comments.

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You Need to Understand These 6 Things About What B2B Buyers Want

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4. Understand their needs and where they are coming from.

Again ― this is something that many companies just nail. You can tell from the moment you land on their homepage that they get you and what you need.

Then there are other companies. They mean well, but they don’t seem to really know what you need, even when they say they have what you need. Sometimes, they don’t even appear to be clear on what they offer.

Knowing where a prospect is coming from would include:

  • Having really done your buyer persona homework. You have figured out who your buyers are (based on data, not opinions).
  • Leveraging your sales and customer support staff’s knowledge. You understand the unique needs of each of those groups.
  • Developing content and resources (calculators, assessment tools, videos, case studies, and whitepapers) that are solely for each group.

Some of you ― the truly world-class ones ― have created those resources with so little bias and with such good supporting research that prospects can truly trust what you say. Bravo, all you companies who do this. May your tribe increase.

5. “We don’t do that” is a legitimate answer.

If your company doesn’t do certain things, don’t conceal that information. It’s easier on prospects if we just know up front, without hassle, that your company/service/software doesn’t do certain things.

Don’t make us have to quiz you to reveal that information.

Here’s an example:

Last night I was researching survey tools for an upcoming project. I wanted to add tracking code to the final confirmation page of the survey, so I could see which channels (email, advertising, Twitter, LinkedIn) generate the most completed surveys.

Survey tool #1’s site had a help section. When I searched for “’tracking code’ ‘custom page’”, I immediately got a detailed how-to article with screenshots that explained exactly how to do what I wanted.

They even described some other cool things I could do with the tracking. And I could use Google Analytics ― a free, widely-used tool ― to do the job.


In survey tool #2, the help section was hard to find. Multiple searches about tracking codes revealed nothing. So I contacted customer service.

To their credit, someone got back to me within an hour (this is unusual and they deserve major credit for it).

But the person who responded basically said they had no idea what I was talking about and then wrapped up the email with a cheery pitch.

So I replied, and asked my question two different ways. With as much clarity as I could muster.

They emailed me back (again, within an hour) saying, “No, our software can’t do that.”

I get why companies might want to cover up if their software or service doesn’t do basic things a user might expect it to do. But leaving a vacuum of information like that, then making it hard to confirm there is no functionality for that, just makes life harder. Unnecessarily harder.

This isn’t an isolated incident, either. It’s the second time in as many weeks this sort of thing has happened to me.

When I was researching which graphic design outsourcing service to use, I had a specific question about whether or not they would lay out an email for me. That is, I wanted them to set up copy and images to assemble the email message within my email service provider account, using a pre-defined template.

At first I got a breezy, “Yes ― we do emails! We do anything!”

Still not sure that that was a yes, I asked again.

After four emails back and forth, it was finally revealed that no, they won’t do that. Even though my question had never changed. Their rep ignored what I asked and said yes without really reading my question. They even said that much in their last message.

(Of course, if this is the roughest thing I have to deal with, life is pretty good.)

But it’s a pain to do business like this. It turns prospects off.

I would have had a more positive view of both companies if they had just made that information easy to find. If they had told me upfront, “No, we don’t do that.”

6. Your messaging needs to be clear.

Buyers are zooming through hundreds of pages of marketing collateral, trying to develop a short list of products that could solve their problem. They are looking to winnow out companies that can’t help them.

Weak messages, particularly on your home page, can repel potential buyers.

As Gordana Stok says in her article, “5 Things B2B Buyers Want Your Content To Do

The short-form messages on your home page and product-landing page help convince buyers whether it’s worth their time to take a deeper look at your solution.

To get your value proposition under 100 words and ensure it truly resonates with buyers, you need to be absolutely certain that you understand what they’re looking for in the first place.”

It’s not just Gordana saying this, either. In a research study from Ko Marketing, “Lack of message” was the #1 thing that B2B buyers said annoyed them and made them likely to leave a site. (Note that lack of contact info, mentioned earlier, was #2.)

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It’s Not You, It’s Me: 5 Signs Things Aren’t Working with Your Marketing Automation Platform

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Entering into any relationship making that big or not so big commitment can be fraught with anxiety whether this is the right choice. No one wants to be in a relationship that  isn’t working. And the same is true when evaluating your marketing automation platform.

You could be wasting valuable time with the wrong tech partner. But how do you know when it’s time to pull the plug? Like many of us, it’s hard when you’re in the relationship to see the signs and red flags. Often, we benefit from an outside perspective. Let’s look at some clues that your relationship with your marketing automation (MA) platform might not be working out.

1. Too High-Maintenance

It’s 10 a.m. and a last minute email needs to go out in the next 30 minutes, but your high- maintenance MA system mockingly stares back at you: offering limited email templates, requiring  HTML expertise to customize. Plus, it doesn’t maintain a dynamic email list, which means you’ll need to go in and manually update it yourself.

Don’t let a high maintenance system hold you back. Here are a few ways marketing automation tools should make your marketing life easier:

  • Building your emails and landing pages should be simple and not require help from IT or outside consultants
  • Templates should have responsive design built in. Your MA tool should make viewing content on all sizes of devices foolproof for you
  • Easily design your own custom templates  the way you want and not be limited to just the stock ones provided
  • And when it comes to the complicated stuff, like creating nurturing programs, your platform should be easy like Sunday Morning – such as the ability to build workflows with a drop-and-drag tool.

If you are feeling saddled with a very high-maintenance marketing automation software platform; if you miss simplicity and want to have the freedom to choose your own path, it might be a sign that it’s just not working out.

42% of Marketers say complexity of the system is the most significant barrier to marketing automation success. – Ascend2, “Marketing Automation Trends Survey” (2016)

2. Language Barriers

Unless you have the technical language proficiency to read and/or write HTML, JavaScript and/or CSS (and more), you’d better not pick a marketing automation platform that requires programming knowledge. That is, of course, unless you have  time dedicated to learning those languages or have a team of IT techies at your service to serve as your interpreter.

If the language barrier is already an issue and you’re about to experience a literal “communication breakdown,”it might be a sign that it’s just not working out.

Marketers list “complexity of the system” as one of the most significant barriers to marketing automation success. – Ascend2 “Marketing Automation Trends Survey (2016)

3. The Frustration Factor

The clock is ticking, and in the middle of setting up your email something goes terribly wrong. You submit an online support ticket, wait patiently for what seems to be days, and never hear back. You then decide to call tech support for help and when they finally pick up, they take down your information, and then explain that a technician will call you back when they become available (usually when you are away from your desk). You can’t save the screen you are working on due to faulty HTML programming and you’ll lose your work if you exit. It worked before and you only changed a few words! ARRRG…why is this so difficult to use?

If you feel like you’re always playing a frustrating waiting game because you need significant tech support, it might be a sign that it’s just not working out.

86% marketers consider “ease of use” as the most important criterion when evaluating automation tools. – Regalix “The State of Marketing Automation” (2014)

4. Money:  Hidden Surprises

We’re talking about those unforeseen, but actually-seeable-if-you-look-really-closely things that can be avoided. These little surprises might show up as financial charges for things you thought were free, or built into the price. Or your total cost of ownership might be a lot higher than you expected once you add in the cost of consultants to help in system design, architecture and maintenance.

Also: Pay attention to your database. As a growing company, you should be excited about new contacts, not dreading the new cost associated with storing them. Are you charged for how many people you have in your database or only for your active contacts? Are you warned if you are about to go over your threshold or are you just charged? Surprise!

If you are getting too many ongoing unpleasant surprises – it might be a sign that it’s just not working out.

44% of marketers are not fully satisfied with their marketing automation systems, the top 3 reasons being that the software takes too long to implement, is difficult to learn and is too expensive. – Autopilot, 2015

5. Everyone Needs Support (He’s just not that into you.)

Last but not least, do you feel supported? In a healthy and positive relationship, you should feel nurtured and supported:

  • You don’t want to have a vendor who comes only when called to fix something broken. Instead, you should have a vendor who proactively notices and reaches out when things are looking off.
  • Did your vendor’s customer support help you get thoroughly trained on the product’s capabilities?
  • Did they ensure everything functioned correctly from the get-go, setting you up for quick success?
  • Does your vendor really understand your organization’s needs and goals? Are they committed to helping you be successful and see ROI on your MA investment?
  • Do they stay in touch and ensure you are using all the features of your marketing automation platform?

If your customer support makes you feel like you’re skydiving without a parachute, it’s probably a sign that it’s just not working out.

Act-On’s onboarding process and their university taught more about marketing and marketing automation than I ever could have imagined. – Kevin Rice, Technology Coordinator, Quantum Learning Network

Moral of the story

It’s simple: If your current marketing automation platform is causing you or your marketing team stress in any of these ways (or ways we didn’t mention) it might be time to start looking at other solutions. And, yes, there are other solutions that are right for you.

Don’t settle. You deserve better. Want to learn more about how marketing automation vendors stack up when it comes to customer service and ease of use? Read the latest reviews from G2 Crowd.

In fact, we know just the perfect match for you…Even your mom would approve!

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5 Ways The Internet Of Things Can Improve Efficiency

When it comes to buying things—even big-ticket items—the way we make decisions makes no sense. One person makes an impulsive offer on a house because of the way the light comes in through the kitchen windows. Another gleefully drives a high-end sports car off the lot even though it will probably never approach the limits it was designed to push.

We can (and usually do) rationalize these decisions after the fact by talking about needing more closet space or wanting to out-accelerate an 18-wheeler as we merge onto the highway, but years of study have arrived at a clear conclusion:

When it comes to the customer experience, human beings are fundamentally irrational.

In the brick-and-mortar past, companies could leverage that irrationality in time-tested ways. They relied heavily on physical context, such as an inviting retail space, to make products and services as psychologically appealing as possible. They used well-trained salespeople and employees to maximize positive interactions and rescue negative ones. They carefully sequenced customer experiences, such as having a captain’s dinner on the final night of a cruise, to play on our hard-wired craving to end experiences on a high note.

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Today, though, customer interactions are increasingly moving online. Fortune reports that on 2016’s Black Friday, the day after Thanksgiving that is so crucial to holiday retail results, 108.5 million Americans shopped online, while only 99.1 million visited brick-and-mortar stores. The 9.4% gap between the two was a dramatic change from just one year prior, when on- and offline Black Friday shopping were more or less equal.

When people browse in a store for a few minutes, an astute salesperson can read the telltale signs that they’re losing interest and heading for the exit. The salesperson can then intervene, answering questions and closing the sale.

Replicating that in a digital environment isn’t as easy, however. Despite all the investments companies have made to counteract e-shopping cart abandonment, they lack the data that would let them anticipate when a shopper is on the verge of opting out of a transaction, and the actions they take to lure someone back afterwards can easily come across as less helpful than intrusive.

In a digital environment, companies need to figure out how to use Big Data analysis and digital design to compensate for the absence of persuasive human communication and physical sights, sounds, and sensations. What’s more, a 2014 Gartner survey found that 89% of marketers expected customer experience to be their primary differentiator by 2016, and we’re already well into 2017.

As transactions continue to shift toward the digital and omnichannel, companies need to figure out new ways to gently push customers along the customer journey—and to do so without frustrating, offending, or otherwise alienating them.

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The quest to understand online customers better in order to influence them more effectively is built on a decades-old foundation: behavioral psychology, the study of the connections between what people believe and what they actually do. All of marketing and advertising is based on changing people’s thoughts in order to influence their actions. However, it wasn’t until 2001 that a now-famous article in the Harvard Business Review formally introduced the idea of applying behavioral psychology to customer service in particular.

The article’s authors, Richard B. Chase and Sriram Dasu, respectively a professor and assistant professor at the University of Southern California’s Marshall School of Business, describe how companies could apply fundamental tenets of behavioral psychology research to “optimize those extraordinarily important moments when the company touches its customers—for better and for worse.” Their five main points were simple but have proven effective across multiple industries:

  1. Finish strong. People evaluate experiences after the fact based on their high points and their endings, so the way a transaction ends is more important than how it begins.
  2. Front-load the negatives. To ensure a strong positive finish, get bad experiences out of the way early.
  3. Spread out the positives. Break up the pleasurable experiences into segments so they seem to last longer.
  4. Provide choices. People don’t like to be shoved toward an outcome; they prefer to feel in control. Giving them options within the boundaries of your ability to deliver builds their commitment.
  5. Be consistent. People like routine and predictability.

For example, McKinsey cites a major health insurance company that experimented with this framework in 2009 as part of its health management program. A test group of patients received regular coaching phone calls from nurses to help them meet health goals.

The front-loaded negative was inherent: the patients knew they had health problems that needed ongoing intervention, such as weight control or consistent use of medication. Nurses called each patient on a frequent, regular schedule to check their progress (consistency and spread-out positives), suggested next steps to keep them on track (choices), and cheered on their improvements (a strong finish).

McKinsey reports the patients in the test group were more satisfied with the health management program by seven percentage points, more satisfied with the insurance company by eight percentage points, and more likely to say the program motivated them to change their behavior by five percentage points.

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The nurses who worked with the test group also reported increased job satisfaction. And these improvements all appeared in the first two weeks of the pilot program, without significantly affecting the company’s costs or tweaking key metrics, like the number and length of the calls.

Indeed, an ongoing body of research shows that positive reinforcements and indirect suggestions influence our decisions better and more subtly than blatant demands. This concept hit popular culture in 2008 with the bestselling book Nudge.

Written by University of Chicago economics professor Richard H. Thaler and Harvard Law School professor Cass R. Sunstein, Nudge first explains this principle, then explores it as a way to help people make decisions in their best interests, such as encouraging people to eat healthier by displaying fruits and vegetables at eye level or combatting credit card debt by placing a prominent notice on every credit card statement informing cardholders how much more they’ll spend over a year if they make only the minimum payment.

Whether they’re altruistic or commercial, nudges work because our decision-making is irrational in a predictable way. The question is how to apply that awareness to the digital economy.

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In its early days, digital marketing assumed that online shopping would be purely rational, a tool that customers would use to help them zero in on the best product at the best price. The assumption was logical, but customer behavior remained irrational.

Our society is overloaded with information and short on time, says Brad Berens, Senior Fellow at the Center for the Digital Future at the University of Southern California, Annenberg, so it’s no surprise that the speed of the digital economy exacerbates our desire to make a fast decision rather than a perfect one, as well as increasing our tendency to make choices based on impulse rather than logic.

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Buyers want what they want, but they don’t necessarily understand or care why they want it. They just want to get it and move on, with minimal friction, to the next thing. “Most of our decisions aren’t very important, and we only have so much time to interrogate and analyze them,” Berens points out.

But limited time and mental capacity for decision-making is only half the issue. The other half is that while our brains are both logical and emotional, the emotional side—also known as the limbic system or, more casually, the primitive lizard brain—is far older and more developed. It’s strong enough to override logic and drive our decisions, leaving rational thought to, well, rationalize our choices after the fact.

This is as true in the B2B realm as it is for consumers. The business purchasing process, governed as it is by requests for proposals, structured procurement processes, and permission gating, is designed to ensure that the people with spending authority make the most sensible deals possible. However, research shows that even in this supposedly rational process, the relationship with the seller is still more influential than product quality in driving customer commitment and loyalty.

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Baba Shiv, a professor of marketing at Stanford University’s Graduate School of Business, studies how the emotional brain shapes decisions and experiences. In a popular TED Talk, he says that people in the process of making decisions fall into one of two mindsets: Type 1, which is stressed and wants to feel comforted and safe, and Type 2, which is bored or eager and wants to explore and take action.

People can move between these two mindsets, he says, but in both cases, the emotional brain is in control. Influencing it means first delivering a message that soothes or motivates, depending on the mindset the person happens to be in at the moment and only then presenting the logical argument to help rationalize the action.

In the digital economy, working with those tendencies means designing digital experiences with the full awareness that people will not evaluate them objectively, says Ravi Dhar, director of the Center for Customer Insights at the Yale School of Management. Since any experience’s greatest subjective impact in retrospect depends on what happens at the beginning, the end, and the peaks in between, companies need to design digital experiences to optimize those moments—to rationally design experiences for limited rationality.

This often involves making multiple small changes in the way options are presented well before the final nudge into making a purchase. A paper that Dhar co-authored for McKinsey offers the example of a media company that puts most of its content behind a paywall but offers free access to a limited number of articles a month as an incentive to drive subscriptions.

Many nonsubscribers reached their limit of free articles in the morning, but they were least likely to respond to a subscription offer generated by the paywall at that hour, because they were reading just before rushing out the door for the day. When the company delayed offers until later in the day, when readers were less distracted, successful subscription conversions increased.

Pre-selecting default options for necessary choices is another way companies can design digital experiences to follow customers’ preference for the path of least resistance. “We know from a decade of research that…defaults are a de facto nudge,” Dhar says.

For example, many online retailers set a default shipping option because customers have to choose a way to receive their packages and are more likely to passively allow the default option than actively choose another one. Similarly, he says, customers are more likely to enroll in a program when the default choice is set to accept it rather than to opt out.

Another intriguing possibility lies in the way customers react differently to on-screen information based on how that information is presented. Even minor tweaks can have a disproportionate impact on the choices people make, as explained in depth by University of California, Los Angeles, behavioral economist Shlomo Benartzi in his 2015 book, The Smarter Screen.

A few of the conclusions Benartzi reached: items at the center of a laptop screen draw more attention than those at the edges. Those on the upper left of a screen split into quadrants attract more attention than those on the lower left. And intriguingly, demographics are important variables.

Benartzi cites research showing that people over 40 prefer more visually complicated, text-heavy screens than younger people, who are drawn to saturated colors and large images. Women like screens that use a lot of different colors, including pastels, while men prefer primary colors on a grey or white background. People in Malaysia like lots of color; people in Germany don’t.

This suggests companies need to design their online experiences very differently for middle-aged women than they do for teenage boys. And, as Benartzi writes, “it’s easy to imagine a future in which each Internet user has his or her own ‘aesthetic algorithm,’ customizing the appearance of every site they see.”

Applying behavioral psychology to the digital experience in more sophisticated ways will require additional formal research into recommendation algorithms, predictions, and other applications of customer data science, says Jim Guszcza, PhD, chief U.S. data scientist for Deloitte Consulting.

In fact, given customers’ tendency to make the fastest decisions, Guszcza believes that in some cases, companies may want to consider making choice environments more difficult to navigate— a process he calls “disfluencing”—in high-stakes situations, like making an important medical decision or an irreversible big-ticket purchase. Choosing a harder-to-read font and a layout that requires more time to navigate forces customers to work harder to process the information, sending a subtle signal that it deserves their close attention.

That said, a company can’t apply behavioral psychology to deliver a digital experience if customers don’t engage with its site or mobile app in the first place. Addressing this often means making the process as convenient as possible, itself a behavioral nudge.

A digital solution that’s easy to use and search, offers a variety of choices pre-screened for relevance, and provides a friction-free transaction process is the equivalent of putting a product at eye level—and that applies far beyond retail. Consider the Global Entry program, which streamlines border crossings into the U.S. for pre-approved international travelers. Members can skip long passport control lines in favor of scanning their passports and answering a few questions at a touchscreen kiosk. To date, 1.8 million people have decided this convenience far outweighs the slow pace of approvals.

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The basics of influencing irrational customers are essentially the same whether they’re taking place in a store or on a screen. A business still needs to know who its customers are, understand their needs and motivations, and give them a reason to buy.

And despite the accelerating shift to digital commerce, we still live in a physical world. “There’s no divide between old-style analog retail and new-style digital retail,” Berens says. “Increasingly, the two are overlapping. One of the things we’ve seen for years is that people go into a store with their phones, shop for a better price, and buy online. Or vice versa: they shop online and then go to a store to negotiate for a better deal.”

Still, digital increases the number of touchpoints from which the business can gather, cluster, and filter more types of data to make great suggestions that delight and surprise customers. That’s why the hottest word in marketing today is omnichannel. Bringing behavioral psychology to bear on the right person in the right place in the right way at the right time requires companies to design customer experiences that bridge multiple channels, on- and offline.

Amazon, for example, is known for its friction-free online purchasing. The company’s pilot store in Seattle has no lines or checkout counters, extending the brand experience into the physical world in a way that aligns with what customers already expect of it, Dhar says.

Omnichannel helps counter some people’s tendency to believe their purchasing decision isn’t truly well informed unless they can see, touch, hear, and in some cases taste and smell a product. Until we have ubiquitous access to virtual reality systems with full haptic feedback, the best way to address these concerns is by providing personalized, timely, relevant information and feedback in the moment through whatever channel is appropriate. That could be an automated call center that answers frequently asked questions, a video that shows a product from every angle, or a demonstration wizard built into the product. Any of these channels could also suggest the customer visit the nearest store to receive help from a human.

sap Q217 digital double feature1 images4 5 Ways The Internet Of Things Can Improve Efficiency

The omnichannel approach gives businesses plenty of opportunities to apply subtle nudges across physical and digital channels. For example, a supermarket chain could use store-club card data to push personalized offers to customers’ smartphones while they shop. “If the data tells them that your goal is to feed a family while balancing nutrition and cost, they could send you an e-coupon offering a discount on a brand of breakfast cereal that tastes like what you usually buy but contains half the sugar,” Guszcza says.

Similarly, a car insurance company could provide periodic feedback to policyholders through an app or even the digital screens in their cars, he suggests. “Getting a warning that you’re more aggressive than 90% of comparable drivers and three tips to avoid risk and lower your rates would not only incentivize the driver to be more careful for financial reasons but reduce claims and make the road safer for everyone.”

Digital channels can also show shoppers what similar people or organizations are buying, let them solicit feedback from colleagues or friends, and read reviews from other people who have made the same purchases. This leverages one of the most familiar forms of behavioral psychology—reinforcement from peers—and reassures buyers with Shiv’s Type 1 mindset that they’re making a choice that meets their needs or encourages those with the Type 2 mindset to move forward with the purchase. The rational mind only has to ask at the end of the process “Am I getting the best deal?” And as Guszcza points out, “If you can create solutions that use behavioral design and digital technology to turn my personal data into insight to reach my goals, you’ve increased the value of your engagement with me so much that I might even be willing to pay you more.”

sap Q217 digital double feature1 images10 1024x572 5 Ways The Internet Of Things Can Improve Efficiency

Many transactions take place through corporate procurement systems that allow a company to leverage not just its own purchasing patterns but all the data in a marketplace specifically designed to facilitate enterprise purchasing. Machine learning can leverage this vast database of information to provide the necessary nudge to optimize purchasing patterns, when to buy, how best to negotiate, and more. To some extent, this is an attempt to eliminate psychology and make choices more rational.

B2B spending is tied into financial systems and processes, logistics systems, transportation systems, and other operational requirements in a way no consumer spending can be. A B2B decision is less about making a purchase that satisfies a desire than it is about making a purchase that keeps the company functioning.

That said, the decision still isn’t entirely rational, Berens says. When organizations have to choose among vendors offering relatively similar products and services, they generally opt for the vendor whose salespeople they like the best.

This means B2B companies have to make sure they meet or exceed parity with competitors on product quality, pricing, and time to delivery to satisfy all the rational requirements of the decision process. Only then can they bring behavioral psychology to bear by delivering consistently superior customer service, starting as soon as the customer hits their app or website and spreading out positive interactions all the way through post-purchase support. Finishing strong with a satisfied customer reinforces the relationship with a business customer just as much as it does with a consumer.

sap Q217 digital double feature1 images11 1024x572 5 Ways The Internet Of Things Can Improve Efficiency

The best nudges make the customer relationship easy and enjoyable by providing experiences that are effortless and fun to choose, on- or offline, Dhar says. What sets the digital nudge apart in accommodating irrational customers is its ability to turn data about them and their journey into more effective, personalized persuasion even in the absence of the human touch.

Yet the subtle art of influencing customers isn’t just about making a sale, and it certainly shouldn’t be about persuading people to act against their own best interests, as Nudge co-author Thaler reminds audiences by exhorting them to “nudge for good.”

Guszcza, who talks about influencing people to make the choices they would make if only they had unlimited rationality, says companies that leverage behavioral psychology in their digital experiences should do so with an eye to creating positive impact for the customer, the company, and, where appropriate, the society.

In keeping with that ethos, any customer experience designed along behavioral lines has to include the option of letting the customer make a different choice, such as presenting a confirmation screen at the end of the purchase process with the cold, hard numbers and letting them opt out of the transaction altogether.

“A nudge is directing people in a certain direction,” Dhar says. “But for an ethical vendor, the only right direction to nudge is the right direction as judged by the customers themselves.” D!

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

About the Authors:

Volker Hildebrand is Global Vice President for SAP Hybris solutions.

Sam Yen is Chief Design Officer and Managing Director at SAP.

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

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A few new things in XML plan to help you troubleshoot query performance

In this blog Added per-operator level performance stats for Query Processing, Senior PM in QP talks about extending operator level performance stats.  They include stats related to reads, CPU and elapse time.  These are very helpful to track down query performance issues.  We worked on recent case where we put ActualElapsedms in a good use.

In this case, customer stated a merge query ran very slowly and wanted us to help improve the performance. By examining ActualElapsedms  which is displayed as “Actual Elapsed Time(ms)”, we quickly found out which operator was taking most of the time.  In this case, it’s a “Distributed streams” parallel operator that caused most slowdown.  Upon further operation, the operator caused spill (‘exchange spill’).  so we were able to provide a relief to use serial plan. The original plan took over 20 minutes but serial plan took less than a 1 minute.  Knowing how where most of the time is spent will help you understand the issue sooner.

image thumb524 A few new things in XML plan to help you troubleshoot query performance

I also want to point out another improvement as documented in SQL 2016 SP1 release. We also added top wait stats for the query.  if your query runs slowly but consumes very little CPU, this will be a great help. In the past, the only way to get this is to use wait_info xevent.  Now it’s part of xml execution plan.  Below is an example of wait stats gathered from same plan as above.  Please note that you just have WaitStats for the entire query. You can search WaitStats in XML file.  But in SSMS, you will need to click on the very first node of the plan to get this property.

image thumb525 A few new things in XML plan to help you troubleshoot query performance


Note that you may not be able to see the above properties either in XML or SSMS.  That may be because you are not on the right version. Additionally, the fact that they are in the xml plan, you may not see them in SSMS if you are not using right version of SSMS.

For per operator level stats (such as logical reads, elapse time) to be included in the plan, you must be on SQL Server 2014 SP2 or SQL 2016 RTM.

For the top wait statis (WaitStats) to be included, you must be on SQL Server 2016 Sp1.

For both properties to show up in SSMS, you must be  latest SSMS (Nov 2016 or later release). you can get latest SSMS from Download SQL Server Management Studio (SSMS).

Jack Li |Senior Escalation Engineer | Microsoft SQL Server

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