Tag Archives: Experience

Leading with Customer Experience, Value, Technology, and Credibility

rsz bigstock golden trophy cup on table 155670149 1 Leading with Customer Experience, Value, Technology, and Credibility

When I was an industry analyst, I always felt that the most enlightening and valuable research came from first hand, end user feedback. Who better to hear from than practicing professionals doing real world work? Pleasing customers with enterprise software isn’t for the faint of heart and if you really want unvarnished insights, end users should be the core of your critical feedback loop.

It’s for this reason the team at TIBCO is so excited to see the results of The Wisdom of Crowds® Business Intelligence and Enterprise Planning Market Studies delivered by Dresner Advisory Services, LLC. This research speaks directly to the end user community on a wide variety of categories to unearth a complete view of market realities, plans, and perceptions from users in all roles and across industries.

This month Dresner Advisory Services announced its 2017 Industry Excellence Awards based on high vendor ratings in their most recent research. TIBCO Spotfire achieved awards as a Customer Experience Leader and a Value Leader, while TIBCO Statistica received the Technology Leader and Credibility Leader awards. Both solutions were acknowledged for their overall strength in sales, support, consulting services, and more. Vendors who are awarded Customer Experience and Technology leaders are executing at a high level for sales and service, as well as product and technology. Credibility and Value leaders have customers who reflect a high level of confidence and sense of value for the price paid.

Dresner’s Wisdom of Crowds research started in 2010 and dives deep when appraising vendors performance by tracking 33 different criteria across 7 topic areas that include, Sales Experience, Value for Price Paid, Technology/Product, Technical Support, Consulting Services, Customer Recommendation, and Vendor Integrity.

The Wisdom of Crowds research examines the details of our industry and surfaces positive trends that point to great progress for Business Intelligence and Analytic consumers. When reviewing the the Value dimension of the Dresner report a positive trend emerges: Since 2012, respondents to the survey are scoring the vendors with progressively higher value scores year over year. Keeping up with this competitive landscape puts pressure on solution providers, making it harder to compete and in the case of Spotfire even more satisfying to be among the leaders in this area.

Dresner tracks 12 different criteria to score product quality and usefulness, which includes robustness/sophistication of technology, completeness of functionality, reliability of technology, scalability, integration of components within product, integration with third-party technologies, overall usability, ease of installation, ease of administration, customization and extensibility, online training, forums and documentation, and ease of upgrades and migration to new versions. All saw increases in 2017 except ease of upgrades again. This trend points to increased competition and maturity in the market, making it more difficult to rise to leadership positions in the research.

The vendor credibility model employed by Dresner combines the value for price paid as scored by the user along with a vendor’s integrity score (honesty and truthfulness in all dealings) and recommendation score (customers willingness to recommend the vendor) to create an overall confidence dimension. The value and confidence dimensions position where a vendor is placed in the overall rankings. TIBCO Statistica placement among credibility leaders is an award to be proud of considering the competition and the scoring criteria.

The 2017 Industry Excellence Awards speak highly of TIBCO’s analytic strategy and our Connected Intelligence approach to digital transformation. To differentiate and maintain competitive advantage, smart companies should rely on solutions that lead in Customer Experience, Value, Technology, and Credibility.

Read more about the 2017 Excellence Awards here.

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The TIBCO Blog

Xiaomi Opens First Offline Experience Store In Northwestern China

Xiaomi’s first offline experience store in the northwestern region of China opened in Wangfujing Outlets, Hohhot, Inner Mongolia.

On the first day of the opening of this new store, it welcomed 12,000 customers and realized sales of CNY596,000.

This is the eighth store under the cooperation between Wangfujing Group and Xiaomi and it is also the first Xiaomi store that opens in Wangfujing Outlets.

With an operating area of 215 square meters, the new store includes a 125-square-meter customer experience zone, a 50-square-meter product sales zone, and a rest area. Products displayed in the store include Xiaomi’s smartphones, laptops, home appliances, and electronic product accessories.

Yang Haiyan, general manager of Hohhot Wangfujing Outlets, said that the arrival of Xiaomi further improves the brand quality of Hohhot Wangfujing Outlets and expands their category operating models. The emerging Internet electronic products will offer better shopping experience to local consumers.

So far, Wangfujing Group has become the largest commercial group partner of Xiaomi. During the first half of 2017, Xiaomi stores opened in Wangfujing achieved accumulated sales of CNY150 million and the sales expect to reach nearly CNY400 million for the entire year.

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An Inside-Out Approach to ERP can Deliver a Modern Customer Experience

websitelogo An Inside Out Approach to ERP can Deliver a Modern Customer Experience

Posted by Anand Misra, Principal Product Marketing Manager

For businesses today still running siloed, departmental solutions of yesterday, there are daily challenges meeting the needs of the modern consumer. The answer for many is to turn their ERP system inside out.

Consumers today have virtually unlimited options for researching and purchasing products, with online sales and new digital channels providing not only transparency into pricing but the actual shopping experience for millions of shoppers around the world. That ubiquity of information has raised expectations and most businesses are having a hard time delivering on.

A chief culprit for the challenge in delivering an omnichannel customer experience is the ERP system itself. Traditionally, ERP software was built to serve the needs of employees, not today’s consumers, let alone the partners and vendors that are a critical part of any modern enterprise. Today’s customers expect accurate inventory information, cross-channel order history and flawless order execution. These are inherently difficult for the legacy ERP systems from the ‘90s that too many businesses are still running on today. Those systems were designed around departmental processes, rather than around the customer and many of the newer versions of those older systems struggle to cast of the legacy of their origins. When the internet emerged with new platforms to transform the way companies deliver product support and information to customers, most companies just began bolting on ecommerce and content management systems that were disconnected from the system of record. To this day, customer data is still spread across CRM, ecommerce, marketing and multiple systems of record, making it near impossible to reward the most profitable customers, predict demand or ensure repeat business.

The answer for many is to spend hundreds of thousands of dollars trying to integrate these separate systems to support their omnichannel ambitions. The results are mixed, with integrations breaking with software upgrades, a lack of real-time visibility as data transfers are done in batches and companies still left with software designed to support employees rather than customers.

These companies fail to realize the depth to which the need to redesign their core infrastructure. Every aspect of this infrastructure needs to be evaluated to design around a customer-centric model from the beginning. They need to turn the ERP system inside out with the explicit goal of improving the customer experience.

Companies that orient around their customers and directly connect demand to a digitally-enabled supple chain will be the long-term winners. Amazon, the prime example, has built its infrastructure to take advantage of global product and price transparency, even dynamically pricing versus competitors.

Today, a company no longer needs to physically own a product to sell it on its website. If a retailer knows a vendor has inventory, it can take the order without ever possessing the product. And beyond supply chain efficiencies, there was incredible efficiency from operating at scale. As the businesses grow, the incremental costs associated with demand could be handled with far fewer employees and far lower inventory costs.

Newer companies that built (or rebuilt) from the ground up with ecommerce and a digital supply chain reap significant advantages:

  • Visibility into supplier and manufacturer inventory.
  • Responsive, consistently excellent customer service.
  • The ability to track and evaluate customer buying histories, behaviors and preferences.
  • Customer profiling and product recommendations for better targeting.
  • Customer self-service through low-cost online portals.

What started as a challenge for B2C companies is now manifesting in the B2B world. B2B customers that have seen the ease of use, visibility and real-time information provided in the B2C world, couple with a new generation of employees that knows no other way, are forcing B2B companies to reimagine their own processes.

Delivering the best customer experiences requires wholesale changes: in organizational structure, in culture and in IT systems. It requires a more modern infrastructure built around the customer. A modern infrastructure is an investment that will pay off in the years and decades to come. But finally, the ultimate goal is within reach: give customers a personalized, relevant and consistent experience across every channel.

For more on the power of building ERP around the customer, download the white paper Customer Commerce: Turning Your ERP Inside Out.

Learn how NetSuite helps create ubiquitous customer experience, helps differentiate your brand and exceed customer expectations: www.suitecommerce.com

Posted on Wed, August 16, 2017
by NetSuite filed under

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3 Ways Personalized Marketing Improves the Consumer Experience

blog title diverse personas 351x200 3 Ways Personalized Marketing Improves the Consumer Experience

For today’s marketers, personalization is no longer a fancy buzzword. It’s a necessary part of a robust marketing strategy.

Consumers expect custom, one-to-one content – regardless of the device or platform they choose to use at a particular moment. They’re looking to feel a real relationship with the companies and brands they engage with and they aren’t satisfied feeling like just a number. They demand to be treated as a valued individual.

Just how important is personalization? According to a report from Econsultancy, 48% of marketers strongly agree that their growth depends on personalizing the consumer’s experience. On the other hand, only 35% of consumers say that communications from their companies are usually relevant. Therefore, opportunities are plentiful for those marketers looking to bridge that gap.

The best marketers are constantly searching for new and innovative ways to engage their audiences, which ultimately results in conversions and expansions to their bottom lines. Personalization provides not only a way for marketers to improve the overall consumer journey and experience, but to truly add value in the process. All the while these strategies are building the ever-important strong, lasting relationships with each customer.

This personal touch must remain at the heart of almost everything marketers do. As readers of this blog know, consumer experience is considered the next new market differentiator. For a brand to provide the best of the best experience, customized engagement needs to be a key player in the game plan. While personalizing emails is still one of the strongest strategies, there are quite a few others. To start, here are three ways personalized marketing efforts can improve the overall customer journey.

1. Personalized Marketing Based on Geography and Behavioral Data

The digital world is bringing people closer and closer each day. That provides an extremely large amount of data that marketers can analyze and utilize. By simply keeping each individual network at the forefront when developing tactics, marketers can easily reach targeted audience groups. By segmenting according to time zones or by individual behaviors, marketers can better provide content at a time when consumers are going to see it.

For example, one consumer may continuously check email on the train en route to work in the morning; another may check social media on the way home, as a way to unwind. By tracking this data, marketers can tailor their efforts to the time zone and geographical location of each recipient. They then can create and issue more relevant material. That will in turn direct content through a more personalized approach, allowing marketers to show they are not only paying attention to their customers’ locations and preferences, but are also willing to work to improve the consumer experience.

2. Providing Personalized Product Recommendations

Finding ways to further personalize content can be challenging. However, new marketing technology tracks what and when the consumer is purchasing, as well as where they are browsing. These statistics can allow marketers to determine, based on historical data, what products are the most relevant to the consumer, as well as indicate which products, services, or other offers or information will bring about a higher conversion rate.

More importantly, when a consumer opens an email or a social media account and is served with information that resonates with them, they feel increasingly connected to the company. That creates a warmer, more one-to-one consumer experience that they will likely want to have again.

3. Working within the Consumer’s Preferred Channel and Device

Some consumers favor email. Others are more inclined to be social media savvy. And still others like to be targeted via SMS. Today’s customers also use a variety of devices. By paying attention to what people best engage with, marketers know which channels and devices to target.

If a consumer is a mobile-first or mobile-only user, marketers should naturally tailor their marketing tactics toward mobile. By paying attention to where consumers are, companies can better personalize their messaging and reach people where they prefer, creating a better overall consumer experience.

Ready for Advanced Personalized Marketing?

Marketers are inundated with new, highly innovative technology each and every day. It has certainly allowed for greater personalization and a better consumer experience through the data tracking it provides. But only those marketers willing to engage with it can reap the benefits.

Certainly such target-specific labor can be challenging, but it’s also well worth the effort. In today’s competitive landscape, marketers must work to not only differentiate, but also to meet the expectations of consumers who now both want and expect to be treated as unique individuals.

Knowing how personalized marketing can help boost brands is only half the battle. The next step is learning how to implement targeted strategies. Luckily, Act-On has that covered, too.

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Regina Andrew Design Overhauls Its B2B Ecommerce Experience with NetSuite

Posted by Maggie Miller, Senior Commerce Content Manager

Regina%20Andrew Regina Andrew Design Overhauls Its B2B Ecommerce Experience with NetSuite

In 1997, husband and wife team Carla “Regina” Zajac and Jimi “Andrew” Slaven turned their passion for art into Regina Andrew Design, a wholesale manufacturer specializing in lighting, furniture and other home décor. Running on siloed, legacy systems for years, the couple knew the company would ultimately need to improve its inventory management and streamline operations, while continuing to provide a best of class customer experience.

Regina Andrew Design chose the NetSuite unified cloud platform, replacing its legacy systems with NetSuite’s ERP, ecommerce, inventory and order management, CRM and WMS. The unified cloud-based solution allows the company to process orders faster, lower operational costs and deliver an engaging online experience for its B2B buyers.

Optimizing Inventory Management

One of the biggest challenges for the growing company was the inventory management of more than 1,000 SKUs that turn over 30 times a year.

“When we introduced new products it was a lot of manual work,” said James Bonomo, Chief Operating Officer. “Another major issue was that our old system lacked the ability to show real-time inventory data.”

Now that it has a single source of inventory data with NetSuite’s warehouse management solution, Regina Andrew Design has been able to better manage their inventory and increase warehouse efficiencies. An EDI SuiteApp solution from NetSuite partner SPS Commerce streamlines transactions with major retailers such as Neiman Marcus and Lamps Plus. 

B2B Functionality with a B2C Experience

With customers ranging in size from individual designers to large retailers, Regina Andrew Design looked to the website to better support its buyers. Leadership knew they needed to provide an online experience that felt like a B2C website, but with the added functionality B2B buyers need.

They replaced a Magento ecommerce site with NetSuite SuiteCommerce and saw sales increase in the first month. “It was like night and day,” said Bonomo. The new website displays detailed product information, images, related items and other product suggestions.

To support retail partners, the website provides a store locator for consumers interested in their products to search for retailers in their area. Bonomo also likes the integration with FedEx and UPS that offers real-time rates and allows customers to decide how they want orders shipped. With the previous system, it was limited to flat-rate percentages based on zones.

Once a buyer logs into the website they can see their account pricing, get real-time current and future inventory availability and place orders. Account management has also been streamlined. Clients can now view their account balances, apply credits and make payments to outstanding invoices.

Another new feature is the ability for designers to create project lists. Designers can easily manage multiple projects, add items appropriate for each project and purchase. With extensive product information and detailed images, designers can use the website as online tear sheets and even disable pricing information when presenting to clients.

Empowering Sales Representatives 

The SuiteCommerce website has also transformed the role of the sales representatives at Regina Andrew Design. Instead of taking and processing orders manually, sales representatives can focus time on value-add services and building brand loyalty.

“We can’t be everywhere,” said Bonomo. “The new website has empowered our customers and given them the opportunity to order 24/7 – when it’s convenient for them.”

With real-time visibility into what customers are buying, sales representatives can spend time understanding individual customer preferences and serve as more of a consultant to customers. This enables reps to suggest new products to customers that are similar to items they’ve previously purchased. Spending less time on administrative tasks also enables B2B sales reps to spend more time engaging with new buyers to increase awareness and sales of the brand.

“By implementing the NetSuite unified cloud-based system, we have lowered IT costs, streamlined our business and improved the customer experience,” said Bonomo.

Learn more about creating rich and engaging online experiences with SuiteCommerce.

Posted on Wed, June 14, 2017
by NetSuite filed under

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A Continuous Finance Experience

When the Netflix series House of Cards premiered in 2013, it quickly became the most downloaded content in the company’s history – a statistic that came as no surprise to Netflix executives. They had previously examined a vast pool of Netflix data on subscribers’ viewing habits and determined that the show was likely to become a hit even before they purchased it.

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The wisdom behind Netflix’s sure-fire choice came from machine learning, which, loosely defined, is the ability of computers to learn on their own (without being programmed) by using algorithms that churn through large quantities of data.

Machine learning’s talents aren’t limited to picking the next TV blockbuster, either. Consider some of the more down-to-earth uses that we already take for granted today. Have you noticed how spam e-mails have almost disappeared from your inbox? That’s machine learning. Or how you can casually converse with anthropomorphic voices coming from your smartphone? Also machine learning.

But these examples pale when compared to machine learning’s potential for remaking business. Increased data-processing power, the availability of Big Data, the Internet of Things, and improvements in algorithms are converging to power a renaissance in business intelligence.

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The untapped potential of machine learning

Here are some ways that machine learning could transform the core elements of the business ecosystem– and society:

Intelligent business processes. Many of today’s business processes are governed by rigid, software based rules. This rules-based approach is limited in its ability to tackle complex processes. Further, these processes often require employees to spend time on boring, highly repetitive work, such as checking invoices and travel expenses for accuracy or going through hundreds or thousands of résumés to fill a position. If we change the rules and let self-learning algorithms loose on the data, machine learning could reveal valuable new patterns and solutions that we never knew existed. Meanwhile, employees could be reassigned to more engaging and strategic tasks.

Intelligent infrastructure. Our economy depends on infrastructure, including energy, logistics, and IT, as well as on services that support society, such as education and healthcare. But we seem to have reached an efficiency plateau in these areas. Machine learning has the potential to discover new signals in the data that could allow for continuous improvement of complex and fast-changing systems. That gives humans more time to apply their creativity (something that machines may never learn to duplicate) to new discoveries and innovation.

Digital assistants and bots. Recent advances in machine learning technology suggest a future in which robots, machines, and devices running on self-learning algorithms will operate much more independently than they do now. They may come to their own conclusions within certain parameters, adapt their behavior to different situations, and interact with humans much more closely. Our devices – already able to react to our voices – will become more interactive, continuously learning assistants to help us with our daily business routines, such as scheduling meetings, translating documents, or analyzing text and data.

Plan for change

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Although machine learning has already matured to the point where it should be a vital part of organizations’ strategic planning, several factors could limit its progress if leaders don’t plan carefully. These limitations include the quality of data, the abilities of human programmers, and cultural resistance to new ways of working with machines. However, the question is when, not if, today’s data analysis methods become quaint relics of earlier times. This is why organizations must begin experimenting with machine learning now and take the necessary steps to prepare for its widespread use over the coming years.

What is driving this inexorable march toward a world that was largely constrained to cheesy sci-fi novels just a few decades ago? Advances in artificial intelligence, of which machine learning is a subset, have a lot to do with it. AI is based on the idea that even if machines can’t (yet) duplicate the actual structures and thought patterns of the human brain itself, they can at least offer a rough approximation of important functions, such as learning, reasoning, and problem solving.

AI has been around since the 1950s, but it didn’t take off until the late 1990s, when Moore’s Law’s true exponential effects on computing power were realized, and researchers reined in their impulses to build a mechanized brain, focusing instead on using algorithms and machine learning to solve specific problems. Highly publicized machine-learning triumphs by IBM, such as Watson’s drubbing of human contestants on Jeopardy, captured the imagination of the public and business leaders.

Machine learning comes in several flavors, sometimes referred to as supervised learning  (the algorithm is trained using examples where the input data and the correct answers are known), unsupervised learning (the algorithm must discover patterns in the data on its own), and reinforced learning  (the algorithm is rewarded or penalized for the actions it takes based on trial and error). In each case, the machine can learn from data – both structured (such as data in fields in a spreadsheet or database) and, increasingly, unstructured (such as e-mails or social media posts) – without explicitly being programmed to do so, absorbing new behaviors and functions over time.

Machines’ ability to learn puts them on an evolutionary path not unlike our own. They are gaining the ability to speak, listen, see, read, understand, and interact with ever-increasing sophistication. In just the last four years, the error rate in machine-learning–driven image recognition, for example, has fallen dramatically to near zero– practically to human performance levels.

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Machine learning as collaborator

As machine-learning–based skills approach those of human beings, it’s tempting to view their evolution as a zero-sum competition with humans that we are destined to lose.

However, there is another view that says that automation will lead more to collaboration rather than outright replacement. Consulting firm McKinsey & Company argues that while 49% of jobs will be subject to some degree of automation, just 5% will be fully replaced anytime soon. In most cases, says McKinsey, automation will take over specific tasks rather than entire jobs.

McKinsey’s argument is compelling, at least when it comes to knowledge work, because it mirrors the way computing has evolved within the organization. Early mainframes were programmed to perform specific tasks, such as tallying up an organization’s daily receipts. When PCs were first introduced in the 1980s, they were dismissed by businesses as expensive typewriters until packaged spreadsheet software came along, allowing organizations to automate some of their manual accounting tasks at the individual employee level. Knowledge work would never be the same.

Today, most organizations have enterprise software that uses rules-based processing to automate many tasks in departments such as finance and human resources and in warehouses. Yet while the task-based automation of enterprise software has brought tremendous productivity improvements, the software could not learn and improve with experience as humans can.

Until now.

Thanks to advances in computer processing power, memory, storage, and data tools, machine learning can be integrated into the enterprise-software systems that form the heart of most organizational IT infrastructures. This means that the software, using the mastery that it develops in individual tasks, will be able to contribute increasing levels of performance and productivity to the organization over time, rather than merely offering a one-time boost, as most software packages do today.

The strength of machine-learning integration

The improvements the software brings to organizations will not be limited to individual tasks. One of the biggest strengths of enterprise software is its integration– the ability of individual applications to share information and be part of process workflows both within individual departments and across the organization. Integration allows organizations to experiment with new combinations of ever-more intelligent and versatile machine-learning applications and, where possible, let the machines learn how to improve the ways they work with each other and with their human colleagues. Together, these applications form the intelligent enterprise.

Just as individual applications will contribute more productivity to the organization as their embedded machine-learning abilities become more sophisticated, so too will the combinations of those applications evolve to bring more intelligence and flexibility to departmental and organizational processes over time.

Here are some concrete examples of how machine learning is creating value in organizations today:

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Personalized customer service. Organizations can use machine-learning to improve customer service while lowering costs by combining natural-language processing, historical customer service data, and algorithms that continuously learn from interactions. Customers can ask the system questions and get accurate answers, lowering response times and allowing human customer service representatives to focus on higher-priority or more-complex interactions.

Financial-exception handling.
A machine-learning system can be trained to recognize payments that arrive without an order number and match them to invoices based on knowledge of customers’ order and payment histories. This lets organizations reduce the amount of work outsourced to service centers and frees up finance staff to focus on more strategic tasks.

Improved hiring.
A machine-learning system can learn to pluck the most suitable job candidates from the thousands of résumés that organizations receive. It can also spot biased language in job descriptions that might discourage qualified people from applying and rescue other top candidates who fall through the cracks because they don’t fit with traditional hiring models.

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Algorithmic security.
By building models based on historical transactions, social network information, and other external sources of data, machine-learning algorithms can use pattern recognition to automatically spot anomalies. This identification helps detect and prevent fraudulent transactions in real time, even for previously unknown types of fraud. And this type of algorithmic security is applicable to a wide range of other situations, including computer hacking and cybersecurity.

Image-based procurement. Instead of having to log into a procurement system and search manually, employees can simply use a smartphone app to snap a picture of the item they’re looking for– a particular brand and type of laptop, for example– and the system will use machine learning to hunt through its database to find a match or the nearest equivalent. It will then send a message to the employee, who can launch the ordering process with a single click.

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Brand-exposure measurement. Brands spend billions on sponsorships, often without knowing exactly what they are getting for their money. A machine-learning application can sort through thousands of hours of sports video footage or track the action in real time, for example, to tell marketers how often their logo appears on screen, how large it is, how long it appears, and where it is located on the screen. Brands can then quantify their return on investment in the moment.

Contextual concierge.
Let’s say that your flight is suddenly delayed. A travel app on your smartphone can use context-sensitive machine learning to determine how this delay will affect your other travel plans and prompt you with rescheduling options.

Visual shelf management. Employees can take photos of shelves in a store aisle, kicking off a machine-learning process that automatically senses missing or improperly displayed items and prompts the store manager and the warehouse to fill the shelves correctly.

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Manufacturing quality control. By examining video of an assembly line, a machine-learning system can spot defects that a human might miss and automatically reroute the damaged parts or assemblies before products leave the factory.

Drone- and satellite-based inspection. A machine-learning system can sift through thousands of aerial images
of a pipeline, for example, and automatically spot areas that need maintenance or replacement.

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Machine learning needs a platform

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To be sure, organizations will gain tremendous benefits from individual machine-learning applications, even if they are never integrated into a larger whole. However, the benefits become much greater when these applications are on an integrated platform.

The business press has been discussing the power of platforms a lot lately, with iTunes being a well-known example. By creating a set of common software development tools that are available free to anyone who wants them, Apple has enabled developers to create thousands of applications for the iTunes App Store. Developers win because they can easily reach vast numbers of Apple device owners through iTunes. Apple wins because it takes a cut of the revenues for each app it makes available in the App Store.

Platforms are equally important to enterprises, not necessarily because of the profit motive (though some organizations are launching their own public, for-profit platforms similar to iTunes), but because having a platform gives them a base for quickly and cost-effectively combining different applications together, whether they are from different software vendors or are built in house.

No software vendor will ever be able to claim that it offers every machine-learning–enabled application that an organization needs out of the box. But vendors do offer platforms that organizations can use as bases for building out their entire machine-learning infrastructure.

The core of these machine-learning–enabled platforms is application programming interfaces (APIs). APIs are a kind of software version of those universal electric plug adapters that business travelers lug around with them so they can charge their electronic devices wherever they may be in the world. APIs allow software developers to plug into another software vendor’s applications without having to know anything about the complex code at the heart of those applications.

Another benefit of having a unified software platform is that organizations can use it to create a single point of access to data from across the organization. Data is the sole nutrient in a machine-learning diet. Algorithms need to binge on it constantly to lead a healthy and successful life. The larger and richer the data set, the more accurate the results. Having a single platform helps break down the data silos that exist across the organization so that organizations can make the most of machine-learning intelligence.

Organizations don’t need to go it alone

Inevitably, organizations will want to develop machine-learning–based applications that are not available in the marketplace. However, this does not mean that they need to create large internal machine-learning centers of expertise (although having some internal experts is recommended). Service providers can bring the expertise and perspective from within and across industries to help organizations focus on a small set of highly strategic processes that will benefit from machine learning.

The first step toward developing such applications is to determine where to apply machine learning. Organizations need to ensure that it erects barriers to entry against competitors or provides new ways of capturing and retaining customers by improving repurchase cycles or achieving new levels of win rates.

That means focusing investments on the machine-learning problems that will matter most to the industry’s basic competitive economics. Developing those engines will take considerable effort and time, so focusing the enterprise on those one or two projects that will really make a difference matters.

Here are five criteria to determine how to apply machine learning in a way that will create lasting differentiation.

1. The focus area as an appropriate candidate.

Not every facet of business will benefit from machine learning. The greatest potential is in automating high-volume tasks that have complex rules and large amounts of unstructured data.

Is your focus area big and complex enough for machine learning?

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2. A clearly formulated issue. Machine learning works best on specific, well-defined tasks where the desired output and relevant inputs can be clearly stated: given X, predict Y. While it isn’t a magic bullet that will automatically help organizations learn from all the data in their enterprise, machine learning can be valuable in discovering correlations in large amounts of data that humans could never have deduced for themselves.

3. A sufficient quantity of examples to learn from. Machine learning requires a lot of data to be accurate. There must be enough examples for the machine to learn meaningful approximations of the decisions you want to make. This is discovered through experimentation.

4. Meaningful differences within the dataset. If the data you are trying to learn from does not contain meaningful differences, then the algorithm will fail at its mission. Let’s say that you are trying to identify different types of buyers. If the training data does not contain significant differences in buyer characteristics, the algorithm cannot give you useful results.

5. A clear definition of success. Machine learning is always evaluated by measures of performance on a specific task. Typically, the computer will try to optimize whatever performance measure is defined. Clear evaluation criteria for the algorithm are therefore critical. You also need to be certain that the evaluation criteria are actually helpful for solving your business problem.

Key evaluation criteria for machine learning

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The human factor

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Ultimately, the technical barriers to machine-learning adoption will be easier to solve than the human ones. Predictions of steep job losses due to automation are stoking fear and uncertainty about how these self-learning systems will impact our roles and our livelihoods.

These fears must be addressed, and significant investment must be made in change management as business processes and models are reworked to integrate self-learning systems into collaborative human-machine environments.

Indeed, self-learning machines have the potential to become valuable collaborators with humans, augmenting their skills and helping employees become more productive in their current jobs while freeing them from boring, repetitive tasks.

Experts also predict that machine learning will create new roles inside the organization. There is already a shortage of data analysts and those capable of developing the intricate algorithms that machine learning requires. Other new roles will become evident as machine learning integrates deeper into the organization – and not all roles will require a degree in computer science or math. For example, creative thinking, strategy development, quality management, and people development and coaching will be crucial skills in an AI-driven organization, according to a survey by consulting firm Accenture2.

What’s next

When machine learning matures to the point that it can handle unstructured data (still an issue today), when organizations openly share data, and when algorithms begin to interact with each other more freely, machine learning will be embedded in all systems, devices, machines, and software. That will enable highly context-sensitive insight at both the organizational and individual levels. We can only guess at the level of automation that will result, but the impact on business – and society – will be significant.

Already, commercial machine-learning applications based on these technologies are available, and more are being created all the time. That is why business leaders should engage now with trusted providers that can help them evaluate data structures and availability, free up information from siloed systems, and identify the richest areas for machine-fueled insight and improvement. Together, they can address the cultural and change management challenges to take advantage of this new wave of business intelligence.

White Paper Images 10 A Continuous Finance Experience

Download the white paper Why Machine Learning and Why Now?

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Daniel Wellers is Digital Futures Lead, Thought Leadership Marketing, at SAP.

Jeff Woods is Vice President, Marketing Strategy and Head of Thought Leadership Marketing at SAP.

Dirk Jendroska is Head of Machine Learning Strategy and Operations, SAP Innovation Center Network, at SAP.

Christopher Koch is Director, Thought Leadership Marketing, at SAP.


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Employee Experience: The Great Differentiator

The September issue of the Harvard Business Review features a cover story on design thinking’s coming of age. We have been applying design thinking within SAP for the past 10 years, and I’ve witnessed the growth of this human-centered approach to innovation first hand.

Design thinking is, as the HBR piece points out, “the best tool we have for … developing a responsive, flexible organizational culture.”

This means businesses are doing more to learn about their customers by interacting directly with them. We’re seeing this change in our work on d.forum — a community of design thinking champions and “disruptors” from across industries.

Meanwhile, technology is making it possible to know exponentially more about a customer. Businesses can now make increasingly accurate predictions about customers’ needs well into the future. The businesses best able to access and pull insights from this growing volume of data will win. That requires a fundamental change for our own industry; it necessitates a digital transformation.

So, how do we design this digital transformation?

It starts with the customer and an application of design thinking throughout an organization – blending business, technology and human values to generate innovation. Business is already incorporating design thinking, as the HBR cover story shows. We in technology need to do the same.

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Design thinking plays an important role because it helps articulate what the end customer’s experience is going to be like. It helps focus all aspects of the business on understanding and articulating that future experience.

Once an organization is able to do that, the insights from that consumer experience need to be drawn down into the business, with the central question becoming: What does this future customer experience mean for us as an organization? What barriers do we need to remove? Do we need to organize ourselves differently? Does our process need to change – if it does, how? What kind of new technology do we need?

Then an organization must look carefully at roles within itself. What does this knowledge of the end customer’s future experience mean for an individual in human resources, for example, or finance? Those roles can then be viewed as end experiences unto themselves, with organizations applying design thinking to learn about the needs inherent to those roles. They can then change roles to better meet the end customer’s future needs. This end customer-centered approach is what drives change.

This also means design thinking is more important than ever for IT organizations.

We, in the IT industry, have been charged with being responsive to business, using technology to solve the problems business presents. Unfortunately, business sometimes views IT as the organization keeping the lights on. If we make the analogy of a store: business is responsible for the front office, focused on growing the business where consumers directly interact with products and marketing; while the perception is that IT focuses on the back office, keeping servers running and the distribution system humming. The key is to have business and IT align to meet the needs of the front office together.

Remember what I said about the growing availability of consumer data? The business best able to access and learn from that data will win. Those of us in IT organizations have the technology to make that win possible, but the way we are seen and our very nature needs to change if we want to remain relevant to business and participate in crafting the winning strategy.

We need to become more front office and less back office, proving to business that we are innovation partners in technology.

This means, in order to communicate with businesses today, we need to take a design thinking approach. We in IT need to show we have an understanding of the end consumer’s needs and experience, and we must align that knowledge and understanding with technological solutions. When this works — when the front office and back office come together in this way — it can lead to solutions that a company could otherwise never have realized.

There’s different qualities, of course, between front office and back office requirements. The back office is the foundation of a company and requires robustness, stability, and reliability. The front office, on the other hand, moves much more quickly. It is always changing with new product offerings and marketing campaigns. Technology must also show agility, flexibility, and speed. The business needs both functions to survive. This is a challenge for IT organizations, but it is not an impossible shift for us to make.

Here’s the breakdown of our challenge.

1. We need to better understand the real needs of the business.

This means learning more about the experience and needs of the end customer and then translating that information into technological solutions.

2. We need to be involved in more of the strategic discussions of the business.

Use the regular invitations to meetings with business as an opportunity to surface the deeper learning about the end consumer and the technology solutions that business may otherwise not know to ask for or how to implement.

The IT industry overall may not have a track record of operating in this way, but if we are not involved in the strategic direction of companies and shedding light on the future path, we risk not being considered innovation partners for the business.

We must collaborate with business, understand the strategic direction and highlight the technical challenges and opportunities. When we do, IT will become a hybrid organization – able to maintain the back office while capitalizing on the front office’s growing technical needs. We will highlight solutions that business could otherwise have missed, ushering in a digital transformation.

Digital transformation goes beyond just technology; it requires a mindset. See What It Really Means To Be A Digital Organization.

This story originally appeared on SAP Business Trends.

Top image via Shutterstock


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DIY Home Center sets foundation for growth and customer experience innovation with NetSuite unified cloud commerce platform

og image DIY Home Center sets foundation for growth and customer experience innovation with NetSuite unified cloud commerce platform

Online home improvement distributor sees 16% increase in conversion rate and 6% increase in average order value with new commerce platform

SAN MATEO, Calif.—May 31, 2017—Oracle NetSuite Global Business Unit, one of the world’s leading providers of cloud-based financials / ERP, HR, Professional Services Automation (PSA) and omnichannel commerce software suites, today announced that DIY Home Center, an online distributor of decking products and outdoor furniture, has implemented NetSuite’s unified cloud commerce platform to power its B2C and B2B ecommerce, inventory and order management, CRM and ERP.

Servicing homeowners and professional builders, DIY Home Center has experienced double-digit year over year growth since it launched online in 2004. However, its siloed, legacy systems and applications, including Dynacomp’s MOM, QuickBooks and home-grown product information management (PIM) system and ecommerce website, could no longer support its growth. The company was wasting resources making the technology work together, instead of focusing innovating the customer experience and growing the business.

“We had a lightbulb moment when we realized we could have everything on one unified suite without the hairball of disconnected systems to manage,” said Michael Anderson, President at DIY Home Center. “Now, with a modern ecommerce platform natively part of the solution, we can provide a more visual, intuitive shopping experience.”

DIY Home Center first focused on getting its technology foundation in place. It went live on NetSuite in June 2016 to power its back-end applications, including order and inventory management, CRM and ERP. By consolidating fragmented data into a single source of customer, order and product information, the company was able to make informed, timely business decisions and provide more engaging, relevant customer experiences. The launch of its new ecommerce site powered by SuiteCommerce Advanced followed in November and quickly led to the following results:

  • 6 percent increase in average order value.
  • 16 percent increase in conversion rates.
  • 15 percent increase in average time spent on site.

The new webstore, designed by NetSuite Commerce Agency Partner, Intente, provides a modern and engaging experience. Prior to the new site, only a desktop experience was supported. Now, with a responsive design website, 50 percent of the company’s site visitors are using mobile devices.

To further enrich the online experience and drive engagement, DIY Home Center showcases a wealth of educational product content and improvement tips on its product pages, ranging from videos, blogs and tutorials.

To support its B2B buyers, DIY Home Center developed a Preferred Builder Program. Builders get the same rich online experience that individual shoppers get, but in addition, receive product discounts and enhanced account management capabilities to view invoices, make payments, review past orders and easily reorder products. With these account management activities now online, the company sales reps can focus on offering value-add services to their accounts and building brand loyalty.

DIY Home Center has also gained these other benefits with NetSuite’s unified cloud commerce platform:

Single source of product information. As an omnichannel retailer selling on marketplaces including Amazon, eBay and Walmart.com, DIY Home Center uses its product data from NetSuite to publish to these marketplaces, delivering consistent, high quality data.

Real-time inventory management. DIY Home Center is benefiting from just-in-time ordering. NetSuite SuiteCloud Developer Network partner PaceJet has helped streamline the pick, pack and ship process.

Optimized order management. With centralized order management for orders from all channels, DIY Home Center is able to quickly and efficiently process orders. What used to take sales reps four hours to process, now takes less than 30 minutes.

Unified cloud platform. Moving to the cloud has saved on IT costs and removed the hassles of having to manage systems and software. DIY Home Center also benefits from a platform that allows for easy customizations and integrations. The cloud platform provides the scalability and adaptability needed to keep pace with business, especially during the peak summer months.

“NetSuite is the heartbeat of our company,” said Anderson. “Everything starts and ends with it. If we’re looking to add another system our first requirement is that it must connect with NetSuite.”

Experience NetSuite at IRCE 2017
NetSuite is empowering DIY Home Center to transform its business. Merchants attending IRCE 2017 from June 6–9 at McCormick Place in Chicago will have the opportunity to see firsthand how NetSuite’s unified cloud commerce platform is enabling B2C and B2B businesses to provide customers with a seamless shopping experience as well as optimize business operations. To learn more and schedule a personal demo at NetSuite’s booth #701, please visit www.netsuite-irce.com.

About Oracle NetSuite Global Business Unit
Oracle NetSuite Global Business Unit pioneered the Cloud Computing revolution in 1998, establishing the world’s first company dedicated to delivering business applications over the internet. Today, Oracle NetSuite Global Business Unit provides a suite of cloud-based financials / Enterprise Resource Planning (ERP), HR and omnichannel commerce software that runs the business of companies in more than 100 countries. For more information, please visit http://www.netsuite.com.

Follow Oracle NetSuite Global Business Unit’s Cloud blog, Facebook page and @NetSuite Twitter handle for real-time updates.

About Oracle
The Oracle Cloud delivers hundreds of SaaS applications and enterprise-class PaaS and IaaS services to customers in more than 195 countries and territories while processing 55 billion transactions a day. For more information about Oracle (NYSE:ORCL), please visit us at oracle.com.

Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners.

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Engaging New Hires: A Step-by-Step Recipe For A Structured Onboarding Experience

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.

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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.”

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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.

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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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Experience your data with Power BI Germany and meet your compliance and regulatory needs

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Last January, Microsoft announced the availability of Power BI Germany, a powerful SaaS business analytics service delivered through live, interactive dashboards and reports. While this is the same experience that millions of subscribers are enjoying today across the globe, Power BI Germany delivers this through a unique data trustee and data residency model in Germany to address the needs of the most regulated customers in Germany, the European Union (EU), and the European Free Trade Association (EFTA).

Power BI Germany modern BI capabilities are delivered from German datacenters, with customer data stored at rest exclusively in Germany, and strict customer data access and control measures via a unique data trustee model governed under German law. The data trustee, T-Systems International GmbH, an independent German company and subsidiary of Deutsche Telekom, controls all access to customer data by anyone other than the customer and their users. Power BI Germany helps organizations adhere to strict EU data protection regulations and gives them additional choice of how and where their data is processed.

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With Power BI, you can see all of your data through a single pane of glass. Live Power BI dashboards and reports show visualizations and KPIs from data residing both on-premises and in the cloud, providing a consolidated view across your business regardless of where your data lives.

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Power BI also provides two companion applications. The first is Power BI Desktop, a visual data exploration and reporting desktop tool. With Power BI Desktop, you can visually explore your data with a freeform drag-and-drop canvas, a broad range of modern data visualizations, and an easy-to-use report authoring experience for publishing content. There are also native interactive mobile apps for Windows, iOS, and Android, providing secure access and viewing of live Power BI dashboards and reports from any device.

In September 2016 Azure became the first cloud service available from the new Microsoft datacenters in Frankfurt/Main and Magdeburg, Germany. Power BI Germany further increases Microsoft’s investment in Germany. Additionally, Office 365 Germany, is also now generally available from the Microsoft Cloud Germany. Finally, later in the first half of this year, Dynamics 365 will reach general availability in Germany.

You can learn more about Power BI Germany in our FAQ article, including feature parity, useful URLs and admin configuration. You can also sign up for a free 30 day/25 licenses trial today at our Power BI Germany home page.

Other resources

· Website

· Power BI Germany FAQ

· Microsoft Cloud Deutschland (MCD)

· Microsoft Trust Center

· Microsoft National Clouds

· Power BI YouTube Channel

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