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Cybersecurity: It’s More Than Just Technology

Stefan Guertzgen

Last week I visited the ARC Forum in Orlando, and cybersecurity was one of the most prominent topics throughout the whole event. Here are some key lessons I learned:

There are different categories of cyberattacks. On one end are high-frequency attacks perpetuated by attackers with low-level skills. Those typically have a low impact on your company and its operations.

On the other end are less frequent but high-impact attacks that affect critical operations or that target high-value data. Such attacks require a high skill set on the attacker’s side.

How do you protect yourself and your company from both types of attacks?

The first category includes such things as spam, common viruses, or Trojans, most of which you can to fight with technology like spam filters or anti-virus software. However, the boundaries are blurring. The more the attacks move toward the high-impact category, the more you need resources with special skill sets that at least match those of the cyberattackers.

In other words, technology, skilled resources, and executive-level commitment and support must go hand-in-hand to build a resilient cybersecurity and threat protection system.

Sid Snitkin, from ARC, presented a five-stage maturity model comprising the following levels:

  • Secure
  • Defend
  • Contain
  • Manage
  • Anticipate

The higher you climb on this “maturity ladder,” the more skilled resources come into play, and the more you have to break up silos within and beyond your company boundaries. Dan Rosinski, from Dow Chemical, stated that “it takes more than a village” to establish a strong cybersecurity. Fostering collaboration between IT, engineering, operations, legal, safety, purchasing, and business is a critical success factor.

Also, cybersecurity is not a one-off exercise. As hacker’s skill sets grow exponentially, you need to dynamically revisit your strategy and tools. Increasingly, new hardware and software are developed with embedded security and self-protection, especially tools that are used at the perimeter of a company’s environment. Hence, cybersecurity should be considered as a journey that just has started.

Share your experiences and thoughts on cybersecurity with us!

For more insight on cybersecurity technology, see Machine Learning: The New High-Tech Focus For Cybersecurity.

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About Stefan Guertzgen

Dr. Stefan Guertzgen is the Global Director of Industry Solution Marketing for Chemicals at SAP. He is responsible for driving Industry Thought Leadership, Positioning & Messaging and strategic Portfolio Decisions for Chemicals.

How Digital Transformation Is Rewriting Business Models

Ginger Shimp

Everybody knows someone who has a stack of 3½-inch floppies in a desk drawer “just in case we may need them someday.” While that might be amusing, the truth is that relatively few people are confident that they’re making satisfactory progress on their digital journey. The boundaries between the digital and physical worlds continue to blur — with profound implications for the way we do business. Virtually every industry and every enterprise feels the effects of this ongoing digital transformation, whether from its own initiative or due to pressure from competitors.

What is digital transformation? It’s the wholesale reimagining and reinvention of how businesses operate, enabled by today’s advanced technology. Businesses have always changed with the times, but the confluence of technologies such as mobile, cloud, social, and Big Data analytics has accelerated the pace at which today’s businesses are evolving — and the degree to which they transform the way they innovate, operate, and serve customers.

The process of digital transformation began decades ago. Think back to how word processing fundamentally changed the way we write, or how email transformed the way we communicate. However, the scale of transformation currently underway is drastically more significant, with dramatically higher stakes. For some businesses, digital transformation is a disruptive force that leaves them playing catch-up. For others, it opens to door to unparalleled opportunities.

Upending traditional business models

To understand how the businesses that embrace digital transformation can ultimately benefit, it helps to look at the changes in business models currently in process.

Some of the more prominent examples include:

  • A focus on outcome-based models — Open the door to business value to customers as determined by the outcome or impact on the customer’s business.
  • Expansion into new industries and markets — Extend the business’ reach virtually anywhere — beyond strictly defined customer demographics, physical locations, and traditional market segments.
  • Pervasive digitization of products and services — Accelerate the way products and services are conceived, designed, and delivered with no barriers between customers and the businesses that serve them.
  • Ecosystem competition — Create a more compelling value proposition in new markets through connections with other companies to enhance the value available to the customer.
  • Access a shared economy — Realize more value from underutilized sources by extending access to other business entities and customers — with the ability to access the resources of others.
  • Realize value from digital platforms — Monetize the inherent, previously untapped value of customer relationships to improve customer experiences, collaborate more effectively with partners, and drive ongoing innovation in products and services,

In other words, the time-tested assumptions about how to identify customers, develop and market products and services, and manage organizations may no longer apply. Every aspect of business operations — from forecasting demand to sourcing materials to recruiting and training staff to balancing the books — is subject to this wave of reinvention.

The question is not if, but when

These new models aren’t predictions of what could happen. They’re already realities for innovative, fast-moving companies across the globe. In this environment, playing the role of late adopter can put a business at a serious disadvantage. Ready or not, digital transformation is coming — and it’s coming fast.

Is your company ready for this sea of change in business models? At SAP, we’ve helped thousands of organizations embrace digital transformation — and turn the threat of disruption into new opportunities for innovation and growth. We’d relish the opportunity to do the same for you. Our Digital Readiness Assessment can help you see where you are in the journey and map out the next steps you’ll need to take.

Up next I’ll discuss the impact of digital transformation on processes and work. Until then, you can read more on how digital transformation is impacting your industry.

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About Ginger Shimp

With more than 20 years’ experience in marketing, Ginger Shimp has been with SAP since 2004. She has won numerous awards and honors at SAP, including being designated “Top Talent” for two consecutive years. Not only is she a Professional Certified Marketer with the American Marketing Association, but she's also earned her Connoisseur's Certificate in California Reds from the Chicago Wine School. She holds a bachelor's degree in journalism from the University of San Francisco, and an MBA in marketing and managerial economics from the Kellogg Graduate School of Management at Northwestern University. Personally, Ginger is the proud mother of a precocious son and happy wife of one of YouTube's 10 EDU Gurus, Ed Shimp.

How 3D Printing Could Transform The Chemical Industry

Stefan Guertzgen

The history of 3D printing started 30 years ago with Chuck Hull, the Thomas Edison of the 3D printing industry, who introduced the first 3D printer. Since then, 3D printing (also known as additive manufacturing) has been used to create everything from food and other consumer goods to automotive and airplane parts.

Key drivers of adoption

The tremendous growth of 3D printing has been driven by three key factors. First, the cost is rapidly decreasing due to lower raw material costs, stronger competitive pressures, and technological advancements. Second, printing speeds are increasing. For example, last year, startup company Carbon3D printed a palm-sized geodesic sphere in a little more than 6 minutes, which is 25 to 100 times faster than traditional 3D printing solutions. Third, new 3D printers are able to accommodate a wider variety of materials. Driven by innovations within the chemical industry, a broad range of polymers, resins, plasticizers, and other materials are being used to create new 3D products.

While it’s difficult to predict the long-term impact 3D printing will have on the overall economy, it is safe to say that the it could affect almost every industry and the way companies do business. In fact, the chemical industry has already implemented 3D applications in the areas of research and development (R&D) and manufacturing.

Innovative feedstocks and processes

3D printing provides a vast opportunity for the chemical industry to develop innovative feedstock and drive new revenue streams. While more than 3,000 materials are used in conventional component manufacturing, only about 30 are available for 3D printing. To put this into perspective, the market for chemical powder materials is predicted to be more than $630 million annually by 2020.

Plastics and resins, as well as metal powders and ceramic materials, are already in use or under evaluation for printing prototypes, parts of industry assets, or semi-finished goods—particularly those that are complex to produce and that require small batch sizes. Developing the right formulas to create these new materials offers an opportunity for constant innovation within the chemical field, which will likely produce even more materials in the future. For example, Covestro, a developer of polymer technology, is developing a range of filaments, powders, and liquid resins for all common 3D printing methods; 3M, working with its subsidiary Dyneon, recently filed a patent for using fluorinated polymers in 3D printing; and Wacker is testing 3D printing with silicones.

The chemical industry is also in the driver’s seat when it comes to process development. About 20 different processes now exist that share one common characteristic: layered deposition of printer feed. The final product could be generated from melting thermoplastic resins (for example, laser sinter technology or fused deposition modeling) or via (photo) chemical reaction such as stereo-lithography or multi-jet modeling. For both process types, the physical and chemical properties of feed materials are critical success factors for processing and for the quality of the finished product.

New tools and techniques in R&D and operations

Typically, the laboratory equipment used to do chemical synthesis is expensive and complex to use, and it often represents an obstacle in the research progress. With 3D printing, it is now possible to create reliable, robust miniaturized fluidic reactors as “micro-platforms” for organic chemical syntheses and materials processes, printed in few hours with inexpensive materials. Such micro-reactors allow building up target molecules via multi-step synthesis as well as breaking down molecular structures and detecting the building blocks through reagents which could be embedded during the 3D printing process.

Micro-reactors can also be used as small prototypes to simulate manufacturing processes.

In addition to printing equipment used in laboratories, some chemical manufacturers are using 3D printers for maintenance on process plant assets. For example, when an asset fails because of a damaged engine valve, the replacement part can be printed on site and installed in real time. Creating spare parts in-house can significantly reduce inventory costs and wait time for deliveries, hence contributing to increase overall asset uptime.

For companies that do not want to print the parts themselves, an on-demand manufacturing network is available that will print and deliver parts as needed. UPS has introduced a fully distributed manufacturing platform that connects many of its stores with 3D printers. When needed, UPS and its partners print and deliver requested parts to customers.

Commercial benefits

Across all industries, 3D printing promises to reduce costs across the supply chain. For example, the ability to print spare parts on demand can save money through improved asset uptime and more efficient workforce management. 3D printing also helps control costs with reduced waste and a smaller carbon footprint. In contrast to traditional “subtractive” manufacturing techniques in which raw material is removed, 3D printing is an additive process that uses only the amount of material that is needed. This can save significant amounts of raw materials. In the aerospace industry, for example, Airbus estimates 3D printing could reduce its raw material costs by up to 90 percent.

From a manufacturing perspective, 3D printing can streamline processes, accelerate design cycles, and add agility to operations. Printing prototypes on site speeds the R&D development cycle and shortens time to market. Researchers can make, test, and finalize prototypes in days instead of weeks. Also, the ability to print parts or equipment on demand will eliminate expensive inventory holding costs and restocking order requirements and free up floor space for other purposes. In the U.S. alone, manufacturers and trade inventories for all industries were estimated at $1.8 trillion in August 2016, according to the U.S. Census Bureau. Reducing inventory by just 2 percent would be a $36 billion savings.

Barriers to adoption

As with most new technology, barriers must be overcome for this potential to fully be realized. One much-discussed but unresolved issue is intellectual property protection. Similar to the way digital music is shared, 3D printable digital blueprints could be shared illegally and/or unknowingly either within a company or by outside hackers.

In addition to digital files, users can print molds from scanned objects and use them to mass-produce exact replicas that are protected under copyright, trademark, and patent laws. This problem will continue to grow as companies move to an on-demand manufacturing network, requiring digital blueprints to be shared with independent fabricators. This poses a huge threat on companies losing billions of dollars every year in intellectual property globally.

Regulatory issues are slowing the adoption of 3D printer applications. This is especially applicable in the medical and pharmaceutical industries but has potential impact in many markets. For example, globally regulating what individuals will create with access to the Internet and a 3D chemical printer will be difficult. Also, as 3D printing drives small and customer-specific lot sizes, it will likely spur an explosion of proprietary bills of material and recipes, which will be hard to track and control under REACH or REACH-like regulations. Because this is a new frontier, many regulatory issues must be addressed.

In addition to legal and regulatory challenges, the industry has a long way to go in reliably reproducing high-quality products. Until 3D printing can match the speed and quality output requirements of conventional manufacturing processes, it will likely be reserved for prototypes or small-sized lots.

3D printing: a new frontier

While 3D printing has not reached the point of use for large-scale production or to consistently make custom products, ongoing innovations drive high demand. 3D printer market forecasts estimate that shipments of industrial 3D printers will grow by ~400% through 2021 to a value of about $26 billion. Global inventory value is estimated to be over $10 trillion. Reducing global inventory by just 5% would free up $500 billion in capital. Manufacturing overall is estimated to contribute ~16% to the global economy. If 3D printing just would capture 5% of this $12.8 trillion market, it would create a $640 billion+ opportunity.

3D printing will initially help chemical companies increase profitability by lowering costs and improving operational efficiency. However, the industry-changing opportunity is the chance to develop new feeds and formulations. The most successful chemical companies of the future will be the ones with the vision to begin developing and implementing 3D printing solutions today.

Learn more about SAPPHIRE NOW and secure your spot today!

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About Stefan Guertzgen

Dr. Stefan Guertzgen is the Global Director of Industry Solution Marketing for Chemicals at SAP. He is responsible for driving Industry Thought Leadership, Positioning & Messaging and strategic Portfolio Decisions for Chemicals.

Primed: Prompting Customers to Buy

Volker Hildebrand, Sam Yen, and Fawn Fitter

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.

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.

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.

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.

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.

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.

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.

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.

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.”

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.

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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The Big (Data) Problem With Machine Learning

Dan Wellers

Historically, most of the data businesses have analyzed for decision-making has been of the structured variety—easily entered, stored, and queried. In the digital age, that universe of potentially valuable data keeps expanding exponentially. Most of it is unstructured data, coming from a wide variety of sources, from websites to wearable devices. As a recent McKinsey Global Institute report noted: “Much of this newly available data is in the form of clicks, images, text, or signals of various sorts, which is very different than the structured data that can be cleanly placed in rows and columns.”

At the same time, we have entered an era when machine learning can theoretically find patterns in vast amounts of data to enable enterprises to uncover insights that may not have been visible before. Machine learning trains itself on data, and for a time, that data was scarce. Today it is abundant. By 2025, the world will create 180 zettabytes of data per year (up from 4.4 zettabytes in 2013), according to IDC.

Big Data and machine learning would seem to be a perfect match, coming together at just the right time. But it’s not that simple.

The connected world is ever-widening, enabling the capture and storage of more—and more diverse—data sets than ever before. Nearly 5,000 devices are being connected to the Internet every minute today; within ten years, there will be 80 billion devices collecting and transmitting data in the world. Voice, facial recognition, chemical, biological, and 3D-imaging sensors are rapidly advancing. And the computing muscle that will be required to churn through all this data is more readily available today. There’s been a one trillion-fold increase in computing power over the past 60 years.

The importance of data prep

But having vast amounts of data and computing power isn’t enough. For machine learning tools to work, they need to be fed high-quality data, and they must also be guided by highly skilled humans.

It’s the age-old computing axiom writ large: garbage in, garbage out. Data must be clean, scrubbed of anomalies, and free of bias. In addition, it must be structured appropriately for the particular machine-learning tool being used as the required format varies by platform. Preparing data is likely the least sexy but most important part of a data scientist’s job—one that accounts for as much as 50 percent of his or her time, according to some estimates. It’s the unglamorous heavy lifting of advanced analytics, and it takes experience and skill to do it—qualities that are, and will continue to be, in short supply even as demand for data scientists is predicted to grow at double-digit rates for the foreseeable future.

It took one bank 150 people and two years of painstaking work to address all the data quality questions necessary to build an enterprise-wide data lake from which advanced analytics tools might drink. That’s the kind of data wrangling that has to be done before companies can even begin to test the value of machine-learning capabilities.

More data, more problems

There’s also the misperception that having access to all this new data will necessarily lead to greater insight. There’s great enthusiasm around data-driven decision-making and the promise of Big Data and machine learning in boardrooms and executive suites around the world. But in reality, says UC Berkeley professor and machine learning expert Michael I. Jordan, more data increases the likelihood of making spurious connections. “It’s like having billions of monkeys typing. One of them will write Shakespeare,” said Jordan, who noted that Big Data analysis can deliver inferences at certain levels of quality. But, he said, “we have to be clear about what levels of quality. We have to have error bars around all our predictions. That is something that’s missing in much of the current machine learning literature.”

Again, this is where the expertise of the data scientist is of critical value: deciding what questions machine learning might be able to answer, with what data and at what level of quality.

These problems are not insurmountable. Tools are being developed to help businesses deal with some of the data management blocking and tackling that stands in the way of advanced analytics. One company, for example, has developed a machine-learning tool for real estate and finance companies that it says can extract unstructured data in 20 different languages from contracts and other legal documents and transform it into a structured, query-ready format.

What is clear is that the business of combining Big Data and big computing power for new insight is harder than it looks. The benefits almost certainly will be huge. But companies are still at the early stages of experimenting with new data types and emerging machine-learning tools and discovering the drawbacks and complications we will need to work through over time.

This blog is the fifth in a six-part series on machine learning.

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About Dan Wellers

Dan Wellers is the Global Lead of Digital Futures at SAP, which explores how organizations can anticipate the future impact of exponential technologies. Dan has extensive experience in technology marketing and business strategy, plus management, consulting, and sales.