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The Internet Of Things: An Environmentalist’s Heaven Or Hell?

John Graham

Back in early December, The Guardian ran an article asking whether the Internet of Things will save or sacrifice the environment. As you’d expect, the answer is far from clear. Some environmentalists worry about the effects of producing, installing, and powering those billions of extra devices; others urge the use of IoT sensor networks to help us monitor and curb resource consumption and emissions.

On the surface, the thought of creating huge wireless sensor networks for the benefit of the environment seems paradoxical. However, there is a much bigger picture lurking underneath. The Global e-Sustainability Initiative’s (GeSI) recent #SMARTer2030 report suggests that IoT-related technologies could save “almost 10 times the carbon dioxide emissions that it generates by 2030 through reduced travel, smart buildings, and greater efficiencies in manufacturing and agriculture.”

Even if we achieve a situation in which physical IoT devices have a net positive effect on humanity’s carbon footprint, there is still the massive data transmission and storage growth to consider. Speaking as an executive of a company providing the cloud-based data platform for IoT networks, I can say that it’s in our best interests to keep energy consumption as low as possible, because it costs less. That’s why data centers are built with energy efficiency top of mind.

Ultimately, whether or not the IoT turns out to be an environmentalist’s dream will depend on how we apply its concepts. If it’s primarily used to stream endless high-quality video feeds 24 hours a day or for power-hungry gimmicks and trivialities, the footprint will be far worse than if it’s used directly to get resource and energy management under control. It seems unlikely that the private sector and consumers alone will summon the collective motivation to veer in the direction of the latter, so policy will need to keep up and be sound and assertive.

The attitude of disposability in Western society today is another issue altogether. Perfectly functional year-old smartphones and computers are piling up in landfills across the globe as consumers struggle to resist the lure of the latest model. Can the IoT buck this trend by being founded on sensor networks built to last? With the world trending away from centralized hardware and toward cloud-based software, it could be that upgrades to the virtual aspects of IoT will be enough to satisfy our lust for innovation, while the sensors hum away out of sight and out of mind.

Time will tell.

Register here to listen to an SAP Live webcast in which IBM’s IoT guru Michael Martin discusses the possibilities and challenges of our connected future.

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John Graham

About John Graham

John Graham is president of SAP Canada. Driving growth across SAP’s industry-leading cloud, mobile, and database solutions, he is helping more than 9,500 Canadian customers in 25 industries become best-run businesses.

Devices for the Digital Economy: Frugal Innovation

Danielle Beurteaux

Basic Utilities

Millions of people around the world still lack consistent access to the basics of modern life. They also lack resources to build conventional infrastructure in order to obtain essentials such as water and a consistent supply of electricity. Enter frugal innovation—a process for simplifying complex technologies so they are less expensive to produce and operate. Two startups have devised affordable systems that give people access to essential utilities.

Waterpoint Data Transmitter

About 780 million people, mainly in rural locations, don’t have indoor plumbing. Instead, they rely on hand pumps to access groundwater. Sooner or later, these hand pumps break and often aren’t fixed due to lack of parts and know-how. By some estimates, one-third of pumps aren’t functioning at any given time.

OxWater, a startup launched from Oxford University, has a solution that incorporates basic cell phone technology. The Waterpoint Data Transmitter is a monitoring device that communities deploy to track pump usage. If a pump stops working, a local, trained repair team receives a notification to fix it. The device also provides predictions of which pumps are likely to break and reports low water levels. A pilot project in Kenya showed a dramatic reduction in repair times, from an average of 37 days down to just two.

Quad

Solar power has become an important technology for people living in off-the-grid rural environments. But once the sun goes down, or during spells of cloudy days, the solar panels may not generate enough electricity. That often means a return to inefficient and unsafe solutions, such as kerosene lamps for lighting.

Azuri Technologies has developed a simple, independent system that enables solar users to adapt the amount of power they use according to the amount of energy they generate. The Quad is a small wall-mounted unit that’s wired to a solar panel that comes with a USB port for mobile phone charging. The system uses the company’s HomeSmart technology to monitor local weather patterns and learn consumers’ energy usage. Then, based on available energy, it automatically regulates the amount of power used for lighting (by, for example, adjusting brightness) and battery charging.

A 5-watt system costs about US$156, which users can pay off weekly using a mobile money account. Once they own the unit, they can generate power at no cost. Since its launch in Kenya in 2011, 90,000 Quads have been purchased in 12 African countries.

Digital Rescue

Preventing disasters and delivering aid when they do hit are difficult in isolated locations, where there aren’t enough services that enable quick reaction. Complexity and cost can also keep aid from reaching its targets. These startups are using frugal technology in imaginative ways to issue alerts of impending problems and deliver help to people in need.

Pouncer

Disaster relief is an uphill race against the clock. Whether responding to a natural disaster, war, or famine, aid workers must assemble and deliver supplies, navigate around natural obstacles, avoid thieves, and stay safe. Windhorse Aerospace has developed POUNCER, a disposable drone, to address these problems.

Designed for takeoff from a C-130 Hercules military transport plane and guided using a built-in GPS, POUNCER can be launched from up to 40 kilometers from its destination, with a landing accuracy of within 7 meters. The drone can carry enough food and water rations for 50 people. What’s more, every part is reusable and disposable. For example, the frame, which has a 3-meter wingspan, can be used for shelter or burned for fuel (Windhorse is meanwhile looking to develop an edible frame). Because the entire unit is designed for on-site use, there’s also no cost or peril involved in recovering it from the disaster area.

Lumkani

Many of the world’s poor live in shacks that are built very close together, and they lack electricity. As a result, they rely heavily on open flames for light, heat, and cooking, creating a high risk of fire. But conventional smoke detectors can’t be relied on in places that are already smoky. One devastating fire in Cape Town, South Africa, prompted a group of local university students to design a fire detection device specifically for these environments.

The Lumkani detector is a small wall-mounted unit that runs on batteries and, instead of being triggered by smoke, detects fires by monitoring temperature increases. The detectors use basic radio frequency technology to link all units within a 60-meter radius to a mesh network, which enables early warning alerts for the surrounding inhabitants. The $7 device also stores GPS coordinates, sends warning texts to residents, and can self-monitor the operating health of the whole linked system. Lumkani is working on a way to send real-time data to local emergency response units.

Data at the Digital Frontier

Do you own the land you’re farming? When will the next rainstorm hit? These are basic questions, but for some people living in emerging economies, they’re not so easy to answer. Startups are using clever designs and simple interfaces to provide the information that rural communities need to thrive.

FarmSeal

For millions of small landowners around the world, verifying a legal claim to their land is a complex, expensive, and practically insurmountable process. And without documentation that proves that they own their land, protecting their property rights is nearly impossible, as is getting loans to expand their land holdings and businesses.

Landmapp, based in Amsterdam and operating in Ghana, has developed a mobile platform to make mapping and filing claims accessible to small landowners. The company educates farmers about property rights and then, for a small fee, uses its own platform to record and legally validate land ownership. Landmapp uses geospatial technology and cloud data on a tablet, meaning they don’t need fancy and expensive surveying equipment. FarmSeal, Landmapp’s first product, serves farmers; the company is also launching HomeSeal, for homeowners, and CropSeal, for sharecroppers and landowners. The startup’s platform incorporates local government, legal, and traditional community agreements, and is customizable for different locales.

3D-Printed Weather Stations

Weather data drives numerous economic and public safety decisions. But in many countries, a scarcity of weather stations means no data about vast geographic areas. Unfortunately, conventional weather stations are expensive, costing upwards of $20,000 per unit. In emerging economies, governments and rural communities don’t have the resources or training to buy and maintain them.

At the nonprofit university consortium University Corporation for Academic Research, researchers are leveraging 3D printing to fill the weather gap. They’ve devised a weather station that local government agencies can install in rural communities. The units use off-the-shelf, basic sensors, store data on a small computer, and run on energy generated by a single solar panel. The local agencies have 3D printers to create other parts, including the frame and wind gauges, which can be easily customized or replaced.

The cost? About $300. And beyond letting communities know when, for example, rain is on the horizon, the unit can also be a first alert for natural disasters, like floods.

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

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Climate Change: Look North and South – The Evidence Is Real

Nancy Langmeyer

Explorer Sir Robert Swan – the first and only man to walk on both the North and South Poles unsupported – believes that “the greatest threat to our planet is the belief that someone else will save it.”

As a self-proclaimed survivor, Sir Swan, like many others around the globe, believes that climate change and global warming are very serious issues.

The United Nations (UN) adopted 17 Sustainable Development Goals (SDGs) in 2015, and Goal 13 asks the world to “take urgent action to combat climate change and its impacts.” According to the UN, “Climate change is now affecting every country on every continent. It is disrupting national economies and affecting lives, costing people, communities, and countries dearly today and even more tomorrow.”

The National Aeronautics and Space Administration (NASA) says the rate of temperature increase around the globe has nearly doubled in the last 50 years due to greenhouse gases released as people burn fossil fuels. But even though 2016 was the hottest year in recent history, sadly there are still people in the world who say global warming is of no concern and that it is actually a “hoax!”

Well, like Sir Swan, let’s look to the North and Sole Poles and see what we can learn about the reality of this situation.

The Poles have a story to tell us…

Sir Swan believes the North and South Poles hold vital clues to the issue of global warming and that they are an indication of what is going on around the world in respect to climate change.

In his TED talk, Swan showed pictures of melting ice in the North and South Poles, describing it as a dangerous situation. He says, “We need to listen to what these places tell us, and if we don’t, we’ll end up with our own survival situation here on planet Earth.”

So, let’s start in the North and find out what we can learn there.

At 90⁰ north latitude, the North Pole is 450 miles north of Greenland, in the middle of the Arctic Ocean. There is no actual landmass at the North Pole – only massive amounts of ice that expand in winters and shrink down to half the size in summers.

The climate change story here is that the North Pole has been experiencing unusually high temperatures, reaching 32⁰ Fahrenheit in December 2016, which was 50⁰ warmer than typical! This trend has lead to an alarming shrinkage of the Arctic Sea ice masses that equates to approximately 1.07 million km² of ice loss every decade.

Why is this a problem? Well, according to the National Science Foundation, sea ice variability – the amount of water the ice puts into or pulls out of the ocean and the atmosphere – plays a significant role in climate change. NASA says that, “The sea ice cover of the Arctic Ocean and surrounding seas helps regulate the planet’s temperature, influences the circulation of the atmosphere and ocean, and impacts Arctic communities and ecosystems.”

Even the coldest place on Earth is getting warmer!

Now, in the completely opposite direction, what can we learn from the South Pole and Antarctica? At 90⁰ south latitude, Antarctica, which includes approximately 90% of the ice on the planet, is a little over 300 feet above sea level with an ice sheet on it that is about 9,000 feet thick.

Much colder than the North Pole, the temperature here has dropped to a chilling low of -135.8⁰ Fahrenheit in 2013. However, this pole, too, is experiencing warmer weather, with its highest temperature reaching 63.5⁰ in March 2015.

NASA indicates that Antarctica has been losing about 134 gigatonnes of ice per year since 2002. And just recently, a new concern emerged – a rift in the continent that could send a significant part of the polar cap off into the ocean and create one of the largest icebergs ever recorded. This could, in the long run, raise global sea levels by four inches.

So what’s a little rise in sea level?

While a couple inches here or there doesn’t seem like much, NASA says rising sea levels can erode coasts and cause more coastal flooding, and in fact, some island nations could actually disappear.

And that’s just the sea level. There are other ramifications as the climate changes, such as an increase in infectious diseases with the expansion of tropical temperature zones, more intense rain storms and hurricanes, and many other life-threatening issues.

Let’s be the “someone else”

These insights are just the tip of the iceberg (so to speak) in the story of global warming, but it is evident the Poles are telling us that climate change is real. It’s also evident that it’s time for us as the inhabitants of this world to become the “someone else” Sir Swan talks about. And the good news is that it’s not too late for us to save this planet.

We don’t have to go to the North or South Pole to make an impact. We can simply follow Swan’s advice: “A survivor sees a problem and doesn’t go, ‘Whatever.’ A survivor sees a problem and deals with that problem before it becomes a threat.”

Whether it’s at work with a company like SAP that supports the UN SDGs with its vision and purpose, or individually – we all have to help climate change before there are irreversible threats to our place. Let’s be the someone else, starting today.

A quick note: My last blog focused on how women in the arts and sports are helping to break gender inequality barriers. Well, I am happy to report that this same movement is happening in science too! In 2016, an initial 76 women in science embarked on a leadership journey to increase the awareness of climate science. The inaugural session of the year-long Homeward Bound program, which focused on empowering women in science, culminated in December 2016 with the largest female expedition in Antarctica. Here these brilliant, dedicated female scientists and engineers saw the effects of climate change first-hand and brainstormed how they, through “collaborative leadership, diverse thinking, and creative approaches,” could make an impact. 

SAP’s vision is to help the world run better and improve people’s lives. This is our enduring cause; our higher purpose. Learn more about how we work to achieve our vision and purpose.

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Nancy Langmeyer

About Nancy Langmeyer

Nancy Langmeyer is a freelance writer and marketing consultant. She works with some of the largest technology companies in the world and is a frequent blogger. You'll see some under her name...and then there are others that you won't see. These are ones where Nancy interviews marketing executives and leaders and turns their insights into thought leadership pieces..

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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Dan Wellers

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.