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Cathy O’Neil: Unmasking Unconscious Bias in Algorithms

Fawn Fitter

In the wake of the 2008 banking crisis, Cathy O’Neil, a former Barnard College math professor turned hedge fund data scientist, realized that the algorithms she once believed would solve complex problems with pure logic were instead creating them at great speed and scale. Now O’Neil—who goes by mathbabe on her popular blog and 11,000-follower Twitter account—works at bringing to light the dark side of Big Data: mathematical models that operate without transparency, without regulation, and—worst of all—without recourse if they’re wrong. She’s the founder of the Lede Program for Data Journalism at Columbia University, and her bestselling book, Weapons of Math Destruction (Crown, 2016), was long-listed for the 2016 National Book Award.

We asked O’Neil about creating accountability for mathematical models that businesses use to make critical decisions.

Q. If an algorithm applies rules equally across the board, how can the results be biased?

Cathy O’Neil: Algorithms aren’t inherently fair or trustworthy just because they’re mathematical. “Garbage in, garbage out” still holds.

There are many examples: On Wall Street, the mortgage-backed security algorithms failed because they were simply a lie. A program designed to assess teacher performance based only on test results fails because it’s just bad statistics; moreover, there’s much more to learning than testing. A tailored advertising startup I worked for created a system that served ads for things users wanted, but for-profit colleges used that same infrastructure to identify and prey on low-income single mothers who could ill afford useless degrees. Models in the justice system that recommend sentences and predict recidivism tend to be based on terribly biased policing data, particularly arrest records, so their predictions are often racially skewed.

Q. Does bias have to be introduced deliberately for an algorithm to make skewed predictions?

O’Neil: No! Imagine that a company with a history of discriminating against women wants to get more women into the management pipeline and chooses to use a machine-learning algorithm to select potential hires more objectively. They train that algorithm with historical data about successful hires from the last 20 years, and they define successful hires as people they retained for 5 years and promoted at least twice.

They have great intentions. They aren’t trying to be biased; they’re trying to mitigate bias. But if they’re training the algorithm with past data from a time when they treated their female hires in ways that made it impossible for them to meet that specific definition of success, the algorithm will learn to filter women out of the current application pool, which is exactly what they didn’t want.

I’m not criticizing the concept of Big Data. I’m simply cautioning everyone to beware of oversized claims about and blind trust in mathematical models.

Q. What safety nets can business leaders set up to counter bias that might be harmful to their business?

O’Neil: They need to ask questions about, and support processes for, evaluating the algorithms they plan to deploy. As a start, they should demand evidence that an algorithm works as they want it to, and if that evidence isn’t available, they shouldn’t deploy it. Otherwise they’re just automating their problems.

Once an algorithm is in place, organizations need to test whether their data models look fair in real life. For example, the company I mentioned earlier that wants to hire more women into its management pipeline could look at the proportion of women applying for a job before and after deploying the algorithm. If applications drop from 50% women to 25% women, that simple measurement is a sign something might be wrong and requires further checking.

Very few organizations build in processes to assess and improve their algorithms. One that does is Amazon: Every single step of its checkout experience is optimized, and if it suggests a product that I and people like me don’t like, the algorithm notices and stops showing it. It’s a productive feedback loop because Amazon pays attention to whether customers are actually taking the algorithm’s suggestions.

Q. You repeatedly warn about the dangers of using machine learning to codify past mistakes, essentially, “If you do what you’ve always done, you’ll get what you’ve always gotten.” What is the greatest risk companies take when trusting their decision making to data models?

O’Neil: The greatest risk is to trust the data model itself not to expose you to risk, particularly legally actionable risk. Any time you’re considering using an algorithm under regulated conditions, like hiring, promotion, or surveillance, you absolutely must audit it for legality. This seems completely obvious; if it’s illegal to discriminate against people based on certain criteria, for example, you shouldn’t use an algorithm that does so! And yet companies often use discriminatory algorithms because it doesn’t occur to them to ask about it, or they don’t know the right questions to ask, or the vendor or developer hasn’t provided enough visibility into the algorithm for the question to be easily answered.

Q. What are the ramifications for businesses if they persist in believing that data is neutral?

O’Neil: As more evidence comes out that poorly designed algorithms cause problems, I think that people who use them are going to be held accountable for bad outcomes. The era of plausible deniability for the results of using Big Data—that ability to say they were generated without your knowledge—is coming to an end. Right now, algorithm-based decision making is a few miles ahead of lawyers and regulations, but I don’t think that’s going to last. Regulators are already taking steps toward auditing algorithms for illegal properties.

Whenever you use an automated system, it generates a history of its use. If you use an algorithm that’s illegally biased, the evidence will be there in the form of an audit trail. This is a permanent record, and we need to think about our responsibility to ensure it’s working well. D!

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Fawn Fitter

About Fawn Fitter

Ever since discovering the fledgling Internet in the early 1990s, Fawn Fitter has been fascinated by the places where business and technology intersect. She’s spent 15 years in San Francisco, watching the ebbs and flows of the digital economy and writing for magazines, including Entrepreneur and Fortune Small Business.

The Intelligent Supply Chain: A Use Case For Artificial Intelligence

Dr. Ravi Prakash Mathur

The term artificial intelligence (AI) invokes images of robot uprisings, space missions to galaxies far, far away, and lab-created clones that make humans immortal. For years, thought-provoking talks by professors have entertained the notion of whether AI is—or ever will be—self-aware. The more adventurous among us may be drawn toward theosophical discussions on creationism or debates about the realities and influences of the quantum world.

Current thinking about AI may border on science vision (if not science fiction or philosophy)—perhaps for a good reason. Technologies once imagined only on the movie screen now bring convenience and value to our daily lives. Some examples include gestural interfaces, machine-aided purchases, facial recognition, autonomous cars, miniature drones, ubiquitous advertising, and electronic surveillance. Machines are now making predictions on trading stocks, customer purchases, traffic flows, and crime—much as we saw in the 2002 movie “Minority Report.”

From movie screen to real-world applications

Technology leaders have placed big bets on technologies such as brain-computer interfaces, AI in medicine, and deep learning and machine learning tools. AI is expected to lead the new economy, which is becoming known as the Fourth Industrial Revolution or the Second Machine Age. AI is at the forefront of business innovation, along with emerging technologies such as robotics, the Internet of Things3D printingquantum computing and nanotechnology.

Companies are still deciding how AI can be designed to fit into their processes. However, burning questions persist around whether self-learning machines will replace or assist humans in white-collar and blue-collar jobs:

  • Can machines learn common sense and empathy?
  • Who owns the insights that are generated by AI technology, and who owns the responsibility for an erroneous decision made by a machine?
  • Can you teach a machine how to make a decision when dealing with an ethical dilemma?

While these concerns still require much deliberation, most industries understand that the application of AI in businesses brings immense potential. Currently, the top 10 use cases for the technology are data security, personal privacy, financial trading, healthcare, marketing personalization, fraud detection, recommendations, online search, natural language processing (NLP), and smart cars.

Considering how quickly these new technologies are adopted and adapted to new use cases, it is only a matter of time before we start seeing AI capabilities become a part of the fabric of normal business processes. While routine transactions have already been automated, many companies that are higher on the learning curve use predictive and prescriptive analytics to guide their operations.

In the supply chain management function, people talk about degrees of autonomy in the planning process. From use of historical data for planning, it goes through use of automation that can be overridden and ends at nonoptional automation, where planners cannot review the recommendations of the algorithms. The algorithmic supply chain requires organizational maturity and cultural readiness to embed and regularly rely on systems. The concept of an intelligent supply chain goes a step further by incorporating self-learning capabilities of the machine to make better supply-chain decisions.

An opportunity to “learn” and improve–without disruption

Common wisdom tells us that organisations compete on the strength of their supply chain ecosystems. Future organisations would compete on the strength of intelligence embedded in their systems. Ultimately, the winner will be the supply chain that learns most quickly with greatest precision.

At a fundamental level, machine-learning algorithms are a teaching set of data. The machine then answers a question by adding every possible correct or incorrect answer to the teaching set. The algorithm keeps getting better and smarter over time.

Organisations learn in a similar fashion: Every organisation has its own embedded intelligence, which manifests itself through the behavior of its managers and their response to the environment. Supply-chain managers use it to review and modify machine-generated forecasts, production plans, or procurement plans.

Putting a self-learning loop into the system will allow a machine to analyse, for example, why a manual override was made to its recommendation, and it can then check for it during the next cycle. This capability is helpful with managing transactions such as fixing incorrect settings, changing norms, or addressing evolving market dynamics. Over a period of time, machines would learn how managers prioritize their plans based on emerging business scenarios, not just optimization algorithms.

For more on how advanced technology is transforming traditional business models, see Are You Joining The Machine Learning Revolution?

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Dr. Ravi Prakash Mathur

About Dr. Ravi Prakash Mathur

Dr. Ravi Prakash Mathur is Senior Director of Supply Chain Management (SCM) and Head of Logistics and Central Planning at Dr. Reddy’s Laboratories Ltd. He heads the global logistics, central planning, and central sourcing for the pharmaceutical organization. Winner of the 2015 Top 25 Digitalist Thought Leaders of India award from SAP, Dr. Mathur is an author, coach, and supply chain professional with 23 years of experience and is based in Hyderabad. He is also actively involved in academic activities and is an internal trainer for DRL for negotiation skills and SCM. In 2014, he co-authored the book “Quality Assurance in Pharmaceuticals & Operations Management and Industrial Safety” for Dr. B. R. Ambedkar University, Hyderabad. He is also member of The Departmental Visiting Committee (DVC) for Department of Biotechnology, Motilal Nehru National Institute of Technology (MNNIT) Allahabad. Professional recognitions include a citation from World Bank and International Finance Corporation for his contribution to their publication “Doing Business in 2006” and the winner of the Logistics-Week Young Achiever in Supply Chain Award for 2012.

21 Facts On Supply Networks In The Digital Economy

Peter Johnson

Part 6 of the six-part blog series “Facts on the Future of Business

Innovation in the business world is accelerating exponentially, with new disruptive technologies and trends emerging that are fundamentally changing how businesses and the global economy operate. To adapt, thrive, and innovate, we all need to be aware of these evolutionary technologies and trends and understand the opportunities or threats they might present to our organizations, our careers, and society on a whole.

With this in mind, I recently had the opportunity to compile 99 Facts on the Future of Business in the Digital Economy. This presentation includes facts, predictions, and research findings on some of the most impactful technologies and trends that are driving the future of business in the Digital Economy.

To help you more easily find facts for specific topics, I have grouped the facts into six subsets. Below is the sixth of these:

 

New value opportunities

Digital supply chains can reduce supply chain process costs by 50%, reduce procurement costs by 20%, and increase revenue by 10%.

Source: “Digital Supply Chains: A Frontside Flip,” The Center for Global Enterprise.

Companies with 50% or more of their revenues from digital ecosystems achieve 32% higher revenue growth and 27% higher profit margins.

Source: “Thriving in an Increasingly Digital Ecosystem,” MIT Sloan Management Review.

During their operation, the NASA space shuttles cost $60,000 per kilogram to get their payload into low Earth orbit.

Source: “Back to the Moon – Getting There Faster for Less,” National Space Society.

The SpaceX Falcon Heavy will cost an estimated $447 per kilogram to get its payload into low Earth orbit.

Source: “Increasing the Profit Ratio,” The Space Review.

 

Platforms

82% of executives believe platforms will be the glue that brings organizations together in the digital economy. The top 15 public platforms already have a market capitalization of $2.6 trillion.

Source: “Accenture Technology Vision 2016,” Accenture.

By 2020, over 80% of the G500 will be digital services suppliers through Industry Collaborative Cloud (ICC) platforms.

Source: “IDC FutureScape: Worldwide IT Industry 2017 Predictions,” IDC Research Inc.

The world’s biggest banks have taken the first steps to moving onto blockchains, the technology introduced to the world by the virtual currency bitcoin.

Source: “Wall Street Clearinghouse to Adopt Bitcoin Technology,” The New York Times.

By 2027, blockchains could store as much as 10% of global GDP.

Source: “Making The Next Moves With Blockchain,” D!gitalist Magazine.

Africa is leapfrogging the developed world’s traditional banking systems through fast adoption of mobile and Internet-based technology, and is poised to take advantage of the disruptive opportunity that blockchains offer.

Source: “The Blockchain Opportunity, How Crypto-currencies and Tokens Could Scale Disruptive Solutions Across Africa,” BitHub.Africa.

Car sharing could reduce the number of cars needed by 90% in 2035, resulting in only 17% as many cars as there are today.

Source: “Self-Driving Cars Are a Disaster for the Car Industry, but Great for the Rest of Us,” Seeking Alpha.

Millennials are more than twice as willing to car-share as Generation Xers, and five times more likely than baby boomers.

Source: “Cars 2025,” Goldman Sachs.

Airbnb usage is projected to grow 1,165% by 2025, reaching one billion “room nights.” Key growth factors include Airbnb’s high levels of repeat customers, and significant word of mouth, as more than 80% of customers are likely to recommend Airbnb to friends.

Source: “One Wall Street Firm Expects Airbnb to Book a Billion Nights a Year Within a Decade,” Bloomberg.

Once fully available, 5G data speeds will be 1,000-times faster than today. This revolutionary leap will enable ubiquitous connections across the Internet of Things, engagement across virtual environments with only millisecond latency, and whole new Big Data applications and services.

Source: “2017 Predictions: Behind the Scenes with 5G – 2017 Lays Groundwork for Telecom Revolution,” Canadian Wireless Trade Show.

 

Automation and circumventing technologies

Self-driving trucks are hauling iron ore in Australia, convoying across Europe, and appearing on roadways across the globe. And because they offer business savings, self-driving trucks are expected to be more rapidly adopted than self-driving cars.

Source: “Self-Driving Trucks: What’s the Future for America’s 3.5 Million Truckers?” The Guardian.

Amazon uses 30,000 Kiva robots in its global warehouses, which reduces operating expenses by approximately 20%. Bringing robots to its distribution centers that have not yet implemented them, would save Amazon a further $2.5 billion.

Source: “How Amazon Triggered a Robot Arms Race,” Bloomberg Technology.

88% of U.S. consumers say free shipping makes them more likely to shop online, and 79% would select drones as a delivery option if it meant they could receive packages within an hour.

Source: “Reinventing Retail: What Businesses Need to Know for 2016 Whitepaper,” Walker Sands Communications.

Taxi drones will start flying passengers in Dubai in July 2017. Passengers will select destinations on a touch screen and will be able to travel up to 30 minutes at a top speed of around 100 kph.

Source: “Taxi Drones Set for July Launch of Passenger Service Over Dubai,” RT News.

Only 13% of U.S. and Canadian manufacturing jobs recently lost were lost due to international trade. 85% of the job losses stemmed from productivity growth — another way of saying machines replaced human workers.

Source: “Industrial Robots Will Replace Manufacturing Jobs — and That’s a Good Thing,” TechCrunch.

The European Union is proposing new laws that require robots to be equipped with emergency “kill switches” and to be programmed in accordance to Isaac Asimov’s “laws of robotics,” stipulating that robots must never harm a human.

Source: “Europe Calls for Mandatory ‘Kill Switches’ on Robots,” CNN.

By 2030, 25% of Dubai’s buildings will be 3D-printed.

Source: “25% of Dubai’s Buildings Will Be 3D Printed by 2030: Mohammed,” Emirates24|7.

Patients dying while waiting for an organ donor could soon be a thing of the past. By 2030, organs will be biologically 3D-printed on demand.

Source: “Healthcare in 2030: Goodbye Hospital, Hello Home-Spital,” World Economic Forum.

 

To view all of the 99 Facts on the Future of Business in the Digital Economy, check out the Slideshare or other subsets below.

 

To see the rest of the series, check out our page “Facts on the Future of Business” every Thursday, where we will cover these six topics:

  • The value imperative to embrace the digital economy
  • Technologies driving the digital economy
  • Customer experience and marketing in digital economy
  • The future of work in the digital economy
  • Purpose and sustainability in the digital economy
  • Supply networks in the digital economy

 

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Peter Johnson

About Peter Johnson

Peter Johnson is a Senior Director of Marketing Strategy and Thought Leadership at SAP, responsible for developing easy to understand corporate level and cross solution messaging. Peter has proven experience leading innovative programs to accelerate and scale Go-To-Market activities, and drive operational efficiencies at industry leading solution providers and global manufactures respectively.

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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How Artificial Intelligence Will Transform Tomorrow’s Digital Supply Chain

Alina Gross

Artificial intelligence (AI) may sound futuristic, but it’s a real-life breakthrough that exists in the present. Anyone who interacts with an online search engine, shops on Amazon, owns a self-parking car, or talks to voice-powered personal assistants like Siri or Alexa is using AI.

AI is a field of computer science in which a machine is equipped with the ability to mimic the cognitive functions of a human. An AI machine can make decisions or predictions based on its past experiences, or it can respond to entirely new scenarios. When given a goal, not only does it attempt to achieve its objective, it continuously tries to improve upon its past performance.

Revolutionizing the digital supply chain

Within five years, 50% of manufacturing supply chains will be robotically and digitally controlled and able to provide direct-to-consumer and home shipments, according to IDC Manufacturing Insights. Additionally, 47% of supply chain leaders believe AI is disruptive and important with respect to supply chain strategies, per a 2016 SCM World survey. With that in mind, 85% of organizations have already adopted or will adopt AI technology into their supply chains within one year, according to a 2016 Accenture report.

Supply chains need AI to aggregate their mass amounts of data. In the supply chain, AI can analyze large data sets and recommend customer service and operations improvements while supporting better working capital management. As corporate systems become more interconnected, providing access to a wider breadth of supply chain data, the opportunity to leverage AI increases.

Let’s look at the potential benefits of using AI to link transportation data with order data:

A logistics enterprise ensures the delivery of a product within two days. With AI, the carrier can view past performances from shipping a similar product on a specific day, using a particular route, which reveals there’s a 25% chance the order will arrive in four days, not two. This information supplies customer service and supply chain professionals with proactive alerts of potential fulfillment challenges.

To take this a step further, AI could also compare historical shipping data to the customer’s requested delivery date to provide recommendations on whether this particular carrier’s performance meets requirements, or if you need to consider a different logistics enterprise that is 15% more expensive, but 25% more likely to deliver the product on time.

Step by step to a more efficient supply chain with AI

There are many opportunities to use AI throughout the supply chain, from buying raw materials/components and converting them into finished products to selling and delivering items to customers. Supply chains can also use AI to end repetitive manual tasks and begin automating processes. This can enable companies to reallocate time and resources to their core business, and other high-value, judgment-based jobs, by using AI for low-value, high-frequency activities.

In an AI-driven selling platform, chatbots can manage many of the sales, customer service, and operations tasks traditionally handled by humans, including interacting with buyers, taking orders, and passing those orders through the supply chain. In warehouse operations, AI-capable robotics and sensors can enable organizations to enhance stacking and retrieval, order picking, stock-level management, and re-ordering processes.

Amazon is currently combining automation with human labor to increase productivity by using robots that can glide quickly across the floor to rearrange items on shelves into neatly organized rows, or alert human workers when they need to stack the shelves with new products or retrieve goods for packaging. And Logistics company DHL is using AI and automation to create self-sufficient forklifts that understand what products need to be moved, where they need to be moved, and when they need to be moved.

Supply chain companies see a path forward with AI

Leveraging AI is an important next step for supply chain companies looking to lower costs and improve productivity. It can enable your organization to spend less time on repetitive processes, such as planning, monitoring, and coordinating, and focus more on innovation and growth.

AI still needs careful monitoring, however, as well as experienced and knowledgeable logistics and operations professionals to ensure it’s being used to its maximum potential.

For more on how AI and advanced tech can help boost your business, see Next-Gen Technology Separates Digital Leaders From The Rest.

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Alina Gross

About Alina Gross

Alina Gross is currently pursuing her BA in international business at Heilbronn University. She plans on deepening her knowledge by adding an MA in international marketing. During her six-month, full-time internship at SAP, she has focused on marketing and project management topics within the field of supply chain, especially around event management and social media.