Big Data Privacy Risks And The Role Of The GDPR: Part 2

Evelyne Salie

Part 2 of a 2-part series. Read Part 1.

The European Union’s General Data Protection Regulation (GDPR) is prompting companies to take extra efforts to guarantee the data privacy rights of its business partners, including employees, customers, vendors, and so on.

My last blog discussed the six ways Big Data analytics can threaten personal privacy as well as the two parties that are prompted to take protective actions by the GDPR: individuals and companies with customers in the EU. Individuals can distinguish the risks they are willing to take by asking themselves the following questions:

  • What data am I making publicly available, and where are the potential threats?
  • What risks can I avoid, and on which data do I have no influence?
  • Do I have any right to claim my data? Where can I make that claim?

GDPR sets a base for future development in global data protection and security. As KPMG wrote, “It is fair to say that this new legislation is the biggest and most impactful change in privacy and data protection regulation in history. This regulation came about after more than four years of deliberations and negotiations and will impact organizations worldwide.”

GDPR

Changes ahead

As outlined in EY’s report EU General Data Protection Regulation in the Digital Age: Are You Ready? GDPR requires fundamental changes in how data is processed, stored, and used.

Data protection officers (DPOs)

  • DPOs must be appointed if an organization conducts large-scale systematic monitoring or processes large amounts of sensitive personal data

Accountability: Organizations must prove they are accountable by:

  • Establishing a culture of monitoring, reviewing, and assessing data processing procedures
  • Minimizing data processing and retention
  • Building in safeguards to data processing activities
  • Documenting data processing policies, procedures, and operations that must be made available to the data protection supervisory authority on request

Privacy impact assessments

  • Organizations must undertake privacy impact assessments when conducting risky or large-scale processing of personal data

Consent

  • Consumer consent to process data must be freely given and for specific purposes
  • Customers must be informed of their right to withdraw their consent
  • Consent must be “explicit” in the case of sensitive personal data or trans-border dataflow

Mandatory breach notification

  • Organizations must notify a supervisory authority of data breaches “without undue delay” or within 72 hours, unless the breach is unlikely to be a risk to individuals
  • If there is a high risk to individuals, those individuals must be informed as well

New rights

  • The right to be forgotten – the right to ask data controllers to erase all personal data without undue delay in certain circumstances
  • The right to data portability – where individuals have provided personal data to a service provider, they can require the provider to “port” the data to another provider, provided this is technically feasible

Privacy by design

  • Organizations should design data protection into the development of business processes and new systems
  • Privacy settings are set at a high level by default

Obligations on processors

  • Data processors become an officially regulated entity

Joint controllers

  • Data protection responsibility might split among several controllers

Conclusion

Though responsibility to protect their data does lie on every individual using Internet services (whether online shopping, banking, gaming, or social media), the new EU regulations explicitly require that companies take a more active role in data protection.

Given these changes, the role and importance of information management and governance in data privacy will be a key success factor for all organizations with EU customers.

There are solutions and services available to help you provide protection, availability, resilience, and governance for one of your most important assets – individuals’ data.

For more information, see:

This was originally published on the SAP BusinessObjects Analytics blog and is republished with permission.

Follow SAP Finance online: @SAPFinance (Twitter)|LinkedIn|Facebook|YouTube

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Evelyne Salie

About Evelyne Salie

Evelyne is a highly experienced IT-Solution Principal, Business Developer and Project Manager with over 10 years IT- industry experience within the Governance Risk and Compliance and Finance area of expertise. She currently works as a Senior Director in Business Development at SAP Finance and GRC solutions. In her business development role she is working on concepts and realization for new generation of Finance solutions, running in real time, integrating predictive, Big Data, and mobile, which will change how offices of the CFO work, how the business is run, and how information is consumed.

How Blockchain Revolutionizes Supply Chain Management

Paul Brody

Part 1 in a 3-part series

Nearly all of the world’s leading companies run computerized enterprise resource planning (ERP) and supply chain management software. From connected manufacturing equipment to digital shipping notices and RFID scanning, products are tracked on computerized systems from their earliest origins, often all the way to the recycling bin.

Yet despite this huge investment in digital infrastructure, most companies have only limited visibility and insight into where all their products are at any given moment.

The culprit, in most cases, is the analog gaps that exist between systems within enterprises and across enterprise boundaries. Production may be recorded digitally, but the moment it moves to shipping, a PDF document is created for the shipping label that is little more than a software copy of a printout. The shipment may have its own digital number, but that number tells you where the box is and who signed for it, not what is actually in the box. And so on down the road: oceans of digital data but only islands of useful information.

This is not a new problem, and companies using systems like electronic data interchange (EDI) and XML messaging try to maintain information continuity across system and enterprise boundaries. But point-to-point messaging systems have their own issues, as they are often out of sync and move data only one stop down the supply chain. The result: inventory that seems to be in two places at once.

These systems were created for an era of big, vertically integrated companies with large, but mostly static supply chains. They were very relevant 30 years ago, but not so much today.

The advent of huge, dynamic ecosystems

Two big transformations have swept through global supply chains recently. First, supply chains are no longer traditional networks of OEMs and suppliers. Now they are vast ecosystems, with many product variants moving through multiple parties, all trying to coordinate work together. It’s not uncommon for a single company to have multiple contract manufacturers, all drawing upon a similar supplier network and feeding a range of distribution models, from traditional retail stores to online consignment services.

Secondly, supply chains and operations have become increasingly dynamic. Product lifecycles are shorter, and ramp-up and ramp-down periods are more intense.

Even as supply chains have transformed, companies have not updated the underlying technology for managing them in decades. With blockchain technology, companies can rebuild their approach to supply chain management at the ecosystem level and go from islands of insight to an integrated global view.

Trustworthy truth without trusted intermediaries

Everyone loves to hate middlemen, but it turns out they are really useful. Until the advent of bitcoin and blockchain technology, the only way you could get a large number of entities to agree upon a shared, truthful set of data, such as who has what bank balance, was to appoint an impartial intermediary to process and account for all transactions. Blockchains make it possible for ecosystems of business partners to share and agree upon key pieces of information. But they can do it without having to appoint an intermediary and deal with all the complex negotiations and power plays that come with setting the rules before handing over really critical business information. Instead of having a central intermediary, blockchains synchronize all data and transactions across the network, and each participant verifies the work and calculations of others. This enormous amount of redundancy and crosschecking is why financial solutions like bitcoin are so secure and reliable, even as they synchronize hundreds of thousands of transactions across thousands of network nodes every week.

The core logic of blockchain, applied to the supply chain

Apply that same security and redundancy to something like inventory, and substitute supply chain partners for banking nodes, and you have the foundation for a radically new approach to supply chain management.

The use cases for this new way of working are compelling. At its most basic level, the core logic of blockchains means that no piece of inventory can exist in the same place twice. Move a product from finished goods to in-transit, and that transaction status will be updated for everyone, everywhere, within minutes, with full traceability back to the point of origin.

Do you want to negotiate procurement deals based on total ecosystem volume—not just what you buy from a supplier, but what all your partners do as well? With a blockchain-based solution, you can calculate the exact volume discount based on total purchasing. You can mathematically prove the calculation is correct. And you can do so even while preserving the privacy of each company’s individual volumes.

Promising pilots

The added transparency offers proof about how goods were sourced and how they comply with regulations. The physical, financial, and digital information is brought together in one platform to reveal sources of value leakage—from everyday inefficiencies to fraud and abuse—and helps you hone new strategies to combat them.

Blockchains are still new technology, but the early results EY is seeing in pilots with clients suggest big benefits and the opportunity to recast how we approach these problems, from point-to-point integration to ecosystem-level thinking. We expect to see significant strategic transformations and fairly quick tactical returns as these solutions gain traction. I’ll examine both areas in more detail later in this series.

For more insight on advanced technology’s impact on supply chain management, see How Artificial Intelligence Will Transform Tomorrow’s Digital Supply Chain.

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Paul Brody

About Paul Brody

Paul Brody is global innovation blockchain leader at EY. Paul is responsible for driving EY’s initiatives and investments in blockchain, playing a dual role as global innovation blockchain leader as well the Americas strategy leader for the technology sector. He has extensive experience in the areas of IoT, supply chain, and operations and business strategy.

Standardize, Centralize, And Automate Your Corporate Close: Step 2

Elizabeth Milne

Part 13 in the Continuous Accounting Series

Following on the previous post in this series, let’s take a closer look at the financial consolidation process. With globalization, complex entity structures, increasing intercompany trading relationships across countries and currencies, and new reporting regulations, getting a handle on financial consolidation has attracted renewed interest.

Sheer complexity is a significant issue for most global enterprises. In a recent survey of over 1,000 companies by EY, over 60% had more than 10 legal entities, and nearly half were dealing with more than 10 reporting standards, with over 30% indicating 16 or more different reporting systems. That’s a lot of complexity!

From a corporate reporting standpoint, all of this means time and risk. In the previous post, we discussed best practices to standardize processes for financial reporting and to manage detail and trial balance inflows from local entities to corporate. Financial consolidation, the next step, is about mapping the different GLs to a common, “mezzanine” corporate chart of accounts, managing intercompany eliminations and currency translations, making necessary adjustments, and then producing consolidated reports.

While financial consolidation applications can help handle the mapping process and ensure a strong audit trail, surprisingly (or perhaps not!), many financial consolidation processes still happen in spreadsheets, with complex and brittle VLOOKUPS mapping across accounting structures and currency rates. These aren’t just hard to maintain; they pose a significant risk, especially with changing organizational and accounting structures over time.

Centralizing and automating chart-of-account mappings

An essential part of any consolidation process is mapping accounts from different charts of accounts to the corporate GL, often across different accounting periods. Mappings often vary, from one-to-one account mappings to one-to-many. Further complexity can arise with changing account structures, where new accounts are added at corporate or within entities, requiring new mappings. Moving to a rules-based, centralized approach for mapping accounts is essential to maintain account consolidations across many entities and many different dimensions.

One example is to create rules to automatically aggregate data if multiple subsidiary GL accounts map to a single consolidated account based on the accounting structures of different entities. Older financial consolidation apps can make it painful to change account mappings, while newer technology enables self-service by accountants to update mapping and aggregation rules themselves.

Managing intercompany eliminations

Managing intercompany transactions and eliminating them is often a major driver to move off spreadsheets and automate. New regulations like Base Erosion and Profit Shifting (BEPS) have put a further spotlight on intercompany transactions, increasing the need for a clear, centralized perspective of intercompany processes for tax reporting.

In a nutshell, intercompany transactions are those that happen between entities within the enterprise, whether upstream (from subsidiary to corporate), downstream (from corporate to subsidiary), or horizontally (between subsidiaries). Failure to eliminate them results in an inaccurate view of the enterprise’s financials. Types of intercompany eliminations vary, including eliminating revenue and expenses, intercompany debt, and stock ownership. Additional complexity often arises if a subsidiary is partially owned, which brings the need to allocate to minority and majority interests.

Some of the challenges that can overwhelm corporate accounting teams are the volume of intercompany transactions and a lack of supporting documentation, such as pricing and agreements. The key with intercompany eliminations is to centralize with an audit trail, ideally with the documentation behind each transaction, to ensure substantiation behind consolidated balances. It’s hard to do it with spreadsheets and manual processes alone.

At scale, automation can provide significant efficiencies, with technology like robotic process automation (RPA) that can apply rules to intercompany transactions to automatically eliminate them and raise red flags by exception. Intercompany repositories can act as a clearinghouse for transactional detail.

Managing currency translations

When preparing consolidated financial statements that include a foreign subsidiary, the financial statements of the foreign subsidiary need to be translated into the reporting currency of the parent. One of the biggest problems is that the currency rate used for consolidating an entity can vary, whether dealing with assets and liabilities, income statement items, allocations, different balance sheet dates, or profit eliminations.

Managing currency rates, involving mapping rates based on which date, in turn based on which kind of transaction, can create significant risk as well as manual overhead. You can reduce workload and improve reporting integrity with a single version of the truth for currency rates, using business rules and automation to apply the right currencies in the right situation, and enabling accounting to easily identify why a rate was used when looking at a given balance.

Management, financial reporting, and planning flexibility

My next blog will go into more detail. But financial consolidation doesn’t stand alone. In additional to being essential for financial reporting and ultimately disclosure, it is also the system of record for management reporting, as well as planning and modeling. As such, dimensional flexibility to support both management and financial accounting, performance for ad hoc analysis, and planning and modeling functionality are all essential. Otherwise, data flowing from the financial consolidation system will quickly devolve back into spreadsheets or other separate apps, creating manual or brittle integration points.

The key to rethinking any financial consolidation is automation in detail. Many financial consolidation tasks are detail-oriented and repetitive, such as performing eliminations. Others are highly error-prone, like mapping account structures. With automation, accounting organizations face two poor choices: a fast close that risks errors, or a slow close that screens them out at a substantial resource cost. Using automated rules to process mappings, apply currencies, and look up eliminations provides consistency and helps accounting organizations scale.

Centralizing mappings, intercompany data, and other areas simplify maintenance and provide an audit trail. New cloud applications that handle the processes can be deployed quickly and enable accounting to take control and make changes themselves. There has never been a better time to rethink financial consolidation processes.

Learn how organizations are gaining instant financial insights and using them to make better decisions – both now and in the future. Register now for the 2017 Financial Excellence Forum, Oct. 10-11 in New York City.

Follow SAP Finance online: @SAPFinance (Twitter) | LinkedIn | FacebookYouTube

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Elizabeth Milne

About Elizabeth Milne

Elizabeth Milne has over 20 years of experience improving the software solutions for multi-national, multi-billion dollar organizations. Her finance career began working at Walt Disney, then Warner Bros. in the areas of financial consolidation, budgeting, and financial reporting. She subsequently moved to the software industry and has held positions including implementation consultant and manager, account executive, pre-sales consultant, solution management team at SAP, Business Objects and Cartesis. She graduated with an Executive MBA from Northwestern University’s Kellogg Graduate School of Management. In 2014 she published her first book “Accelerated Financial Closing with SAP.” She currently manages the accounting and financial close portfolio for SAP Product Marketing. You can follow her on twitter @ElizabethEMilne

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.