Tetra Pak: Master Data Is A Corporate Asset

Nilly Essaides

In 2014, Jeff DeWolf of Tetra Pak put master data management (MDM) near the top of management’s priority list by treating it as a corporate asset. As the company’s director of global master data strategy, he framed master data within a strong business context. At the same time, he launched a data rejuvenation effort that completely overhauled the giant Swiss packaging company’s MDM model.

Tetra Pak already had a high level of maturity in its management of master data. It has an enterprise-level model and a single ERP instance. But the company was beginning to add new applications that had their own embedded data. Implementations were handled by a small group of individuals. Ownership was incomplete and scattered. Metrics varied. This led, for example, to problems with spare parts pricing, which in turn caused customers to complain. “The problem was we were not treating master data with respect,” DeWolf said.

The right framing

Between 2005 and 2013, the company had made many improvements by focusing on master data management rather than maintenance. But this was not enough to take the next giant leap forward. To do this, DeWolf changed the context of the program by framing it as a business imperative. “When I addressed the C-suite, I didn’t talk to them in terms of data, but in terms of customer realities,” said Dewolf.

For example, when the company developed a new product, it moved it to the ERP environment once it became commercially viable. Initially, there weren’t good checks and balances governing the use of data between the development and commercial environments. The item might have appeared to salespeople as available at a certain price. They would then proceed to quote the price to customers. But parts still in the design phase were constantly updated, leading to pricing changes.

“The result was that customers would keep getting new price confirmations in their inboxes, which customer representatives had to explain. In this case, the lack of clear data governance rules affected the end customer. We were exposing the customer to our own inefficiency,” DeWolf summed up. That got top management attention.

The three objectives

To put MDM back on the map, DeWolf set out three objectives:

  1. Automate the MDM workflow
  2. Remodel the MDM organization
  3. Change the kind of metrics used to measure the quality of the data

His first step was to transition the four master data domains (vendors, finance, customers, and materials) to a single system. To make this happen, he chose master data governance software—an off-the-shelf solution that allows users to centrally create, change, and distribute master data across the organization.

Next he turned to the way the group was organized. Prior to 2013, master data management was distributed among many regional master data teams. He wanted to centralize the expertise in a center of excellence (CoE) and chose Panama for its location, because Tetra Pak already had a global business services (GBS) operation there with some MDM staff. And there were several more in nearby Colombia who were willing to move. He ended up with 20 MDM experts (down from 50) who could work with global process owners around the world.

He says the role of the CoE will evolve overtime. Ultimately, he expects it to become a professional problem-solving operation. “The objective is to find areas of improvements, make suggestions, and feed them to the global process teams,” he notes. “We expect to have 70% of all manual MDM-related work automated by the end of this year.”

The COE has already made a huge impact. It reduced the number of MDM workflows by more than 85 percent, to just 400. Previously, each site had its own change-approval process. While some variations were minor, in the aggregate, the process was so unwieldy that it couldn’t be managed on a global level.

The next step is to reduce the 400 remaining workflows to four. Each will be supported by business logic that will allow customization within a context of standardization that would facilitate global process management.

The CoE is also going to be charged with very little data maintenance and focus more on proactively monitoring data quality and proposing solutions. That’s a role that didn’t exist before. The CoE staff will initiate cleanups based on materiality thresholds. It will then target areas with a potentially significant impact on the business, instead of waiting for problems to pop up.

Finally, DeWolf and his team of MDM experts are working on creating value metrics to replace traditional data-quality measures. For example, one metric DeWolf would like to introduce is linking data about the bills of materials accuracy to data about open sales orders. The idea is to measure the value (in terms of pending sales) that is at risk due to incorrect data. Erroneous bills of materials with open sales orders represent potential missed revenue. The idea here is to use business context to focus on what yields tangible business benefit. This is a non-traditional approach to master data quality.

There is an additional benefit to using value metrics: By translating data fields into value drivers, the MDM team moves the conversation with the business into a language that makes sense to them. If additional resources are needed to stop putting shipments at risk, this is a conversation that suddenly becomes possible.

Next steps

Next on DeWolf’s agenda is master data governance. Right now, the process owners are also the governors of the data, but they don’t have the time to do it. He wants to give the process and organizational structure time to settle in before assembling a data governance council. That piece will happen in 2018-2019. At the same time, Tetra Pak is already pulling in new kinds of data as part of other big data projects, creating data lakes (big reservoirs of varied data types) and considering the possibility of eventually selling data.

Another future goal is to extend the MDM model to other data areas where DeWolf feels the company could benefit, like people, brand, and category data.

DeWolf feels strongly that the project will retain its momentum because it continues to have strong support from Tetra Pak’s senior leadership. “Senior management may not understand the details, but they certainly understand the importance of MDM, and I can get an audience when I need it.” An important factor in maintaining that strong support is presentation style. “Speak with a focus on customer accounts,” DeWolf advises.

For more on how data management can benefit your business, see How To Use The Right Data At The Right Time For Better Customer Relationships.


Nilly Essaides

About Nilly Essaides

Nilly Essaides is senior research director, Finance & EPM Advisory Practice at The Hackett Group. Nilly is a thought leader and frequent speaker and meeting facilitator at industry events, the author of multiple in-depth guides on financial planning & analysis topics, as well as monthly articles and numerous blogs. She was formerly director and practice lead of Financial Planning & Analysis at the Association for Financial Professionals, and managing director at the NeuGroup, where she co-led the company’s successful peer group business. Nilly also co-authored a book about knowledge management and how to transfer best practices with the American Productivity and Quality Center (APQC).

Game Theory: The Nobel Prize-Winning Idea That’s Changing Marketing (And What To Do With It)

Dan Stevens

It’s not immediately obvious what three people sitting around a card table have to do with getting a better understanding of how effective your marketing tactics are. Or how it can help you speak to the people who need persuading, not those who have already been persuaded. But this is the world of collaborative game theory, an idea whose versatility has influenced the way organ donation works, made dating more successful, and helped match foster parents to children in need of families. And now it’s marketing’s turn.

At this point you’d be forgiven if your internal monologue was sounding something like this: “Not another Very Important Big Idea. Please. We’re still trying to work out what to do with the last Very Important Big Idea. If only we could remember what it was…” But game theory itself is not the Very Important Big Idea. It’s the principle that underpins the ability to attribute true value to each touchpoint in the customer journey, from channels to the content you put on them. It’s been around since 1953. Oh, and it won its inventor, Lloyd Shapley, the Nobel Prize for economics. So it has longevity and credibility.

Two’s company, three’s the winning number

Back to the card players. Two players are at the table, playing against the house for money. They’re breaking even, not losing, but not really winning much. A third joins them and together they clean up. Question is, how do you fairly allocate the winnings? This is where collaborative game theory helps. It applies a value, called the Shapley Value after its inventor, to each player based on the value they bring to the game. That value determines what each gets. It’s recognized as the fairest, most equitable and logical way to ascribe value. And we can use this to ascribe value to what we do as marketers.

It’s done using attribution software – SAP Hybris Customer Attribution (formerly known as Abakus), to call it by its proper name – that can measure the true impact of your marketing activities. True impact here means generation of incremental sales, those sales that would have only ever happened because the customer saw an ad or watched a video or read a testimonial. Now it’s possible to know which part of what you do influences and appeals to the incremental prospects. And that, of course, is where the growth lies.

The most promising thing about this prospect is that, unlike last-click and first-click attribution, you can look at the whole customer journey. Each channel can be assessed to see how effective it is at attracting incremental sales and how well it works. For instance, you’re running a campaign across Facebook, YouTube, and Instagram. You can measure the impact of each of these based on the metrics you want to use – engagement, likes, click-throughs – to see if it’s doing what you want it to. Clever, accurate, and true.

It’s about the data AND what you do with it

Your internal monologue will, no doubt, have something to say about this. “Not more data,” it says. “We’ve got so much data we don’t know what to do with, as we have Very Important Big Ideas and Next Big Things. We’re drowning in the stuff. Please don’t make us collect any more. There won’t be any room for actual people in here.”

Okay, okay – having the data and the knowledge is one thing. It’s what you do with the knowledge that will make the difference. And there’s no point in just acquiring the knowledge for the sake of having it. So let’s look at how used attribution software to change the way its business operated. The online retailer, acquired by Walmart just a year after it launched in 2015, stands as one of the fastest-growing and most successful retail launches ever and has established itself at the forefront of innovation. It knew that it wanted to increase new customers and reduce the cost of activating each one. So it looked closely at the true incremental value of its campaigns, publishers, and platforms to build a picture of what attracted new customers and then restructured its marketing plans to do more of it.

With a clear understanding of which campaigns delivered new customers, enjoyed a 23% increase in new customers and a 24% decrease in the cost of activating them. “We are able to better attribute the true credit to each of our marketing initiatives using SAP Hybris Customer Attribution, resulting in more efficient marketing spend and greater scale,” says Micah Moreau,’s senior director of marketing.

This is the application of cooperative game theory to help solve today’s marketing challenges. It’s real, dependable and, most significantly, gives us the most complete picture yet of how effective our activities are. Whereas before, that picture was only partially visible under the dim light of first- and last-click attribution, game theory ups the wattage and reveals what’s really going on. Not bad for three card players around a table.

Want to know more about how SAP Hybris Customer Attribution can help you become more targeted? Click here.


The Best Healthcare System In The World Is About To Change

Komal Mathur

Next year will mark the 70th anniversary of the National Health Service (NHS) in the United Kingdom, deemed the best healthcare system in the world by Commonwealth Fund. Its revered status is justified; the NHS provides consistent, high-quality care for families when they need it most. But these are complex and challenging times for UK’s most trusted and respected social institution.

The five-year forward view, published in October 2014 by NHS England, set out a vision of the biggest integrated care of any major western country. This transformational change is taking the form of “accountable care systems” (ACSs) covering seven million people, as highlighted in the five-year forward view refresh-2017.

ACSs are a way of transforming care and achieving system-wide resilience and efficiency because no single organization can solve current healthcare challenges on its own. They will break down organizational boundaries to streamline services and ultimately improve the experience of patients, caregivers, and citizens.

Strategic priorities of ACSs

Among other things, ACSs will be driving key initiatives in population health and personalized medicine to improve patient outcomes and the quality of their local health economy. Longer-term, by improving the health of the population, there will be reduced demand for health and social care services. ACSs must be able to make informed decisions to improve health outcomes for their population.

ACSs will also drive the direction of medical research in the UK by identifying the gaps. Driving these outcomes will require complete and accurate datasets, harnessed from across organizations in the network. The harnessing of data relies on interoperability of systems and bringing data together so it can be easily analyzed in a secure and private environment.

However, in any ACS geography, providers have chosen Electronic Patient Record (EPR) and other IT systems from multiple vendors. These IT systems do a decent job of cutting waste, eliminating red tape, and reducing the need to repeat expensive medical tests. What ACSs don’t have is the ability to bring together data from these disparate data-rich systems. The result? Patient data has simply moved from one “locker” (paper charts) to another (the cloud). This presents an enormous challenge for ACSs—information sharing is at the heart of their work, and yet the systems are “hiding” that data and hence not providing the necessary evidence and knowledge.

How the right technology platform can help

Simplifying the integration of all structured and unstructured data into an easily accessed, standards-based data warehouse makes it easier for care providers to view, search, and drive more value out of their healthcare data than ever before. It enables the health IT systems to be more efficient in several ways:

  • Consolidates, cleanses, and performs real-time analysis of clinical and genomics data from various source systems on an open, secure platform
  • Gets a holistic view across medical data stored in disconnected silos
  • Achieves faster time to value using a predefined, extensible data model
  • Optimizes patient outcomes through precision medicine and prevention support
  • Captures all hospital data using standard interfaces which can be used to capture new information or perform historic migrations
  • Maintains data quality, e.g. demographic updates
  • Ensures data is stored safely and efficiently and manages onward data migrations, so the EPR doesn’t have to

Where this is working today

The American Society of Clinical Oncology (ASCO) organized massive volumes of data of every cancer patient in the U.S. into usable knowledge via CancerLinq to connect and analyze electronic records to make more informed decisions about patient care. Today patient data from more than 1 million cancer patients is available on the CancerLinq platform. Instead of limiting insights to patients in clinical trials—which takes years to complete—ASCO is learning from nearly every patient as they undergo care and make that information available to experts across the country.

Learn more about SAP Healthcare here.

This story also appeared on the SAP Community.


Komal Mathur

About Komal Mathur

Komal Mathur is a healthcare value engineer at SAP.

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.



Artificial Intelligence: The Future Of Oil And Gas

Anoop Srivastava

Oil prices have fallen dramatically over last few years, forcing some major oil companies to take drastic actions such as layoffs, cutting investments and budgets, and more. Shell, for example, shelved its plan to invest in Qatar, Aramco put on hold its deep-water exploration in the Red Sea, Schlumberger fired a few thousand employees, and the list goes on…

In view of falling oil prices and the resulting squeeze on cash flows, the oil and gas industry has been challenged to adapt and optimize its performance to remain profitable while maintaining a long-term investment and operating outlook. Currently, oil and gas companies find it difficult to maintain the same level of investment in exploration and production as when crude prices were at their peak. Operations in the oil and gas industry today means balancing a dizzying array of trade-offs in the drive for competitive advantage while maximizing return on investment.

The result is a dire need to optimize performance and optimize the cost of production per barrel. Companies have many optimization opportunities once they start using the massive data being generated by oil fields. Oil and gas companies can turn this crisis into an opportunity by leveraging technological innovations like artificial intelligence to build a foundation for long-term success. If volatility in oil prices is the new norm, the push for “value over volume” is the key to success going forward.

Using AI tools, upstream oil and gas companies can shift their approach from production at all costs to producing in context. They will need to do profit and loss management at the well level to optimize the production cost per barrel. To do this, they must integrate all aspects of production management, collect the data for analysis and forecasting, and leverage artificial intelligence to optimize operations.

When remote sensors are connected to wireless networks, data can be collected and centrally analyzed from any location. According to the consulting firm McKinsey, the oil and gas supply chain stands to gain $50 billion in savings and increased profit by adopting AI. As an example, using AI algorithms to more accurately sift through signals and noise in seismic data can decrease dry wellhead development by 10 percent.

How oil and gas can leverage artificial intelligence

1. Planning and forecasting

On a macro scale, deep machine learning can help increase awareness of macroeconomic trends to drive investment decisions in exploration and production. Economic conditions and even weather patterns can be considered to determine where investments should take place as well as intensity of production.

2. Eliminate costly risks in drilling

Drilling is an expensive and risky investment, and applying AI in the operational planning and execution stages can significantly improve well planning, real-time drilling optimization, frictional drag estimation, and well cleaning predictions. Additionally, geoscientists can better assess variables such as the rate of penetration (ROP) improvement, well integrity, operational troubleshooting, drilling equipment condition recognition, real-time drilling risk recognition, and operational decision-making.

When drilling, machine-learning software takes into consideration a plethora of factors, such as seismic vibrations, thermal gradients, and strata permeability, along with more traditional data such as pressure differentials. AI can help optimize drilling operations by driving decisions such as direction and speed in real time, and it can predict failure of equipment such as semi-submersible pumps (ESPs) to reduce unplanned downtime and equipment costs.

3. Well reservoir facility management

Wells, reservoirs, and facility management includes integration of multiple disciplines: reservoir engineering, geology, production technology, petro physics, operations, and seismic interpretation. AI can help to create tools that allow asset teams to build professional understanding and identify opportunities to improve operational performance.

AI techniques can also be applied in other activities such as reservoir characterization, modeling and     field surveillance. Fuzzy logic, artificial neural networks and expert systems are used extensively across the industry to accurately characterize reservoirs in order to attain optimum production level.

Today, AI systems form the backbone of digital oil field (DOF) concepts and implementations. However, there is still great potential for new ways to optimize field development and production costs, prolong field life, and increase the recovery factor.

4. Predictive maintenance

Today, artificial intelligence is taking the industry by storm. AI-powered software and sensor hardware enables us to use very large amounts of data to gain real-time responses on the best future course of action. With predictive analytics and cognitive security, for example, oil and gas companies can operate equipment safely and securely while receiving recommendations on how to avoid future equipment failure or mediate potential security breaches.

5. Oil and gas well surveying and inspections

Drones have been part of the oil and gas industry since 2013, when ConocoPhillips used the Boeing ScanEagle drone in trials in the Chukchi Sea.  In June 2014, the Federal Aviation Administration (FAA) issued the first commercial permit for drone use over United States soil to BP, allowing the company to survey pipelines, roads, and equipment in Prudhoe Bay, Alaska. In January, Sky-Futures completed the first drone inspection in the Gulf of Mexico.

While drones are primarily used in the midstream sector, they can be applied to almost every aspect of the industry, including land surveying and mapping, well and pipeline inspections, and security. Technology is being developed to enable drones to detect early methane leaks. In addition, one day, drones could be used to find oil and gas reservoirs underlying remote uninhabited regions, from the comfort of a warm office.

6. Remote logistics

As logistics to offshore locations is always a challenge, AI-enhanced drones can be used to deliver materials to remote offshore locations.

Current adoption of AI

Chevron is currently using AI to identify new well locations and simulation candidates in California. By using AI software to analyze the company’s large collection of historical well performance data, the company is drilling in better locations and has seen production rise 30% over conventional methods. Chevron is also using predictive models to analyze the performance of thousands of pieces of rotating equipment to detect failures before they occur. By addressing problems before they become critical, Chevron has avoided unplanned shutdowns and lowered repair expenses. Increased production and lower costs have translated to more profit per well.

Future journey

Today’s oil and gas industry has been transformed by two industry downturns in one decade. Although adoption of new hard technology such as directional drilling and hydraulic fracturing (fracking) has helped, the oil and gas industry needs to continue to innovate in today’s low-price market to survive. AI has the potential to differentiate companies that thrive and those that are left behind.

The promise of AI is already being realized in the oil and gas industry. Early adopters are taking advantage of their position  to get a head start on the competition and protect their assets. The industry has always leveraged technology to adapt to change, and early adopters have always benefited the most. As competition in the oil and gas industry continues to heat up, companies cannot afford to be left behind. For those that understand and seize the opportunities inherent in adopting cognitive technologies, the future looks bright.

For more insight on advanced technology in the energy sector, see How Digital Transformation Is Refueling The Energy Industry.


Anoop Srivastava

About Anoop Srivastava

Anoop Srivastava is Senior Director of the Energy and Natural Resources Industries at SAP Value Engineering in Middle East and North Africa. He advises clients on their digital transformation strategies and helps them align their business strategy with IT strategy leveraging digital technology innovations such as the Internet of Things, Big Data, Advanced Analytics, Cloud etc. He has 21+ years of work experience spanning across Oil& Gas Industry, Business Consulting, Industry Value Advisory and Digital Transformation.