The 25 Most Important Customer Experience Questions Answered

Christopher Koch

The Ultimate Customer Experience FAQ

Yes, we’ve done the work so you don’t have to. Our eyes still glowing red from the pain of poring customer experienceover the arcane verbosity of dozens of academic research papers (though a few interesting books helped ease the inflammation; some are noted below), we’ve compiled a list of what we think are the most important questions to ask about the customer experience and, based on our research, come up with the clearest, simplest, and most complete answers to those questions. Please let us know if you agree.

Q. What is customer experience and why does it matter?

Many experts like to say that customer experience is any interaction that customers have with a company. But some interactions matter more than others. The ones that matter the most have a measurable impact on the answers to these two questions:

  • What do your customers think about you?
  • What do your customers do based on their perception of you?

Q. Why these two questions?

Because customer loyalty is the closest thing to a holy grail in customer experience and these two questions represent the two components of customer loyalty: “attitudinal loyalty,” which means having a favorable mental impression of a company, and “behavioral loyalty,” which means that they don’t just like you, they buy from you – and keep buying from you.

Research shows that attitudinal loyalty plays the biggest role in customer loyalty. If customers have a positive emotional outlook towards the customer experience, especially when measured against a competitor, they are more likely to buy from you and become loyal (repeat purchases). Research shows that customers’ evaluations of their experiences mirror the emotions they display during the interactions they have with companies as well as the feelings they experience after the encounter. If those emotions are negative, you can kiss sales and loyalty goodbye.

Q. Isn’t customer experience just another name for customer service?

No. Customer service is just one slice of the customer experience. Customers only contact customer service when they have a problem. As authors Harley Manning and Kerry Bodine put it in their excellent book Outside In, “Equating customer service with customer experience is like saying that a safety net is a trapeze act … If the performer has to use the net then something is wrong with the show.”

Q. So what should be my goals for improving customer experience?

You want them to like you, really like you. A positive attitude toward your company and its products or services has direct ties to customer loyalty and satisfaction. So any efforts that you make to improve customer experience should be considered in terms of how they make customers more satisfied and more loyal. If they are more satisfied with the experience you offer leading up to the sale than competitors, they are more likely to buy from you. If they feel more loyal, they are more likely to buy from you repeatedly.

However, it’s important to keep in mind that satisfaction does not necessarily lead to loyalty. For example, a customer could be satisfied with her experience with you but if a competitor offers something comparable or better she may buy from them next time. Customer experience efforts should drive towards making you customers’ preferred choice. (This distinction between satisfaction and preference is what has helped Frederick Reichheld make millions with his Net Promoter Score methodology.)

Q. But what if my company only sells to a customer once or infrequently? Why should I care about experience and loyalty?

Because even a single positive experience can be expressed in other ways besides repeat purchases. For example, happy customers can give you positive recommendations on websites or in social media or by recommending you to others via word of mouth.

Q. How do I know whether my customer experience needs improvement?

You can’t necessarily trust your customers to tell you. Few will take the time to complain or fill out a survey (especially online); they’ll simply go to a competitor, or worse, social media to complain. Better to ask these questions:

  • Is our market share slipping?
  • Is it costing more to acquire new customers?
  • Are we losing existing customers more rapidly (churn)?
  • Are we getting fewer recommendations and favorable reviews online and in social media?
  • How much pain would our customers have to go through to switch to a competitor (switching costs)?

Getting the answers to these questions will not only help determine the current quality of the customer experience, but will also form the basis of a business case to do something about it.

Q. How much do my actual products and services factor into customer experience?

Less and less, unfortunately. You know the drill: product cycles are getting shorter and automation and globalization have made it much easier for competitors to crank out “good enough” substitutes.

But even for highly complex products and for services, the quality of the customer experience often matters more. Research has found that in some cases, customers would rather buy an inferior (though good enough) product that comes with a superior relationship than a better product that does not.

Q. What are the most important components of the customer experience?

It’s not so much the individual components themselves, such as a Web site or a call center (though those are certainly important); it’s more about whether the individual touch points contribute to creating a positive impression in customers’ minds. Here are the building blocks for creating that impression:

  1. Trust.Thisisthe foundation of a positive customer experience. If customers don’t feel that they can trust the interaction points (say a Web site) or the company behind them, they will be lesslikelytopurchase. Research says that trust consists of two main components:
    • Confidence. Customers must believe that the company has the ability to provide a quality product or service.
    • Benevolence. Customers must believe that the company is willing to consider customers’ self-interest above their own.
  1. Low effort and sacrifice. Customers want their interactions with companies to be free from delays and extra effort. Another issue is the tradeoff between what customers want and what companies are actually capable of providing. In their book The Experience Economy, authors B. Joseph Pine II and James H. Gilmore call this “customer sacrifice.” If the gap between what the customer wants and what the company offers is too great – for example, a cable TV customer has to subscribe to 10 extra channels she doesn’t want to get the one she does want – the experience generates negative emotions.
  1. Positive emotion. Emotion shows up again and again in the research as being the most important factor in the customer relationship. Positive emotions are necessary to build satisfaction and long-term loyalty while negative emotions can destroy in a few moments relationships that companies have invested years in building.
  1. Personalization. Though this is a relatively new and controversial area, research shows that personalizing the customer experience in the right ways creates positive emotion and leads to more satisfaction and loyalty.

Q. How good does my customer experience need to be?

In any relationship with a company, customers expect – or at least hope – that their interactions will require as little effort as possible to get what they want. This means that companies have to make the experience smooth, reliable, and efficient. If, for example, customers are shuttled among three different departments (all asking for their customer numbers) before they can accomplish a typical transaction, then the experience generates negative emotion (frustration is the one that researchers agree is most common) and leads to reduced sales and loyalty.

However, some researchers believe that customers’ perceived effort isn’t just about what they have to do, it’s also tied into how they feel. In their book The Effortless Experience, authors Mathew Dixon, Nick Toman, and Rick Delisi found that only 35% of customers’ perceived effort had to do with exertion; 65% had to do with their emotional reactions during and after the encounter. So easy must go hand in hand with enjoyable.

Q. How do I determine how much I can or should spend to improve customer experience?

If, through competitive analysis and surveys of customers, it’s clear that your customer experience lags behind your competitors then improving customer experience should be considered part of the cost of doing business. Customers can research you and your competitors much more easily now through the web and social media. The holes in your experience will be revealed, causing negative emotion and an exodus to competitors.

Of course, a grocery chain doesn’t have the same profit margins as a luxury hotel chain. However, even companies with limited budgets can try experimenting with small pilots to see how changes in the customer experience impact sales, satisfaction, and loyalty. The percentages of extra revenue, improved loyalty, and increased profits gained from the pilots can help determine the budget for customer experience improvements.

Q. Where should I begin to improve the customer experience?

Removing the bumps in the road that cause customers to expend extra effort is the best place to start. Research by the Corporate Executive Board outlined in the book The Effortless Experience found that moving customers from rating the experience “below expectations” to “meets expectations” gave companies as much economic value as customers who said their expectations were exceeded. So just fixing the existing potholes in the experience will go a long way.

To do this, companies need to look outside by surveying customers about their experiences. Companies also need to look inside by surveying employees (and partners and external providers), about the frustrations they encounter in trying to accomplish their roles in the customer experience.

Q. What role should employees play?

Employee emotions are as important as customer emotions in the customer experience. Employees and managers who feel unable to do their jobs as they perceive they should be done – and feel powerless to change the situation – become unhappy and less able and willing to put out the effort it takes to keep customers happy. Rather than speak up about problems, they simply focus on doing what they are told, what research company Forrester calls a “culture of compliance.”

Yet let’s not be too hard on employees and managers here. An individual employee or manager may not be able to tell where the bumps in the road are. Employees may be happy and feeling confident about their contributions while being completely unaware that they are in fact causing problems further down the line because they are isolated from the rest of the experience process and can’t see the negative impacts.

Q. So how do I identify where the problems are in the customer experience?

Most companies begin by mapping out the customer experience both in terms of how customers interact with the company and the internal processes designed to make the experience flow smoothly. You also have to capture all the processes that happen outside your company, with partners and outsourcers. Having a holistic view can reveal where failures are occurring and form the basis of the case for change.

To create the map, you need your most knowledgeable process participants; ideally, those who have unbroken visibility out to what customers experience, as well as to the internal processes and experiences of employees and managers. Where the line of sight is broken, bring in people who can fill in the gaps. This is best done as a group exercise using the proverbial whiteboard and sticky notes, so that everyone has the opportunity to comment and contribute to determining where the problems are and debunking myths about where people might have thought the problems were, but weren’t.

Of course, this all presumes that the different areas involved in the customer experience in your company are even speaking to one another, much less willing to collaborate on fixing problems. Old habits, old grudges – and old silos – die hard. There should be a high-level executive leading the customer experience change effort, one who is a charismatic convincer (and decider), and who has a direct mandate from the CEO to get everyone to play nice with each other.

Q. What is the role of digital in improving the customer experience?

Digital channels and processes play the most important roles in pursuing the goals of speed and convenience and reducing customer effort. Of course, fixing existing problems with the digital experience (not just for customers but also for employees) is easier said than done because it is expensive and time consuming. Many of the systems that customer service representatives use in call centers, for example, are as old as a graying dad – even a few grandfathers.

Why we are still on hold

And this is the rub. It’s one thing to identify potholes in the customer experience, it’s quite another to fix them. The reason that customers must be put on hold and transferred to different departments and asked for their identification information again and again is usually because the systems that serve these departments were developed in the mainframe era when the concept of integration – and more importantly, the technology to accomplish it – simply did not exist.

Customization has created a nightmare

To make matters worse, companies have layered customization on top of customization over the years to make these systems more able to talk to one another and to company networks, databases, and the internet. They’ve made huge investments just to attain the level of mediocrity we all endure today.

New technologies will help – eventually

The good news is that technology has finally caught up with the customer experience problem. Cloud technologies make application integration easier and in-memory databases have the power to hold massive amounts of information from multiple systems together in real time; that would have seemed like science fiction to mainframe developers of the sixties and seventies.

However, we are still in the early wave of the transformation. Companies remain cautious about discarding old systems that work well in favor of new technologies that are less proven. And though companies have a lot of freedom to make changes in their website experiences, the best Web site is only as good as the data behind it. Customer experience executives would do well to make the CIO their best friend right now.

In the meantime, companies do what they have always done. They pave the potholes in the customer experience with people.

People fill the experience potholes – and pay the price

Companies use people to try to ameliorate the long hold times and the call transfers that stem from having to navigate among different archaic systems and process workarounds. It’s an extremely difficult job and it’s why call center turnover rates are so high.

 Q. What is “emotional labor?”

Most people who work directly with customers these days have been trained to suffer. Researchers have even developed a term for it: “emotional labor.” Studies have shown that employees expend a lot of mental energy in the customer experience, such as having to express happiness when they don’t feel it and having to suppress anger and other inappropriate behaviors when customers treat them abusively.

The toll of this emotional labor can become so high that employees can suffer from researchers call “emotional exhaustion,” which expresses itself in burnout, feelings of low accomplishment, and a kind of emotional numbness in which employees are no longer able to summon the positive attitude and empathy that are so necessary to a successful customer experience.

Q. So does that mean I should be looking for a certain type of person to fill roles in the customer experience?

For those who interact directly with customers, yes. Research says that extroverts do better in customer experience roles because they are more naturally inclined to want to interact with others. But these extroverts should also have the ability to do three things:

  • Regulate inner emotions
  • Tolerate ambiguity
  • Enjoy helping others

In combination, these factors give employees extra endurance when it comes to dealing with people and more ability to suppress inappropriate behavior (even when customers deserve it).

Q. How should I train employees to act during the customer experience?

Even the best employees can burn out if they are forced to adopt what researchers call “surface acting,” in which employees have to put on the proverbial smile and feign emotions that they aren’t feeling during an encounter with customers. Part of the stress is that customers can sometimes detect the falseness of employees’ emotions, which research says causes customers to react negatively.

Instead, companies should focus on training employees to offer two things:

  • Treat customers with empathy. This means hearing customers out and treating them with dignity and respect at every point in the interaction and acting to defuse emotional tension – without having to put on false emotions such as a painted on smile.
  • Offer customers justice. Employees need to get on the same wavelength as the customer to determine what would constitute a just outcome for the experience in the customer’s mind and then weigh that against the limits the company has set on the experience and come to a mutually agreed upon resolution.

In part, this depends on the degree to which employees are allowed to exercise their own independence and judgment. But it also depends on the preset outcomes that the company builds around the experience. For example, are customer service representatives given the freedom to send a replacement product for one that is one month – or one year – past warranty? Companies need to constantly revisit these outcomes to maintain a good balance between giving employees the power to give customers experiences that lead to positive emotions while not breaking the bank.

Q. To what extent should I try to replace the human customer experience with a digital one?

There’s clear evidence that digital contributes a lot to making the experience easy and fast, especially in transactional types of relationships such as buying a book on Amazon, which customers like. Digital is also great for information-intensive experiences, such as complex products and services that require customers to do a lot of research before buying. And of course, digital experiences are much cheaper for companies, though most surveys show that companies do a poor job of managing them — especially when it comes to coordinating across digital and human channels.

Indeed, a good customer experience can rarely be completely online or offline. Increasingly, it’s the coordination of the two that matters most.

Digital may be great for easy, but we still need humans for when things become hard – such as when that product that was so easy to order online breaks offline. Research shows that customers place a high value on the quality of the relationship they have with companies. In that regard, there is no replacement for human-to-human interaction – at least not until virtual reality hits the mainstream (which could be sooner than you think). Until then, careful placement of a pleasurable human interaction into the customer experience when competitors are trying to pave everything over with digital can have a major impact.

An example is ING, the Dutch bank that entered the U.S. market in the nineties with an online experience only – no branches. But in 2001, the company decided to create a human experience, not with a traditional bank branch but with a café in New York City. Instead of serving up deposit slips, employees serve coffee, treats (sales of which help defray the costs of running the offices), and financial planning advice. The original café was a big hit and ING (whose U.S. online business was purchased by Capital One for $9 billion in 2012) began building cafes in major metro areas around the country. Capital One has continued the expansion plan while other banks have been shutting down branches or imitating the approach.

Q. What if I don’t have the resources for a “delight-the-customer” approach to customer experience?

Companies that invest in delighting the customer without first making sure they are at least meeting the expectations of the vast majority of customers are probably wasting their money. Getting a free gift card for a restaurant is meaningless if the food and service aren’t so hot to begin with.

Plus, giving stuff away or sending your employees out on time-consuming missions to bring smiles to customers’ faces is expensive – 10-20% more, according to executives surveyed by authors Mathew Dixon, Nick Toman, and Rick Delisi in The Effortless Experience.

The first priorities should be to drive down customer effort and sacrifice.

However, delighting the customer does not necessarily need to be focused on going above and beyond the call of duty. There are less expensive ways to do it (see the “customer experience as theatre” examples below).

Q. Okay, let’s say customers say the experience is easy and fast. Is that really enough to build loyalty over the long term?

Given the current sorry state of the customer experience in most industries, yes.

But let’s assume for a moment that you are the world’s master of easy, fast, reliable, and convenient. What happens when a competitor catches up? What’s next?

Some researchers argue that there are two other ways to differentiate your customer experience that are harder for competitors to match:

  1. Customer experience as theatre
  2. Personalization

Q. What is customer experience as theatre?

Author B. Joseph Pine II says that Best Buy’s Geek Squad has taken the classic military motif of the uniform a step farther by adding a dose of humor and humility. The Geek Squad purposely dresses its employees in an outfit that still gets nerds hung from their underwear in gym lockers around the world: white button-down shirts, thin, clip-on black ties, black pants, and white socks.

Each employee also gets a titanium badge designed to look just like a policeman’s badge. The geek squad drives Volkswagen Beetles painted black and white to look like extremely awkward and ineffective police cars. It is the nerd as the anti-hero hero, here to save the day for you and your computer.

Theatre need not cost much

Through this minimalist and, Pine is careful to point out, inexpensive, bit of theatre, the Geek Squad, which Best Buy bought when it was a tiny startup, has grown exponentially and become a household brand name. Is it because of the name and the uniforms, or is it simply because the Squad offers better service and is tied to Best Buy, which has long been a household brand? Impossible to tell, but once again, the clip-ons haven’t hurt.

Pine, who is co-author, with James H. Gilmore, of The Experience Economy, believes that any company, with enough creativity and a good employee screening and training program, can create the same kind of differentiated experience. “Whenever employees are in front of your customers, those employees are acting,” says Pine. “They need to act in a way that engages the audience. And it does not require any expenditure. It requires that you direct your workers to act, that you give them roles to play and you help them characterize those goals on the business stage.”

Though he acknowledges that he has nothing beyond anecdotal evidence to back up his theory, Pine argues that performance is a way to stand apart in a crowded field and create customer preference rather than mere satisfaction.

Q. What about personalization? Can that create a differentiated customer experience?

Personalization is controversial but holds promise because it can be another form of easy. Though we usually think of customization as adding more, it can also mean simplifying the experience by removing everything except what the customer truly wants. This is particularly true on the web, where websites and e-commerce portals overstuffed with offers and information can trip that magic switch of frustration that kills sales and loyalty.

Sweeping away the noise and personalizing a Web site to a customer’s tastes – we will look back on Amazon’s recommendation engine as the stone-age prototype for this sort of thing – can reduce customer effort and sacrifice. Research has shown that offering relevant information and simplifying the experience result in more customer trust and satisfaction and more sales.

More importantly, as databases become ever-more fast and powerful, we can add a powerful new aspect to the digital customer experience: learning. At the core of recommendation engines like Amazon’s and Netflix is machine learning – the ability to memorize your actions and preferences and use algorithms to serve up personalized offers.

However, personalization treads on the same dangerous turf as efforts to “delight” the customer. It can be complex and expensive. And if it only leads to satisfaction rather than positive preference for the brand, that money may be wasted. Indeed, one research study found that for customers that were already satisfied with their experience, personalization had limited benefits. Only in instances where the customer had a high degree of trust in the company but low levels of satisfaction did personalization make a significant difference. Therefore, it’s best to start with a pilot project to see if personalization will make a difference before investing too much.

Q. Personalization also raises issues of privacy, right?

In order to make recommendations and personalize web pages, companies need to gather information. And as we’ve all learned, various companies and governments have stepped all over people’s privacy in order to gather data about them.

Businesses need to build a customer experience model that helps individuals understand the data that companies want to collect about them, the methods the companies will use to make behavioral predictions, and the trustworthiness they can expect from those predictions.

Here are five ways to use Big Data to be cool, not creepy.

  • Articulate “What’s in it for me?” Research has found that the majority of consumers in the United States and the United Kingdom are willing to have trusted retailers use some of their personal data in order to present personalized and targeted products, services, recommendations, and offers. But the value has to be crystal clear, no matter who’s tracking the data. For example, insurance provider Progressive and Tesla Motors have convinced car owners to have devices installed in their vehicles that track where and how they drive. In exchange, customers potentially get lower car insurance rates (an average 10% to 15% reduction on premiums) or improved service, such as supercharger stations near their most frequent routes.
  • Be transparent about the data relationship. Slapping a dense data use policy written in legalese on the corporate Web site does little to enlighten customers. Instead, companies should think about the customer data transaction – what information the customer is giving them, how they’re using it, and what the result will be – and try to describe it as simply as possible.
  • Let customers learn about each other. In 2011, Procter & Gamble created a “Mean Stinks” campaign for Secret deodorant that encouraged girl-to-girl anti-bullying posts on Twitter, Facebook, and Instagram. The pages let participants send apologies to those they had bullied; view videos; and share tips, tools, and challenges with their peers. Besides helping girls, it drove a 16% market share increase in the Secret deodorant line.
  • Experiment and build trust. Building a Big Data strategy that improves customer experience takes time and continual tweaking. Google’s Autocomplete isn’t always on point. Amazon’s suggestions sometimes go astray. But as customers build up a history of experience with a brand, they see that data is used for their benefit more often than not. They develop a trust in the exchange of data for value. They see where it came from. And they forgive the missteps.
  • Make the distinction between little data and Big Data. “I steer companies to really focus on leveraging the data that customers give them in the normal process of doing business first and think about the third-party stuff later,” says Elea McDonnell Feit, assistant professor of Marketing at Drexel University. “At least 80% of the value you can generate from customer data comes from using the information customers reveal about themselves directly to you.”

Q. Do some customers deserve more personalization than others?

Given that personalization can be complex and expensive, it could pay to segment customers into those most likely to respond to personalization.

Some companies have created composite personas of customers to do more broad-brush personalization that doesn’t cost as much as one-to-one efforts. For example, you could create a set of categories across the customer base based on past purchase history and other data and create separate customer experiences for each category.

Q. How do I determine the ROI of customer experience improvements?

Unfortunately for customers (and in the long run, companies, too), there’s really only one measure that matters: switching costs. If there are no viable alternatives in the market, or if switching to a competitor would cost more than the product or service itself or involve so much customer effort that it doesn’t seem worth it, customer experience becomes less important to revenues. Research shows that customers who perceive high switching costs are more likely to stick with a company that provides a less-than-stellar (but acceptable – again anger and conflict trump all other factors) experience, thereby reducing the potential returns from investing in improving that experience.

But even companies with high switching costs or lack of competition neglect customer experience at their peril. Cable companies, for example, are feeling the pain today as disruptive pay-per-view entertainment options such as Hulu, Netflix, and Amazon Prime lure away cable customers who have long wished for an alternative but have had no other choices – until now.

Loyalty over time matters

But let’s assume that you are in a competitive industry. The most important impact that a good customer experience has is in customer loyalty. Because it costs more to acquire new customers than to maintain relationships with existing customers, most experts point to loyalty as the decisive metric. More specifically, they cite lifetime customer value – usually computed as the revenue from each customer over the length of the relationship.

Author Frederick F. Reichheld puts a finer point on the metric in his book The Loyalty Effect, saying that companies should measure the lifetime profit per customer minus the cost of acquiring them in the first place. The problem here is that few companies even measure revenue per customer over time, much less take it to Reichheld’s ideal level. And not all researchers agree with Reichheld that profits matter more than revenues.

Three other metrics to consider

In their book Outside In, authors Manning and Bodine modeled three areas where companies can benefit from improved customer experience that are slightly easier to measure :

  • More incremental purchases from existing customers
  • Higher retained revenue as a result of reduced churn
  • New sales driven by word of mouth

They found that in the hotel and wireless industries, small improvements in customer loyalty led to major gains – in the billions – in revenue, because competition in those industries is so intense and switching costs are so low. However, even in less volatile industries where switching costs are higher, such as health insurance, Manning and Bodine saw opportunities to gain revenue in the tens of millions by improving the customer experience.


About Christopher Koch

Christopher Koch is the Editorial Director of the SAP Center for Business Insight. He is an experienced publishing professional, researcher, editor, and writer in business, technology, and B2B marketing. Share your thoughts with Chris on Twitter @Ckochster.

Cognitive Technologies Help Media Companies Build Consumer Loyalty

Catherine Lynch

Media companies need to provide unique, personalized content, driven by deep insights into individual consumer preferences, due to the growing popularity of over-the-top (OTT) streaming services. In the past, media companies were not in direct contact with consumers and interacted in a mass marketing fashion. Now the business model is changing to direct to consumer, and media companies need to adapt to survive and thrive.

Consumers are willing to pay for the right content

In the music world, interactive personalized streaming of music (Spotify, Apple Music, Deezer…) is overtaking physical downloads of music from a revenue perspective, and it is even rumored that Apple will stop downloads from iTunes next year. In 2017 streaming accounted for almost two-thirds of music industry revenue. By the end of this year, over half (57%) of Spotify’s 157 million worldwide active users will be paying for subscriptions.

Over 30% of U.S. households now subscribe to more than one OTT service, according to Parks Associates. The OTT video service industry is expected to reach $30 billion by 2020.

Using analytics and identity management to suggest relevant content to consumers

To understand what a viewer will like in six months, media companies must manage the complexity of multiple touchpoints, both physical and digital. It is also essential to build consumer trust and loyalty if you are seeking personal information from a viewer to drive that personalized experience. Algorithms underpinned by cognitive technologies help determine which content might interest a subscriber. Identity management software enables the buildup of a profile of preferences and leads to greater personalization and consumer loyalty.

Media companies can also analyze social activity information about a viewer to further increase levels of personalization, and it makes sense to provide that viewer with a personalized subscription offer and “up-sell” based on that person’s video consumption and social media activity.

Machine learning and blockchain help with personalized ads and content monetization

Software such as Pippa developed a technology that allows podcasters to insert personalized ads to a podcast. It is planning to use AI to perform deep audio search and personalize ads based on a podcast’s content. With a new avenue for monetization of podcasts, this technology could boost podcasting and make it much more profitable. Jaak uses blockchain technology to identify the usage and rights to song streams. It enables apps and platforms to identify who is streaming a song and when identifying the multiple rights holders and assigning corresponding payments.

By 2020, Gartner predicts that artificial intelligence (AI) bots, rather than humans, will manage 85 percent of customer interactions. There will be more than 82 million U.S. millennial digital video customers. As media companies grapple with the challenge of getting personalized content to the consumer at the right time, companies that proactively invest in advanced analytics, machine learning, and blockchain will gain a critical first-mover advantage.

To learn more, read our Reimagining Media in the Digital Age blog series.

Are you attending SAPPHIRE? If so, join us at the SAP Industries Experience Area during the event and check out the Media sessions on the agenda builder.


Catherine Lynch

About Catherine Lynch

Catherine Lynch is a Senior Director of Industry Cloud Marketing at SAP. She is a content marketing specialist with a particular focus on the professional services and media industries globally. Catherine has a wide international experience of working with enterprise application vendors in global roles, creating thought leadership and is a social media practitioner.

How To Best Use Data To Reach Your Customer Anywhere

Derek Klobucher

Declarations of the retail apocalypse for brick-and-mortar stores are more than overblown; they’re downright wrong, according to experts at a recent conference – and they’ve got data to back them up.

About 90% of retail purchases occurred in-store, according to a U.S. Census Bureau study last year. And a savvy use of data can help retailers deliver a personalized customer experience – no matter where people decide to shop.

“We should treat you as an individual, not generally, and we should know and be able to respond with what would appeal to you right off the bat,” recently retired Macy’s chairman and CEO Terry Lundgren said during a video interview with SAP at the Global Retailing Conference 2018 in April. “That’s all done through machine learning and repetition from customers … [with] shopping habits like you.”

Engaging individuals more effectively with machine learning

Machine learning is also helping retailers create more effective platforms that can distinguish between individual shoppers, according to Lundgren. Store websites, for example, could quickly display the most appropriate products for each person within one or two pages.

“There’s tremendous value in using technology and data to improve efficiency,” Lundgren said. “And machine learning is helping us become more intelligent.”

But the rise of omnichannel retailing could spell big trouble for those who focus only on e-commerce.

“There’s tremendous value in using technology and data to improve efficiency … and machine learning is helping us become more intelligent,” Lundgren said.

The e-commerce ceiling

“There’s an absolute ceiling for e-commerce,” MasterCard senior VP for market insights Sarah Quinlan said at GRC 2018. “If you have a separate marketing department for your online versus your in-store, that’s a real mistake.”

E-commerce will continue to grow, but it’s unlikely to overtake in-store shopping because we’re social creatures who crave a person-to-person customer experience (CX), according to Quinlan, whose team at MasterCard routinely analyzes massive volumes of consumer data. This is especially true after the Great Recession, which showed consumers that jobs, companies, and capital can be fleeting – but experiences with loyal family and friends are priceless.

“That is what drives their spending,” Quinlan said. “We are not going to stop traveling; we are not going to stop dining out together; we are not going to stop that whole social side.”

The best use of your data

“Think about how to collect that as much as possible, but not just for the sake of collecting data – think about how you’re going to utilize it,” Alilbaba Group VP for North America Lee McCabe said at GRC 2018. “Think like a tech company … [that] means you have a test-and-learn mentality.”

Most retailers wouldn’t survive a tech brawl with the likes of Amazon or Facebook, but they can still learn from them – and even partner with them, according to McCabe. Test everything – including what’s been successful for your organization over the past six months because it might not work over the next six months. If you’re experimenting with a new website, AB test everything on it.

“It’s very rare when you see big innovation, especially when comes to e-commerce,” McCabe said. “It’s the thousands of small ones you should be thinking about – how you can improve conversion by 0.001% by just changing one little thing.

“Doing that on a daily basis is how tech companies think.”

Don’t miss out on the consumer

“Artificial intelligence (AI) opens up a big opportunity to predict the purchasing behavior of in-store customers,” The Financial Express stated. “AI, through its sub-technologies such as machine learning and deep learning, can enable offline retailers to derive actionable insights from consumer data … to offer predictive and precise decisions for better customer experience.”

Data can also help retailers keep their focus on what’s really happening, as opposed to the mythical retail apocalypse, according to Macy’s Lundgren.

“So 90% of the transactions still take place in a brick-and-mortar – that’s going to go to 89, it’s going to go to 88,” Lundgren said during his GRC 2018 keynote. “It’s going to change, but if we don’t focus on how consumers are really shopping – if we get bogged down in believing that everybody shops online all of the time – we’re going to miss out on the consumer.”

For more on digital disruption in the retail industry, see The New Retail Reality: Moving Beyond Sales.

This story originally appeared on SAP Innovation Spotlight. Follow Derek on Twitter@DKlobucher


Derek Klobucher

About Derek Klobucher

Derek Klobucher is a Financial Services Writer and Editor for Sybase, an SAP Company. He has covered the exchanges in Chicago, European regulation in Dublin and banking legislation in Washington, D.C. He is a graduate of the University of Michigan in Ann Arbor and Northwestern University in Evanston.

The Human Angle

By Jenny Dearborn, David Judge, Tom Raftery, and Neal Ungerleider

In a future teeming with robots and artificial intelligence, humans seem to be on the verge of being crowded out. But in reality the opposite is true.

To be successful, organizations need to become more human than ever.

Organizations that focus only on automation will automate away their competitive edge. The most successful will focus instead on skills that set them apart and that can’t be duplicated by AI or machine learning. Those skills can be summed up in one word: humanness.

You can see it in the numbers. According to David J. Deming of the Harvard Kennedy School, demand for jobs that require social skills has risen nearly 12 percentage points since 1980, while less-social jobs, such as computer coding, have declined by a little over 3 percentage points.

AI is in its infancy, which means that it cannot yet come close to duplicating our most human skills. Stefan van Duin and Naser Bakhshi, consultants at professional services company Deloitte, break down artificial intelligence into two types: narrow and general. Narrow AI is good at specific tasks, such as playing chess or identifying facial expressions. General AI, which can learn and solve complex, multifaceted problems the way a human being does, exists today only in the minds of futurists.

The only thing narrow artificial intelligence can do is automate. It can’t empathize. It can’t collaborate. It can’t innovate. Those abilities, if they ever come, are still a long way off. In the meantime, AI’s biggest value is in augmentation. When human beings work with AI tools, the process results in a sort of augmented intelligence. This augmented intelligence outperforms the work of either human beings or AI software tools on their own.

AI-powered tools will be the partners that free employees and management to tackle higher-level challenges.

Those challenges will, by default, be more human and social in nature because many rote, repetitive tasks will be automated away. Companies will find that developing fundamental human skills, such as critical thinking and problem solving, within the organization will take on a new importance. These skills can’t be automated and they won’t become process steps for algorithms anytime soon.

In a world where technology change is constant and unpredictable, those organizations that make the fullest use of uniquely human skills will win. These skills will be used in collaboration with both other humans and AI-fueled software and hardware tools. The degree of humanness an organization possesses will become a competitive advantage.

This means that today’s companies must think about hiring, training, and leading differently. Most of today’s corporate training programs focus on imparting specific knowledge that will likely become obsolete over time.

Instead of hiring for portfolios of specific subject knowledge, organizations should instead hire—and train—for more foundational skills, whose value can’t erode away as easily.

Recently, educational consulting firm Hanover Research looked at high-growth occupations identified by the U.S. Bureau of Labor Statistics and determined the core skills required in each of them based on a database that it had developed. The most valuable skills were active listening, speaking, and critical thinking—giving lie to the dismissive term soft skills. They’re not soft; they’re human.


This doesn’t mean that STEM skills won’t be important in the future. But organizations will find that their most valuable employees are those with both math and social skills.

That’s because technical skills will become more perishable as AI shifts the pace of technology change from linear to exponential. Employees will require constant retraining over time. For example, roughly half of the subject knowledge acquired during the first year of a four-year technical degree, such as computer science, is already outdated by the time students graduate, according to The Future of Jobs, a report from the World Economic Forum (WEF).

The WEF’s report further notes that “65% of children entering primary school today will ultimately end up working in jobs that don’t yet exist.” By contrast, human skills such as interpersonal communication and project management will remain consistent over the years.

For example, organizations already report that they are having difficulty finding people equipped for the Big Data era’s hot job: data scientist. That’s because data scientists need a combination of hard and soft skills. Data scientists can’t just be good programmers and statisticians; they also need to be intuitive and inquisitive and have good communication skills. We don’t expect all these qualities from our engineering graduates, nor from most of our employees.

But we need to start.

From Self-Help to Self-Skills

Even if most schools and employers have yet to see it, employees are starting to understand that their future viability depends on improving their innately human qualities. One of the most popular courses on Coursera, an online learning platform, is called Learning How to Learn. Created by the University of California, San Diego, the course is essentially a master class in human skills: students learn everything from memory techniques to dealing with procrastination and communicating complicated ideas, according to an article in The New York Times.

Attempting to teach employees how to make behavioral changes has always seemed off-limits to organizations—the province of private therapists, not corporate trainers. But that outlook is changing.

Although there is a longstanding assumption that social skills are innate, nothing is further from the truth. As the popularity of Learning How to Learn attests, human skills—everything from learning skills to communication skills to empathy—can, and indeed must, be taught.

These human skills are integral for training workers for a workplace where artificial intelligence and automation are part of the daily routine. According to the WEF’s New Vision for Education report, the skills that employees will need in the future fall into three primary categories:

  • Foundational literacies: These core skills needed for the coming age of robotics and AI include understanding the basics of math, science, computing, finance, civics, and culture. While mastery of every topic isn’t required, workers who have a basic comprehension of many different areas will be richly rewarded in the coming economy.
  • Competencies: Developing competencies requires mastering very human skills, such as active listening, critical thinking, problem solving, creativity, communication, and collaboration.
  • Character qualities: Over the next decade, employees will need to master the skills that will help them grasp changing job duties and responsibilities. This means learning the skills that help employees acquire curiosity, initiative, persistence, grit, adaptability, leadership, and social and cultural awareness.


The good news is that learning human skills is not completely divorced from how work is structured today. Yonatan Zunger, a Google engineer with a background working with AI, argues that there is a considerable need for human skills in the workplace already—especially in the tech world. Many employees are simply unaware that when they are working on complicated software or hardware projects, they are using empathy, strategic problem solving, intuition, and interpersonal communication.

The unconscious deployment of human skills takes place even more frequently when employees climb the corporate ladder into management. “This is closely tied to the deeper difference between junior and senior roles: a junior person’s job is to find answers to questions; a senior person’s job is to find the right questions to ask,” says Zunger.

Human skills will be crucial to navigating the AI-infused workplace. There will be no shortage of need for the right questions to ask.

One of the biggest changes narrow AI tools will bring to the workplace is an evolution in how work is performed. AI-based tools will automate repetitive tasks across a wide swath of industries, which means that the day-to-day work for many white-collar workers will become far more focused on tasks requiring problem solving and critical thinking. These tasks will present challenges centered on interpersonal collaboration, clear communication, and autonomous decision-making—all human skills.

Being More Human Is Hard

However, the human skills that are essential for tomorrow’s AI-ified workplace, such as interpersonal communication, project planning, and conflict management, require a different approach from traditional learning. Often, these skills don’t just require people to learn new facts and techniques; they also call for basic changes in the ways individuals behave on—and off—the job.

Attempting to teach employees how to make behavioral changes has always seemed off-limits to organizations—the province of private therapists, not corporate trainers. But that outlook is changing. As science gains a better understanding of how the human brain works, many behaviors that affect employees on the job are understood to be universal and natural rather than individual (see “Human Skills 101”).

Human Skills 101

As neuroscience has improved our understanding of the brain, human skills have become increasingly quantifiable—and teachable.

Though the term soft skills has managed to hang on in the popular lexicon, our understanding of these human skills has increased to the point where they aren’t soft at all: they are a clearly definable set of skills that are crucial for organizations in the AI era.

Active listening: Paying close attention when receiving information and drawing out more information than received in normal discourse

Critical thinking: Gathering, analyzing, and evaluating issues and information to come to an unbiased conclusion

Problem solving: Finding solutions to problems and understanding the steps used to solve the problem

Decision-making: Weighing the evidence and options at hand to determine a specific course of action

Monitoring: Paying close attention to an issue, topic, or interaction in order to retain information for the future

Coordination: Working with individuals and other groups to achieve common goals

Social perceptiveness: Inferring what others are thinking by observing them

Time management: Budgeting and allocating time for projects and goals and structuring schedules to minimize conflicts and maximize productivity

Creativity: Generating ideas, concepts, or inferences that can be used to create new things

Curiosity: Desiring to learn and understand new or unfamiliar concepts

Imagination: Conceiving and thinking about new ideas, concepts, or images

Storytelling: Building narratives and concepts out of both new and existing ideas

Experimentation: Trying out new ideas, theories, and activities

Ethics: Practicing rules and standards that guide conduct and guarantee rights and fairness

Empathy: Identifying and understanding the emotional states of others

Collaboration: Working with others, coordinating efforts, and sharing resources to accomplish a common project

Resiliency: Withstanding setbacks, avoiding discouragement, and persisting toward a larger goal

Resistance to change, for example, is now known to result from an involuntary chemical reaction in the brain known as the fight-or-flight response, not from a weakness of character. Scientists and psychologists have developed objective ways of identifying these kinds of behaviors and have come up with universally applicable ways for employees to learn how to deal with them.

Organizations that emphasize such individual behavioral traits as active listening, social perceptiveness, and experimentation will have both an easier transition to a workplace that uses AI tools and more success operating in it.

Framing behavioral training in ways that emphasize its practical application at work and in advancing career goals helps employees feel more comfortable confronting behavioral roadblocks without feeling bad about themselves or stigmatized by others. It also helps organizations see the potential ROI of investing in what has traditionally been dismissed as touchy-feely stuff.

In fact, offering objective means for examining inner behaviors and tools for modifying them is more beneficial than just leaving the job to employees. For example, according to research by psychologist Tasha Eurich, introspection, which is how most of us try to understand our behaviors, can actually be counterproductive.

Human beings are complex creatures. There is generally way too much going on inside our minds to be able to pinpoint the conscious and unconscious behaviors that drive us to act the way we do. We wind up inventing explanations—usually negative—for our behaviors, which can lead to anxiety and depression, according to Eurich’s research.

Structured, objective training can help employees improve their human skills without the negative side effects. At SAP, for example, we offer employees a course on conflict resolution that uses objective research techniques for determining what happens when people get into conflicts. Employees learn about the different conflict styles that researchers have identified and take an assessment to determine their own style of dealing with conflict. Then employees work in teams to discuss their different styles and work together to resolve a specific conflict that one of the group members is currently experiencing.

How Knowing One’s Self Helps the Organization

Courses like this are helpful not just for reducing conflicts between individuals and among teams (and improving organizational productivity); they also contribute to greater self-awareness, which is the basis for enabling people to take fullest advantage of their human skills.

Self-awareness is a powerful tool for improving performance at both the individual and organizational levels. Self-aware people are more confident and creative, make better decisions, build stronger relationships, and communicate more effectively. They are also less likely to lie, cheat, and steal, according to Eurich.

It naturally follows that such people make better employees and are more likely to be promoted. They also make more effective leaders with happier employees, which makes the organization more profitable, according to research by Atuma Okpara and Agwu M. Edwin.

There are two types of self-awareness, writes Eurich. One is having a clear view inside of one’s self: one’s own thoughts, feelings, behaviors, strengths, and weaknesses. The second type is understanding how others view us in terms of these same categories.

Interestingly, while we often assume that those who possess one type of awareness also possess the other, there is no direct correlation between the two. In fact, just 10% to 15% of people have both, according to a survey by Eurich. That means that the vast majority of us must learn one or the other—or both.

Gaining self-awareness is a process that can take many years. But training that gives employees the opportunity to examine their own behaviors against objective standards and gain feedback from expert instructors and peers can help speed up the journey. Just like the conflict management course, there are many ways to do this in a practical context that benefits employees and the organization alike.

For example, SAP also offers courses on building self-confidence, increasing trust with peers, creating connections with others, solving complex problems, and increasing resiliency in the face of difficult situations—all of which increase self-awareness in constructive ways. These human-skills courses are as popular with our employees as the hard-skill courses in new technologies or new programming techniques.

Depending on an organization’s size, budget, and goals, learning programs like these can include small group training, large lectures, online courses, licensing of third-party online content, reimbursement for students to attain certification, and many other models.

Human Skills Are the Constant

Automation and artificial intelligence will change the workplace in unpredictable ways. One thing we can predict, however, is that human skills will be needed more than ever.

The connection between conflict resolution skills, critical thinking courses, and the rise of AI-aided technology might not be immediately obvious. But these new AI tools are leading us down the path to a much more human workplace.

Employees will interact with their computers through voice conversations and image recognition. Machine learning will find unexpected correlations in massive amounts of data but empathy and creativity will be required for data scientists to figure out the right questions to ask. Interpersonal communication will become even more important as teams coordinate between offices, remote workplaces, and AI aides.

While the future might be filled with artificial intelligence, deep learning, and untold amounts of data, uniquely human capabilities will be the ones that matter. Machines can’t write a symphony, design a building, teach a college course, or manage a department. The future belongs to humans working with machines, and for that, you need human skills. D!


About the Authors

Jenny Dearborn is Chief Learning Officer at SAP.

David Judge is Vice President, SAP Leonardo, at SAP.

Tom Raftery is Global Vice President and Internet of Things Evangelist at SAP.

Neal Ungerleider is a Los Angeles-based technology journalist and consultant.

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

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Machine Learning In The Real World

Paul Taylor

Over the past few decades, machine learning has emerged as the real-world face of what is often mistakenly called “artificial intelligence.” It is establishing itself as a mainstream technology tool for companies, enabling them to improve productivity, planning, and ultimately, profits.

Michael Jordan, professor of Computer Science and Statistics at the University of California, Berkeley, noted in a recent Medium post: “Most of what is being called ‘AI’ today, particularly in the public sphere, is what has been called ‘machine learning’ for the past several decades.”

Jordan argues that unlike much that is mislabeled “artificial intelligence,” ML is the real thing. He maintains that it was already clear in the early 1990s that ML would grow to have massive industrial relevance. He notes that by the turn of the century, forward-looking companies such as Amazon were already using ML throughout their business, solving mission-critical back-end problems in fraud detection and logistics-chain prediction and building innovative consumer-facing services such as recommendation systems.

“Although not visible to the general public, research and systems-building in areas such as document retrieval, text classification, fraud detection, recommendation systems, personalized search, social network analysis, planning, diagnostics, and A/B testing have been a major success — these are the advances that have powered companies such as Google, Netflix, Facebook, and Amazon,” Jordan says.

Amazon, which has been investing deeply in artificial intelligence for over 20 years, acknowledges, “ML algorithms drive many of our internal systems. It’s also core to the capabilities our customers’ experience – from the path optimization in our fulfillment centers and Amazon’s recommendations engine o Echo powered by Alexa, our drone initiative Prime Air, and our new retail experience, Amazon Go. “

The fact that tech industry leaders like Google, Netflix, Facebook, and Amazon have used ML to help fuel their growth is not news. For example, it has been widely reported that sites with recommendation engines, including Netflix, use ML algorithms to generate user-specific suggestions. Most dynamic map/routing apps, including Google Maps, also use ML to suggest route changes in real time based upon traffic speed and other data gleaned from multiple users’ smartphones.

In a recent article detailing real-world examples of ML in action, Kelly McNulty, a senior content writer at Salt Lake City-based Prowess Consulting, notes: “ML isn’t just something that will happen in the future. It’s happening now, and it will only get more advanced and pervasive in the future.”

However, the broader uptake of ML by enterprises – big and small – is less much less known. A recently published study prepared for SAP by the Economist Intelligence Unit and based on a survey of 360 organizations revealed that 68 percent of respondents are already using ML, at least to some extent, to enhance their business processes.

The report adds: “Some are aiming even higher: to use ML to change their business models and offer entirely new value propositions to customers…… ML is not just a technology.” The report’s authors continue, “It is core to the business strategies that have led to the surging value of organizations that incorporate it into their operating models – think Amazon, Uber, and Airbnb.”

McNulty notes that there are both internal and external uses for ML. Among the internal uses, she cites Thomson Reuters, the news and data services group, which, after its merger in 2008, used ML to prepare large quantities of data with Tamr, an enterprise data-unification company. She says the two partners used ML to unify more than three million data points with an accuracy of 95 percent, reducing the time needed to manually unify the data by several months and cutting the manual labor required by an estimated 40 percent.

In another example of enterprise use of ML, she notes that GlaxoSmithKline, the pharmaceuticals group, used the technology to develop information aimed at allaying concerns about vaccines. The ML algorithms were used to sift through parents’ comments about vaccines in forums and messaging boards, enabling GSK to develop content specifically designed to address these concerns.

In the financial sector, ML has been widely used for some time to help detect fraudulent transactions and assess risk. PayPal uses the technology to “distinguish the good customers from the bad customers,” according to Vadim Kutsyy, a data scientist at the online payments company.

PayPal’s deep learning system is also able to filter out deceptive merchants and crack down on sales of illegal products. Additionally, the models are optimizing operations. Kutsyy explained the machines can identify “why transactions fail, monitoring businesses more efficiently,” avoiding the need to buy more hardware for problem-solving.

ML algorithms also underpin many of the corporate chatbots and virtual assistants being deployed by enterprise customers and others. For Example, Allstate partnered with technology consultancy Earley Information Science to develop a virtual assistant called ABIe (the Allstate Business Insurance Expert). ABIe was designed to assist Allstate’s 12,000 agents to understand and sell the company’s commercial insurance products, reportedly handling 25,000 inquires a month.

Other big U.S. insurance companies, including Progressive, are applying ML algorithms to interpret driver data and identify new business opportunities.

Meanwhile, four years ago, Royal Dutch Shell became the first company in the lubricants sector to use ML to help develop the Shell Virtual Assistant. The virtual assistant enables customers and distributors to ask common lubricant-related questions.

As the company noted at the time, “customers and distributors type in their question via an online message window, and avatars Emma and Ethan reply back with an appropriate answer within seconds.” The tool was initially launched in the U.S. and UK but has since expanded to other countries and reportedly can now understand and respond to queries in multiple languages, including Chinese and Russian.

In the retail sector, Walmart, which already uses ML to optimize home delivery routes, also uses it to help reduce theft and improve customer service. The retail giant has reportedly developed facial recognition software that automatically detects frustration in the faces of shoppers at checkout, prompting customer service representatives to intervene.

Among SAP’s own customers, a growing number are implementing ML tools, including those built into SAP’s own platforms and applications. As SAP notes, “Many different industries and lines of business are ripe for machine learning—particularly the ones that amass large volumes of data.”

The manufacturing, finance, and healthcare sectors are leading the way. For example, a large European chemicals company has improved the efficiency and effectiveness of its customer service process by using ML algorithms to automatically categorize and send responses to customer inquiries.

In the mining sector, Vale, the Brazilian mining group, is using ML to optimize maintenance processes and reduce the number of purchase requisitions that were being rejected causing maintenance and operational delays in its mines. Before implementation, between 25 percent and 40 percent of purchase requisitions were being rejected by procurement because of errors. Since implementation, 86 percent of these rejections have been eliminated.

Elsewhere a large consumer goods company, the Austrian-based consumer good company, is using ML and computer vision to identify images of broken products submitted by customers from the over 40,000 products in the company’s catalog. The application enables the company to speed up repairs and replacements, thereby improving customer service and the customer experience.

Similarly, a global automotive manufacturer is using image recognition to help consumers learn more about vehicles and direct them to local dealer showrooms, and a major French telecommunications firm reduced the length of customer service conversations by 50 percent using chatbots that now manage 20 percent of all calls.

But not every enterprise ML deployment has worked out so well. In a highly publicized case, Target hired a ML expert to analyze shopper data and create a model that could predict which female customers were most likely to be pregnant and when they were expected to give birth. (If a woman started buying a lot of supplements, for example, she was probably in her first 20 weeks of pregnancy, whereas buying a lot of unscented lotion indicated the start of the second trimester.)

Target used this information to provide pregnancy- and parenting-related coupons to women who matched the profile. But Target was forced to modify its strategy after some customers said they felt uncomfortable with this level of personalization. A New York Times story reported that a Minneapolis parent learned of their 16-year-old daughter’s unplanned pregnancy when the Target coupons arrived in the mail.

Target’s experience notwithstanding, most enterprise ML projects generate significant benefits for customers, employees, and investors while putting the huge volumes of data generated in our digital era to real use.

For more insight on the implications of machine learning technology, download the study Making the Most of Machine Learning: 5 Lessons from Fast Learners.