Use Data Analytics to Build Trust and Loyalty

Leaders who can balance mastery of the power of data with the insight and the ability to use it wisely will have a huge advantage. But it's possible to be too smart for your own good.

By Robert E. Siegel August 31, 2021

​We're still in the early days of an analytics revolution. Vast amounts of data are increasingly available for all kinds of companies, from the oldest incumbents to the newest disruptors, from high tech to low tech, in industries ranging from products to services. Data has enormous potential to improve your offerings, enhance your customer service and sharpen your marketing messages, while simultaneously helping you slash unnecessary costs. It's no exaggeration to say that data powers the engines of modern businesses.

But there's a catch: while gathering data has never been easier, using it wisely has never been harder. Everyone needs a strategy to manage the flood of incoming information. You can get distracted by focusing on the wrong metrics—perhaps those that are easy to track but not the real drivers of the success of your business. Or you can be tempted into abusing your data to exploit your own customers in ways large or small, thereby damaging their loyalty and trust. Leaders who can balance mastery of the power of data with the insight and ability to use it wisely will have a huge advantage. But it's possible to be too smart for your own good.

Facebook: The Perils of Analytics

A huge risk is unleashing machine learning and artificial intelligence (AI) without considering how your customers or users might feel about being turned into fodder for analytics. Companies that focus on optimizing data at all costs risk alienating the very people who drive their business model. The ultimate cautionary example is Facebook, which spent its first decade building up an enthusiastic and massive user base, then spent the next half decade blowing up its reputation and angering its users. Although Facebook is still growing and extremely profitable, many business leaders I know see it as a case study of the consequences of being careless, complacent and greedy about the power of data.

Early on, Facebook users were fine with targeted ads based on their interests. It made sense that if you loved to garden, you'd see ads for gardening supplies in your news feed. It seemed like a small and reasonable price for the benefits Facebook offered as a free platform for connecting friends and family. And sometimes those gardening ads would actually show something you wanted.

But over time, by focusing relentlessly on optimizing revenue, Facebook violated user trust. It sold private information to third parties, including unscrupulous political organizations like Cambridge Analytica, without warning users or giving them a fair chance to opt out. That led to a 2018 data breach that was the largest known leak in Facebook history. News headlines about data insecurity and abuse of privacy drove some people to abandon the platform. It didn't matter whether Facebook had upheld the letter of the disclosure laws in the fine print of its baffling user agreement. Its lawyers and compliance experts missed the bigger picture of trust and customer service.

It may sound easy to monetize the golden eggs of data analytics without killing the goose of trust, but it's not. For a deeper look at the complexities involved, let's dig into Charles Schwab & Co.

Using Analytics to Preserve Trust and Strengthen Loyalty

During my interviews with Schwab CEO Walt Bettinger and his visits to my Stanford class, he must have mentioned trust 100 times. It's his biggest obsession, because he knows that anything that damages customer trust in Schwab is potentially catastrophic, and threats can come from multiple directions at any time.

The first threat is that people will lose faith in the financial services industry as a whole, as they did for a while after the financial crisis of 2008. Despite regulatory reforms, there are still enough unscrupulous practices and conflicts of interest that the industry has a trust level, as Bettinger put it, "maybe just one or two levels below used-car sales." He added, "Without trust, everything else you do to a certain extent becomes irrelevant. Because this is not like buying shoes, or cars, or neckties or a cup of coffee. This is your future, this is your family's future, this is what you work for and so trust is at the core of everything you do." On this standard at least, Schwab benefits from its clean reputation since 1975. Whenever it rolls out a new product, its brand equity as the good people of financial services goes a long way toward reassuring customers.

A second key threat is technical—the risk of hacking or cyber-sabotage, whether by individual hackers, organized crime, terrorists or foreign enemies of the United States. Joe Martinetto, Schwab's CFO from 2007 to 2017, noted that, "You can spend an infinite amount of money, but if anybody tells you that they can give you a 100 percent guarantee that their systems are impermeable, they're lying."

Schwab's technical experts consider a wide range of possible new security technologies, but it's hard to calculate their ROI. Should Schwab move aggressively on voice authentication? Facial recognition? Retina scanning? Thumb-print technology? Other biometrics? As Martinetto observed, "There's very little that's not affordable in technology anymore. I would love to have some of this better authentication stuff in place, because . . . once we know it's really you, it just makes it a whole lot easier for us to deliver better, faster experiences to you."

Perhaps the biggest and most frightening threat to customer trust, because it's the most tempting, is the potential misuse of customer data. It would be extremely easy for Schwab to follow Facebook and others to the dark side by exploiting that ocean of data it collects every day. The more a company knows about customer behavior, the more it can deduce via AI, which opens up countless opportunities for customized offers at low marginal cost. But where do you draw the line between targeted marketing and an invasion of clients' privacy? Machine learning can't answer that question, nor can the legal department. It requires a strong, companywide sense of mission and values.

Consider an example Bettinger brought up. "If you're on and you click on the 'life events, divorce' section, we might know something before your spouse does." It would be easy to send an email saying something like, "We noticed that you're researching the financial impact of a possible divorce. Here are links to some articles and online calculators that can help you plan." Even customers who rarely worry about data privacy would consider that the worst kind of "Big Brother is watching you" scenario. Any incremental profit generated by that email would be dwarfed by customer departures and bad publicity.

In 2021 trust was raised as an issue yet again in the financial services industry with the volatile and erratic surge in stock trading around the company GameStop. Robinhood was at the center of the controversy when it restricted trades in certain companies, including GameStop, which prevented people from selling their positions as the stock was falling. Some asserted that Robinhood was hurting individuals by not allowing them to make trades, but large hedge funds were still able to sell their shares and get out as the company's stock price dropped. While it was later revealed that Robinhood needed to raise additional capital to cover their customers' trading activity, and thus limited the trades as it was necessary for the company to operate its business, the lack of trust in the company's actions led to the CEO being summoned to testify in front of Congress. The company's opaqueness in why it limited trades caused intense blowback for several weeks. In an industry that sells something as vitally important as financial well-being, Robinhood's approach stands in stark contrast to Schwab's relentless obsession with customer trust.

Bettinger says that Schwab executives almost never have internal disagreements about trust-related decisions. "Those [company's core] five principles go a long way toward preventing debate, because they're so simple and clear. We think of them as both a road map to growth and a set of guardrails to keep us out of trouble."

Bettinger's team rejects the Silicon Valley cliché of "Move fast and break things"—their style is more like "Move as fast as you can without breaking things." They study the competitive landscape, evolve with the times and cannibalize parts of the business when necessary. They learn from upstarts instead of ignoring them. But they never risk the trust of the customer base or nickel-and-dime customers to inflate profits. And they never forget that doing the right thing is still the best path to long-term loyalty.

Reprinted by permission of McGraw-Hill. Adapted from The Brains and Brawn Company: How Leading Organizations Blend the Best of Digital and Physical by Robert E. Siegel. Copyright 2021 Robert E. Siegel. All rights reserved.

Robert E. Siegel is a Lecturer in Management at the Stanford Graduate School of Business and a venture investor based in Silicon Valley. He is the author of The Brains and Brawn Company: How Leading Organizations Blend the Best of Digital and Physical.

Upcoming EventsSee All Events

October 17 - 18
June 11 - 14
SHRM Annual Conference & Expo 2023