The Trouble with Sales Goals: Lessons from the Banking Industry

Peter Drucker once said, “What gets measured gets improved.” In sales, we might say, “What gets measured gets sold”—but not always for the better. Having worked in commissioned sales roles for most of my career before joining Gainplan, I’ve seen this dynamic play out up close.

Take retail banking, for example. I used to work at Chase, where we were pushed to open checking accounts and credit cards as part of our sales goals. Banks like Chase and Wells Fargo set targets that sounded simple—“Open more accounts”—but didn’t anticipate how employees might hit those numbers in ways that ultimately hurt customers and the bank.

Wells Fargo is a notorious case. Between 2011 and 2016, employees opened over 1.5 million accounts without customer consent. The result? A $185 million fine, roughly $2 million refunded in fees, and over 5,000 employees terminated. But the problem wasn’t rogue employees alone—it was systemic. Unrealistic sales quotas created a culture where employees felt pressured to commit fraud just to keep their jobs. What’s worse, these fake accounts generated little to no revenue, and the reputational damage was immense. It wasn’t a case of people trying to cheat the system for personal gain. It was a desperate attempt to meet unattainable goals in a broken system.

Can a Robot Fix the Hiring Problem?

In financial planning, we’re used to seeing “robo-advisors” help people invest. Now, we’re seeing “robo-recruiters”—and honestly, it might be time. Humans aren’t always great at hiring. We rely on gut feelings, personal biases, and limited exposure to people outside our usual social circles.

Deutsche Bank has started using online dating algorithms to screen job candidates. These algorithms compare responses from applicants to those of top-performing employees—not based on skills alone, but on personality traits and work styles. It’s an interesting shift, especially in white-collar jobs where company culture and values can matter just as much as technical expertise.

The kicker? The system isn’t built to follow human logic. In some cases, it might recommend candidates who don’t seem like a good fit at all—but align well with the company’s high performers. Whether this turns out to be a breakthrough or a mess remains to be seen. Either way, it signals a move toward removing unconscious bias and traditional thinking from hiring, and that’s worth watching.

When Robo-Advisors Cross the Line

Speaking of robots, let’s talk about Betterment. I’ve written about them before, but during the Brexit market shock, they made headlines by halting trading on their platform—including for advisors managing client portfolios.

This wasn’t just a delay—it was a block. Advisors couldn’t make portfolio changes, even if they felt it was necessary. The idea was to prevent rash decisions in a volatile market, but it raised serious concerns. One advisor even said he’d stop using the platform for newer investors who might need more frequent changes in times of uncertainty. (That statement alone makes me question his approach.)

Betterment’s actions raise tough questions: Who should make decisions in a crisis—the client’s advisor or the algorithm? Was this responsible risk management, or overreach? At its worst, it felt like a scene from a sci-fi movie, where the AI decides, humans aren’t fit to manage their own money.

Final Thoughts

Technology continues to reshape how we work, hire, and manage money. But human oversight, ethical standards, and clear goals still matter. Whether its banks setting the right metrics, companies using AI to find top talent, or advisors navigating robo-platforms—how we design and manage systems has real consequences. We can’t just “set it and forget it,” especially when people’s finances and futures are on the line.

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