Sam Yagan, current ShopRunner CEO, cofounder of OkCupid, and former Match Group CEO, discusses the importance of culture for driving data-based decision making, innovation, and, ultimately, company success.
Sam Yagan has led an illustrious entrepreneurial career by embracing data and innovation. He helped transform online dating when he cofounded OkCupid, which continually searched for unique ways to leverage user data to increase the chances of singles finding compatible mates. After selling the successful company to the Match Group, he became the dating conglomerate’s CEO, during which time the group innovated digital matchmaking again by introducing the “swipe” method for companion selection in its Tinder app.
Now, as the CEO of ShopRunner, he’s at it once more. The online service offers members benefits such as two-day shipping and free returns across more than 100 retailers. But some of its biggest benefits derive from data. The company uses data to provide online shoppers with personalized experiences and its participating retailers with access to new customers. And it’s rolling out innovative products such as its new mobile app District, which, among other neat features, offers users a tailored, real-time feed of trending products.
How has Yagan’s companies had so much success innovating and putting data to work while other organizations continue to struggle to get these efforts off the ground? He recently sat down with McKinsey’s Laura DeLallo to share the secret to success: creating the right culture. The following commentary is adapted from that interview.
How ShopRunner is building a data culture
As I think back on my past 20 years of experience running organizations of varying sizes and in different industries, my single biggest learning is that the most important predictor of a company’s success and ability to innovate is culture.
When I became CEO at ShopRunner, from day one I made it clear to everybody that we were going to make decisions based on data. What that meant was that some people were no longer going to be cultural fits at the company. And when we hired people, we used data experience as a selection filter. We asked direct questions such as, “How have you used data in the past to make decisions?”
In addition, our executives model the desired behavior. When workers see the executive team making data-based decisions, it becomes easy for that kind of decision making to flow through the organization. When workers hear the executive team talking about making decisions in that way, they say, “Of course they’re going to expect me to make decisions in a similar way.”
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I think one of the big changes over the past decade has been the democratization of analytical data—through whatever your business-intelligence tool of choice is. At ShopRunner, it’s expected that everyone in the company is logging into our system and able to check on any piece of data that is available to them.
The way we’ve gotten everyone to do that is by making sure that they know how to “fish.” When somebody comes to the analytics team with a request and says, “Hey, I’d like you to run this report,” the analytics team is expected to not just send the report but also teach the person how to run it in the future and make sure that the request doesn’t come in again.
But, secondarily, because data is so infused in our culture, it doesn’t take long before someone realizes that the person sitting next to them or the person sitting across from them is running his or her own reports and accessing his or her own data.
Requiring people to fail fast is one of the most important attributes of an innovative culture because, let’s face it, most innovations fail. At ShopRunner, there are several ways we reinforce our fail-fast culture. Number one, at every all-hands meeting we have an executive present a failure that he or she had, and we talk openly about it.
Number two, during annual reviews, I request that every executive quantify his or her failures. You can’t just say, “Oh, I worked too hard.” You’ve got to actually put a number against it and say, for example, “Because I managed this person incorrectly, I ended up having to spend $40,000 to replace her.” It’s about getting people to acknowledge and put in writing that they cost the company this amount of money and telling them it’s OK.
In fact, you start to ask them, “Did you take enough risk? You had only a $10,000 failure this year. What if you had had a $100,000 failure? What other returns could we have gotten if that bet had gone the other way?” I think it’s about celebrating and quantifying failure and being willing to talk openly about it.
You need to constantly talk about executive failures, even CEO failures. When I was CEO at Match, we acquired a company, and I think I overpaid for it by $50 million. I got up in front of everyone and talked about that—and people were able to see that I was still there. So people could say, “If he made a $50 million mistake, maybe that’s not so bad. Maybe I can make a $50,000 mistake.”
Lessons from Tinder on innovating in the face of cultural headwinds
I think one of the reasons so few market-leading companies are able to disrupt themselves is the cultural resistance that the organization has to threats, whether they come from the outside or even from the inside. When we launched Tinder inside of Match, we found that there was resistance from some people inside the core business who saw Tinder as a threat—maybe to the business, maybe to their own expertise in the company, and certainly to the culture.
In Tinder, we had a team of people in a different office who were unconventional in a number of ways. But it was that unconventional approach to dating that I think made Tinder what it was.
One of the big challenges we had as an executive team at Match was to figure out how to get an organization that had over 1,000 people and over a billion dollars of revenue to accept this “other” that was birthed inside of the company. In retrospect, I think keeping Tinder alive and allowing it to thrive was harder than the actual birthing of Tinder.
In terms of bringing the organization along, I don’t think there was a silver bullet. We tried a number of different approaches throughout Tinder’s evolution. Number one was explaining that everyone in the company had equity and that, as Tinder grew, the value of our equity grew and the value of the company grew.
Number two, we pointed out that having a new kind of business in the organization was ultimately a learning experience. We were able to cross-pollinate a lot of people from the core business, whether it was the analytics team, the strategy team, or the technology team. As they interacted more with Tinder teams, they were able to embrace Tinder as just another part of the company rather than this insurgent threat.
As Tinder grew, both for logistical reasons and to bring the organizations closer together, we tried moving it into a Match office. This had mixed results. We ended up initially splitting a floor in half, with Match on one side of the office and Tinder on the other side. But, as you can imagine, everything about the way those two halves ran was very different—one side worked different hours, one side worked at a different volume, and one side just was decorated differently.
In some ways, this setup made the cultural differences more stark. It almost made it worse to have the teams so close together but not actually integrated.
Over time, Tinder went through a bunch of different office spaces, including having its own office space. Now it is back in more of a merged setting, with Tinder growing to be such a big part of Match.
I think as people realized that all of the core businesses were still intact and all the core businesses were still successful, it was easier to bring Tinder into the fold and really operate as one organization.
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