Marketing is all about attribution – if you can’t measure it, it doesn’t exist.
MEDIA 7: Could you please tell us something about your wonderful career journey so far, and what inspired you to bring Vrity into existence?
JESSE WOLFERSBERGER: My career has been defined by using data to quantify the abstract. First I did it in baseball, then in media, then in loyalty marketing. All of a sudden, I saw the biggest use case for this skill set I’ve ever seen, so I had to jump at it. In the past two years, there has been a huge movement for brands to stand for more than just their own profit. It goes by many names – Stakeholder Capitalism, ESG, CSR. I like the term, ‘The Values Economy’. I read articles after articles about this trend, but I noticed there was never any data. Here was the biggest trend in marketing since digital, with no data to quantify the financial impact on brands.
M7: Vrity's mission is to show brands the financial impact of their values, so they have the evidence to take more and bigger values-based actions. Could you please elaborate on this?
JW: Marketing is all about attribution – if you can’t measure it, it doesn’t exist. So, when a brand makes a big effort to support racial justice, or climate change, or trans rights, they could gain huge numbers of new customers, but until we came along, there was no good attribution mechanism. Without attribution, those incremental sales are credited elsewhere and the brand never understands what truly drives their revenue. We use AI to enable brands to actually see purchase propensity gains due to their values, so a brand can learn, for example, that 18% of their sales can be attributed to their climate change efforts. When they see that values have a positive ROI, the brand will put more money and effort into making the world a better place. This is a win for the environment, a win for consumers, and a win for the brand. Let me also say that we measure negative propensity shifts too. Sometimes a stance on a more controversial issue could lose customers. However, in our data, the gained customers outnumber the lost customers across every major topic.
When they see that values have a positive ROI, the brand will put more money and effort into making the world a better place.
M7: What’s top of the list for what Vrity wants to achieve this year and as you start moving into the next year?
JW: Our biggest goal is to continue to grow, so we can get our tools into the hands of as many brands as possible. The more brands that understand the ROI of their values, the more brands will lean into their values. This trend is a huge opportunity for brands looking for ways to get out of the pay-per-click race to the bottom. When you are a values-driven brand, consumers seek you out.
M7: From sportswriting, baseball, to being a data scientist, AI thought Leader, and ultimately getting into digital media analytics. Quite a jump! Could you please share this experience?
JW: My varied background is truly my strength. When I transitioned from sports writing to data science, I had a serious case of impostor syndrome. I quickly learned that combining data skills with the ability to write and communicate was an enormous strength. For any data scientists out there, I highly recommend taking a class on communications or news writing. It will make a bigger difference than learning about that new modeling technique.
When you are a values-driven brand, consumers seek you out.
M7: How do you prepare for an AI-centric world as a Business Leader?
JW: Everything new seems scary at first. Then, you learn about it and the fear subsides and it becomes a tool in your toolbelt. In the case of AI, you don’t need to understand how the algorithms work any more than you know how the microchips in your phone work. The key is understanding when to use it, how to use it, and what to be careful about. For example, you don’t need to know how neural networks work to understand that if you train an algorithm on a dataset without black and brown faces, then the algorithm won’t be good at recognizing black and brown faces. That’s not data science, it’s common sense.