Vin Vashishta and Tiffany Perkins-Munn explain how companies can sell data as a repeatable product. Vin uses Instacart's purchase data powering ads for partners as an example, then describes building a platform to monetize the data itself, the relationships, and the models built from it.
So I think it's time we talked about some real examples of how you go about making money, just to make it really concrete. So, Vin, do you want to tell me, like, what are the most common ways that you're actually going to make money from data or AI?
Well, the first one with data is figuring out who wants access. And that's probably the hardest thing for companies to wrap their heads around because they're used to traditional products, we know who the customer is, but with data, it's a unique asset. You can monetize it multiple times. So what we really have to do is change the way that we think about data as a product. It isn't just going to be used once. This is something that can be monetized multiple times. And so the way that I break it down when I'm working with C level leaders is just to say, look what generated this data and who else cares about that thing? If you're looking about at a process where you're helping customers, who else would want access to those customers and when? What sort of intent could you support? If you look at Instacart, great example, they provide ads at the point of intent to buy a particular item. So they want, you know, external partners want access to data that helps them serve ads at the point of intent. So this is critical. If you look at it in ad marketplaces or any other sort of marketplace concept or platform construct, how can you bring partners onto the platform with data? Because then you're not just monetizing them with the data, you can monetize them in multiple ways on the platform. So not only are you monetizing data multiple times, you're also monetizing that same partner multiple times and you can improve the artifacts that you deliver. So you go from delivering data that supports something that they care about to improving that data and delivering a model artifact. So now you are providing not just obvious insights from your platform, but true insights, where it is new information, new domain knowledge that you're bringing to your partners. That's sort of the framework for thinking. If you look at it as how do we bring partners in and how do we monetize what we used to not be able to monetize in the past without building out the same thing that the partners have. How do we use data to get them on the platform and get them engaged? And that works with partners, but it also works with new types of customers. How can we use data to bring new people? And so it's really just connected back to what is it that Generated the data, what was the workflow, the process, the system, whatever it is. And who else cares about that? Who else is interested in that? And that'll point you in the right direction for monetization every single time. And it goes beyond just looking at customer success or looking at experience or looking at cost savings around finance, or different types of really common workflows. It pulls you back and you start saying, oh, it's not just about efficiency, it's not just about incremental, it's about completely new revenue streams and that's the highest value. If you start there, that's where you get the attention of the rest of the business. Now you have C level leaders bought in because you're showing cash growth, things that are hard to get right now.
That's interesting because I think a lot of businesses start with, oh well, we can use, we can automate something, we can save some money here, but maybe that's not the right place to start. Okay, Tiffany, do you have any binge on this? Like, what examples have you seen of using data to make some money?
I think the most obvious one is like selling or licensing data sets to external parties. Right. A company that collects and analyzes market trends might sell aggregated and anonymized data to other businesses for market research purposes. So I'm thinking of the data that we in financial services have actually participated in. There are subscription based services where you charge a subscription for access to premium or exclusive data. Obviously targeted advertising and marketing, that's a big deal. Like using data to deliver targeted advertising or marketing campaigns and charging the advertisers for access to a specific audience. Data driven innovation where you're using data to innovate around new products, like using data and information that you learned from consumers about maybe a new product that doesn't exist. Like I remember I was talking to someone at Amazon once and he was saying that they learn a lot by the search that clients or that customers use Go. You know, when they log into Amazon, they often learn that there are products that people are asking for sort of en masse that aren't available. And so it gives them a lot of ideas. That just simple keyword search in Amazon gives them a lot of ideas about how they could create new products and services that they could subsequently monetize based on sort of demand from consumers. And, and I think there's really easy, that's an unexpected but easy way of driving sort of revenue from data. And there are more complicated ways to do it as well. But I think the goal is think about from the easiest like how could we package this already existing data in a way that is meaningful to share with others that might also find it useful to more complicated how do we use technology technology to wrangle or manage this data in a way that might get us to a new solution that could also be a candidate for monetization?
