Monetizing Data Products: Key Questions to Ask About Turning Data Into $$$
“We’re sitting on this treasure trove of data, think of how valuable it is if we could get other people to pay for it.” As a general rule, I think most companies overestimate the the value of the data they have and underestimate the challenge of creating a product people will pay for. That doesn’t mean they shouldn’t do it. After all, many companies are sitting on top of transformational, hard-to-access data that other people want. But in order to assess if there’s a “product” there, we need to be really critical of its potential market fit and thoroughly vet others’ willingness to pay.
Why does this matter to me? I’ve been part of companies that leverage unique data as a core asset (LinkedIn, Yelp, CoreLogic) and in each company, I’ve seen transformational data products emerge because of the uniqueness of the data. Data products that have allowed others across the world to have unique insight into the day to day happenings in the job market, local business economy and housing market. But I also get many questions about why we don’t do “more” with our data. The simplest answer is that the ROI isn’t as clear as it seems and the opportunity costs of monetizing the data are high relative to other business needs. There is a high bar to justify this effort.
You might be reading the above thinking, “Yada, yada, yada, Eric, I know we have something here and I want to push to bring it to market.” First of all, that’s great. That probably means you have the energy and passion to push something new, which is a prerequisite to seeing a product idea like this across the line. Second, you’ve probably thought about the reasons you believe the data product you have in mind is valuable. But I am also guessing that in creating an idea of what it looks like in your mind, you’ve thought about all the reasons it would be great. I want to turn your focus to a different framing: reasons your data product might fail.
I’m not writing this to shoot down your idea. But there are many things I wish I knew years ago when I thought I had the next great data monetization idea that would have helped me assess what kind of opportunity I really had. While the list of suggestions I have is long, there are 5 questions I think you’ll find useful to ask as you assess the opportunity.
Is your data unique, and for how long? Data uniqueness is not forever, it is a question of how long you have the advantage. Many companies believe they have the only dataset that speaks to certain types of behavior and provides insight that no one else has. My first question is, how do you know? List out the reasons you believe that your data is not available to others and what kind of moat you have around your data. The second question is how long do you believe you’ll have this advantage? It likely won’t be forever, but thinking about this answer in months, years, or decades is a useful exercise. Why does uniqueness matter? It is one of the clearest value propositions and competitive advantages a data product can have.
What is the value proposition of your data product? Another way to put this is what would someone be able to do given your data product that they could otherwise not do, or how does your data product make that easier? For example, you might have unique data that enables a person to make a decision that would otherwise be a random guess. In other cases, you may have the unique ability to combine hundreds of datasets into a singular view that makes life far easier for the end user. A useful outcome of answering this question is to identify a rank ordered list of value propositions, from highest to lowest. Share this with others and get their feedback!
What segments of the population find the most value in your data product? There’s a dangerous statement I see made around data that goes something like this: “Everyone will definitely want this”. In reality, not everyone wants your data. The population you believe will find the value proposition in your data is probably far larger than the group that actually will find that value. Transparently, I’ve come up with ideas for data products over the past 5 years that I thought everyone would be willing to pay for. It turns out, they were just being nice when I pitched the idea. 95% of those who were interested didn’t want to actually pay. Yet I built the data products mostly off of those 95%. [cuts to Eric staring off into the distance muttering about well intentioned data products…]
Is it data access and licensing you care about, or a data product that shapes your data in a new way? Not all data products have to be elaborate. In many cases, others simply want access to data that you have because it is useful for them in a specific goal/task they want to do. Other data products are useful because of the analytical rigor or predictive power they create. There’s a continuum from providing raw data to providing insights and recommendations. All of them can be valuable. You just need to decide where on the continuum your data product falls. What helps you determine this? [cuts to last item]
How much do you know about customers’ willingness to pay? This last point used to make me extremely uncomfortable. But it’s real. If you want to monetize a data product, you must know about what people are willing to pay. This does not mean picking a number out of thin air and declaring “this seems right!” Willingness to pay means doing careful market research, talking to potential customers, and actually selling your product. Pricing is one of the hardest things to get right and if you don’t treat it seriously, you’ll potentially price yourself out of the market or you’ll miss a ton of potential revenue upside from the data product.
By no means do the 5 questions above encompass everything you need to think about when trying to monetize a data product. But I promise you that if you are able to answer them in serious ways, you’ll be on your way to having high quality conversations with your company and peers about your idea and that much closer to bringing your product to market.
Excellent points, thank you for the article.