4 Ways To Know If They're Into You(r data)
Photo by Alexander Sinn on Unsplash
Originally posted on 23 May, 2024.
turning Datasets Into Revenue
(Note: this post is absolutely not about selling user data or PII, or licensing data that shouldn't be )
Of all the famous mythical creatures roaming the Deep Tech canyon and the B2B SaaS forest, the revenue-producing dataset is amongst the most elusive. As a friend of mine once joked, “If your pitch deck to a VC doesn’t talk about the amazing dataset you’re building, are you even raising money?”.
But what if there was an actual dataset somewhere in there?
Perhaps your team has built it to power your product, or perhaps it is a product output? Could there be a way to leverage this into a cost-efficient, secondary revenue source?
Especially in a still-difficult fundraising climate, those are questions worth asking.
I’ve helped numerous startups explore this business challenge. The most prominent public example is developer security unicorn Snyk, where the data-led deals I closed resulted in significant revenue and partnerships with some of the world’s leading technology companies (for example with AWS).
Dataset licensing deals can often have much higher average sales prices than product licenses. The stable revenue vs. low cost of sales can be a great addition to the P&L, while leaving the stage to the main growth vectors.
So as a startup founder or product/commercial leader, what questions should you be asking to assess if this is a viable path for you?
Do you have a data asset that can be licensed?
This seems a bit obvious, but going back to the joke about pitch decks, is there an actual data asset? Remember, the objective is to accelerate revenue while the main products are ramping up—so focus on datasets that are already mostly or fully there, and could be productised without disrupting the product roadmap.
If the asset does exist, can you legally sell it? Just to name two considerations: if it includes PII, the law likely has a position on this; if it is open data or purchased from another source, that could constrain its relicensing.
Could the data asset be valuable?
There is no better way to get a sense of this than, of course, if you have inbound leads from companies with nice logos, asking for access to the data. If you’ve read this far, that means your team absolutely has to know where to direct these queries (for example, a product manager could devote 20% of their time to it). Even if you’re very early in your thinking process, this could lead to some invaluable user interviews and early-stage relationships.
Even without meaningful inbound signals, in my experience value can be explored and assessed using these four elements:
Collection (in the case of proprietary data) can create a unique asset.
Aggregation and cleanup in itself can save the customer direct and indirect costs of headcount, tooling, and time. Don't underestimate how hard it can be to get headcount in a large corporate!
Enrichment (with own insights) can once again help create a unique asset even on top of open data (license permitting).
Packaging like an API or a data product can enable use cases and minimise friction for a customer.
Who is it for?
(and what company goals can it support)
Going back to those inbound leads: how did the user interviews go? You let them go cold, didn’t you? ;)
Whether reaching out to leads or conducting proactive research, this is where some product management fundamentals are needed: Who are these companies? Who are the personas and which of the above value elements do their needs map to? (Hint: more is better!).
Beyond that, what is the use case? An example of one is when the customer feeds the data into their own analysis; another example is when a customer-partner wants the data to power their own, external-facing product.
Importantly, let's link this to company goals: Is this just a brand and revenue play? Can these use cases indirectly accelerate your main revenue lines? Could these leads eventually become targets for up- or cross-sell? Could these transactions lead to richer strategic collaborations?
The more you know, the more focused you’ll be on efficient execution, because—broken record—the point is not to distract from the main event. Which leads us to…
How can I avoid distracting my team?
Once you're done with exploration, what do good planning and execution look like?
If you're a venture-funded SaaS company (or on its board), you might be concerned that beyond the upfront investment, this could take focus away from the main products you are building.
If you're a tech-for-good company, you might be wary of building commercial operations that could clash with the culture and greater purpose you are focused on.
Well, I come bearing good news: In my experience, if you have a dataset, a delivery mechanism, and understand the value proposition—most of the heavy lifting from core teams should be behind you, and you can create this as an isolated operation. What's left to do?
Formulate a sales strategy—this could be an isolated team running a sales playbook, but you could just as easily go with self-service access;
Enable the Support team—datasets are usually an easier support task in terms of the variety and impact of issues;
Keep Product engaged—given the relatively small pool of (usually large) leads, a good dataset and a close relationship can simplify the process of launching an MVP and then iterating very efficiently.
---
If you need help with evaluating a similar challenge for your company, and with structuring the plan, value exchange, and operations around it all—feel free to reach out.
