By Thomas Walle, President Unacast
When companies buy data, whether location intelligence, demographics, mobility trends, or something else, they don’t actually want the data. They want the value the data unlocks. And they want that value fast.
That’s where Time to Value (TTV) comes in. It’s the time it takes from when the client signs the contract, ingests the data, and actually starts seeing results. In a world that’s moving faster, where teams are leaner, and ROI is under more scrutiny than ever, we at Unacast are increasingly experiencing how TTV is quickly becoming one of the most important metrics in the data business.
We’ve learned (sometimes the hard way) that having great data and intelligence is not enough. If we don’t help customers see value from it quickly, the relationship starts on shaky ground. And that initial momentum is everything.
Why Time to Value Matters More Than Ever
I remember that a long onboarding process used to be tolerated. It was almost expected as location data was a novel dataset for most. “Give it a few quarters, then it’ll pay off.” That mindset is gone. Today, data buyers want value in days or weeks, not months. And if they don’t see it quickly, you risk churn, delayed growth, or even worse, getting replaced by someone who made it easier.
Faster Time to Value builds trust. It makes your buyer look good internally. It increases expansion potential. And it proves that what you’re selling is not just interesting, but indispensable.
So How Do You Shrink Time to Value?
At Unacast, our Chief Customer Officer, Bridget Anderson, VP of Customer Growth, Ben Erdtman, and their team are doing a heroic job daily with our clients to ensure they see value as soon as possible. This is what we see shrink TTV.
1. Make Ingestion Frictionless
Whether your client is technical or not, getting the data into their systems should be as close to “one-click” as possible. That means:
- Clear documentation and live examples.
- Pre-kickoff alignment on goals, use cases, and success criteria to set clear expectations.
- Consultative onboarding with technical and industry experts to co-develop an effective plan.
- Structured intake process that captures delivery specs upfront to streamline setup and reduce delays.
- Support that doesn’t just “help when asked,” but proactively walks clients through common challenges or industry issues.
Remember: if ingesting your data takes a sprint planning meeting, you're already in trouble.
2. Pre-Build Value Paths
Don’t just hand them a dataset, hand them use cases. What KPIs will this data improve? What business decisions will it power? What charts should they build first?
Ideally, have playbooks by vertical. A retail brand and an adtech firm shouldn’t get the same onboarding path. Show them what “value” looks like in their world.
3. Deliver Quick Wins
There’s almost always a low-hanging fruit, some quick metric they can surface with your data that makes someone say, “Ah, now I get it.” Find that. Lead with that.
A competitive analysis. A simple heat map. A cohort comparison. Whatever it is, show something tangible and visual in the first few weeks.
4. Assign a Value Owner
This is a big one. On your side (or ideally jointly with the client), someone needs to own the outcome. Not the ingestion. Not the troubleshooting. The actual business value. That person’s job is to ask, weekly: Are we seeing results yet? If not, what’s in the way?
Too often, everyone’s doing their part, but no one’s steering toward value.
5. Iterate With the Client
Don’t assume your customer will find all the insights on their own. Schedule working sessions to explore the data together. Share insights they haven’t thought about yet. You’re not just a vendor—you’re their data partner.
Bottom line
If you're in the business of selling data, you're really in the business of delivering value. The faster you can prove that value, the stronger your relationship, the higher your expansion potential, and the more likely you are to become a core part of your customer's ongoing data stack.
At Unacast, we focus a lot on “Time to Value” internally. If our clients aren't seeing meaningful results fast, we haven't done our job—no matter how great our data products are.
