Every enterprise that operates at scale is generating data. Retailers, destination venues, platforms, hospitality groups — the behavioral signals are accumulating constantly. Ticketing, transactions, foot traffic, dwell time, app usage, loyalty interactions. The infrastructure is there. The data is there.
What's missing is not the asset. It's the commercial lens.
I've spent the better part of a career on both sides of this market — helping data companies find buyers at Circana, and building the GTM and pricing architecture for Instacart's enterprise data products business. The pattern I see in organizations that fail to monetize their data is almost always the same: they know they have something valuable, but they've never been forced to answer the three questions that actually determine commercial value.
The three questions that determine commercial value
What does the market actually pay for? Not what you think your data is worth — what buyers have demonstrated willingness to pay for. These are different things. A venue with 10 million annual visitors may assume its audience reach is the asset. But what a CPG brand actually pays for is behavioral signal quality: identified customers, cross-category spend, dwell-time patterns that proxy for purchase intent. Anonymous foot traffic data has value. Identified behavioral profiles linked to transaction history have multiples more.
Who are the non-obvious buyers? The obvious buyers — advertisers, sponsors, data brokers — have already knocked on your door. They've offered commodity rates for commodity access. The less obvious buyers are the ones willing to pay a premium because your data fills a gap they can't fill elsewhere. Real estate investors evaluating retail viability. QSR chains doing site selection. Financial services firms enriching customer profiles. Insurance actuaries modeling behavior patterns. Tourism boards mapping destination demand. These buyers exist. They're just not in your current commercial relationships.
Is your data actually sellable? This is the question nobody wants to ask, because the answer is often uncomfortable. Data quality, completeness, consent architecture, and privacy compliance determine whether a data asset is commercially viable — regardless of how interesting the signals might be. A dataset built on anonymous behavioral tracking has a different commercial profile than one built on opt-in first-party customer profiles. You need to understand what you have before you can accurately represent it to a buyer.
The gap between what enterprises think their data is worth and what the market will actually pay for it is almost always a function of not knowing the buyer — not a function of the data itself being weak.
The anonymous guest problem
One of the most consistent gaps I see in destination venues — retail-entertainment complexes, theme parks, hospitality properties — is what I call the anonymous guest problem. These properties generate extraordinary behavioral data: who goes where, how long they stay, what they do in sequence, how they respond to events and promotions. The sensors are there. The Wi-Fi is there. The ticketing system is there.
But the guest is anonymous. The wristband doesn't know who's wearing it. The ticket was bought through a third-party platform. The receipt was printed and discarded. The data exists, but it can't be linked to a customer profile — which means it can't be sold as an audience product, it can't power personalization at scale, and it can't support the kind of longitudinal behavioral analysis that premium buyers pay for.
The fix is not complicated in concept, though it requires deliberate investment: a loyalty or membership program, a consumer app with real adoption, a payments infrastructure that captures identity at the point of transaction. These are the building blocks of a first-party data asset. And the organizations that build them early accumulate a commercial advantage that compounds over time.
What data monetization actually looks like in practice
The commercialization models that work are rarely the ones organizations initially imagine. Licensing raw data to a single buyer is often the lowest-value path. The higher-value models tend to be:
- Audience products — packaged behavioral segments licensed to multiple buyers, typically at a CPM or annual licensing fee, structured so no single buyer gets exclusivity
- Insights products — aggregated, anonymized intelligence sold as a subscription or project engagement; benchmarking reports, category analysis, demand forecasting
- Strategic partnerships — a longer-term commercial relationship with a buyer who needs your data as a durable input to their own product or model; higher value, higher commitment, but compounding returns
- Tenant and partner analytics — selling data back to the ecosystem that generates it; retailers, F&B operators, and attraction partners who want to understand their own performance in context
The right model depends on the nature of the asset, the consent architecture, the buyer landscape, and the internal appetite for building versus partnering. That's exactly what a data monetization assessment is designed to determine — before you commit to a path.
I've done this work for retailers, data companies, and entertainment enterprises across several decades. The opportunity is real. The market is active. And the organizations that move deliberately — assessing what they have, mapping who will pay for it, and building the first-party infrastructure that makes the asset sellable — are the ones that turn data from a cost center into a durable revenue stream.
If you're sitting on data you suspect has commercial value and haven't been able to fully characterize or monetize it, that's a tractable problem. It just requires the right commercial lens.