With persisting and rising inflation leading to a slowdown in consumer spending, businesses are discovering new ways to generate revenue and maximize their marketing spend. Although still in its infancy, data monetization is a way for companies to maximize the value of their existing data to create new revenue streams. For specific tactics to get started, see Top Data Monetization Strategies: Maximize Value with a CDP.
Data Monetization: A New Way to Grow Revenue
Data monetization is an emerging discipline that is experiencing rapid growth. A report published by Allied Market Research shows the global data monetization market was valued at $2.1 billion in 2020, with a projected compound annual growth rate (CAGR) of 22.1 percent from 2021 to 2030. A McKinsey Global survey found that respondents at high-performing companies “are three times more likely than others to say their monetization efforts contribute more than 20 percent to company revenues.”
How Prioritizing First-Party Data Enables Data Monetization
Forrester defines data monetization as “the process of leveraging your data to produce insights and decisions that grow revenue and improve your business.” Fast-growing businesses are using their first-party data to improve their business operations, understand audience behaviors, optimize business offerings, and develop more personalized experiences.
As a cookieless future looms closer, successful businesses are those with a plan to prioritize first-party data to monetize it. Customers value transparency and privacy. At the same time, customers are more likely to make a purchase from a brand with personalized experiences. You can only achieve that personalized experience by having access to data that a CDP centralizes for you and makes readily available.
By taking action on insights from first-party data, brands can differentiate themselves from the competition as well. For example, Kaiser Permanente used its data insights about patients to improve how doctors and staff assist them.
With the depreciation of third-party cookies and a growing number of global and U.S. data privacy laws and regulations, collecting and managing first-party data is more critical than ever. Taking a consent-first approach to consumer data collection also builds a more secure relationship and allows you to end a reliance on third-party data companies.
By prioritizing first-party data, businesses fill data gaps and centralize information to gain profits internally and externally. Given that data monetization depends on the ownership of first-party data, early success stories come from businesses that have improved their data collection efforts without relying on a third party in the middle.
Data Monetization Success Story: Walmart
Walmart is another example of a data monetization success story. Their data platform, Walmart Luminate, “collects and identifies shopper patterns and then relays those patterns to the company’s merchants and suppliers.”
Walmart’s data monetization is an example of how external or direct monetization, done by giving second parties or partners access to information, can benefit your business. Businesses have more reliable information because their data is directly connected to customer behavior and activities. Giving business partners access to data-based products or services allows you to partner with them to advertise better, target, and engage with particular audiences.
A Centralized Data Management System is Essential for Data Monetization Success
A centralized data management system is a key element of your data monetization success. Before you can increase revenue through data monetization, you must effectively use your data to improve your business from within. In an article for Data Science Central, author Bill Schmarzo writes that data monetization is a waste of time for most companies unless their Data and Analytics functions have a seat in the C-suite.
Accessing first-party data is a challenge when distributed across different customer touch points, such as websites, apps, social media sites, and more. A customer data platform (CDP) can not only consolidate information siloed across systems, but it can also manage the often-complicated and murky waters of privacy regulations.
A CDP allows you to make better business decisions by bringing in missing pieces that marketing, sales, and customer success previously had to live without. By cutting out the middleman of third-party data, these teams can make improved business decisions with measurable outcomes.
A centralized data management system allows you to work with your vendors, suppliers, and partners to successfully build better marketing campaigns, personalize customer experience, and improve product offerings. By sharing data, you can generate profits, monetize owned data, and improve business relationships.
Which data assets are worth monetizing first
Most teams exploring data monetization start with the wrong question: what could we sell? The question that matters is what someone would pay for repeatedly, because external data products survive on renewals rather than one-time deals. Test any candidate asset against five requirements before investing in it:
- A named buyer. “Marketers” is not a buyer; a category manager at a supplier, a media planner, or a site-selection team is. If you cannot name the person who would use the data and the decision they would make with it, you have a dataset, not a product.
- Permission for the specific use. Consent and your privacy commitments have to cover the use you plan to sell into. Data collected to fulfill an order cannot simply be repackaged for audience targeting without revisiting how it was collected.
- Scarcity. If a buyer can assemble the same picture from public sources, its price trends toward zero. Signals only you can observe — transaction outcomes, service interactions, product usage — hold value because competitors cannot replicate them.
- Freshness and coverage. A dataset that goes stale within weeks, or that covers a thin slice of its market, pushes you into one-off sales and constant manual reconciliation.
- Deliverability. Someone has to package, update, and support what you sell. If every delivery would be a bespoke engineering project, the unit economics will not survive contact with real customers.
| What to establish | Why it matters | Failure mode |
|---|---|---|
| A named buyer and the decision they make with the data | Products built for a workflow renew; products built for a persona sell once | A polished dataset with no market, and engineering effort spent on revenue that never arrives |
| Consent coverage for the exact use case | Permission gaps surface late, when they are most expensive to fix | A product recall after launch, refunds, and lasting damage to customer trust |
| Freshness commitments you can honor every cycle | Buyers build their own processes around your update schedule | Buyers quietly stop trusting the feed, then stop renewing |
| A repeatable packaging and delivery pipeline | Repeatable delivery is what turns a dataset into a product | Every sale becomes a custom project, and margins disappear into delivery work |
| Differentiation from what buyers can get elsewhere | Overlapping data is a commodity, and commodities compete on price alone | Steady price erosion until the product no longer covers its own costs |
Audience data is the most common starting point because packaged segments plug directly into planning and activation on an ad exchange. Be honest about exclusivity, though: audience segments overlap heavily across providers, and sophisticated buyers price accordingly. Signals that only your business can observe — why customers leave, what they buy together, how they behave after purchase — are harder to copy and easier to defend.
How companies charge for data products
Deal structure decides whether monetization compounds or stalls. Three structures cover most data products, and each fails in a predictable way when matched to the wrong buyer:
- Subscription access. The buyer pays a recurring fee for ongoing access to a refreshed product. Establish the refresh cadence, the support commitment, and what happens when data arrives late. The failure mode is the download-once buyer who cancels at renewal because a single export gave them everything they needed, which is why subscriptions work best when the data keeps changing and the product keeps adding decision value.
- Usage-based pricing. The buyer pays per query, per record, or per credit. Establish metering the buyer can audit and a cost model they can predict. The failure mode is bill shock: a buyer who cannot forecast the bill caps usage below the point where the data delivers real value, then blames the product.
- Value share or partnership terms. Instead of cash, data improves commercial terms — co-marketing funds, better wholesale conditions, or shared category insights. Establish how both sides will measure the value created. The failure mode is ambiguity: each partner claims the gains, the numbers never reconcile, and the arrangement ends quietly at the next contract review.
| Structure | What to establish | Failure mode |
|---|---|---|
| Subscription | Refresh cadence, support scope, and a reason the buyer needs ongoing access | Download-once buyers who cancel at the first renewal |
| Usage-based | Auditable metering and a predictable cost model for the buyer | Bill shock that drives buyers to cap usage below real value |
| Value share | A written, shared definition of how created value gets measured | Gains nobody can attribute, eroding trust at every contract renewal |
Match the structure to how the buyer consumes the data: steady operational use suits subscriptions, experimentation suits usage-based pricing, and strategic partnerships suit value share. Switching structure later is expensive because buyers have already integrated your data into their own systems and budgets, so decide deliberately before the first contract, not after.
Why data monetization programs stall
Most programs do not fail on strategy; they stall on operations. Five mechanisms account for most of the damage, and each has a known fix:
- Data quality treated as a back-office concern. External buyers judge data the way they judge any product, and a single inconsistent field in a delivered file can cost a renewal. Publish known limitations alongside the product and treat quality checks as part of delivery, not as a cleanup project after complaints.
- Consent drift. Data collected years ago under older permission models may not support newer use cases. A consent review has to happen before a new data product launches, not after a customer questions it.
- Bespoke datasets instead of a catalog. Teams that hand-build a dataset per deal never accumulate reuse. Build a product catalog with documented schemas, refresh schedules, and delivery methods, so each new sale assembles from parts instead of starting over.
- No accountable owner. When monetization is a side project inside the analytics team, it loses every internal priority fight. Name a product owner who is measured on renewals and buyer outcomes, the same way any other product line is run.
- Measuring outputs instead of outcomes. A dashboard counting published datasets says nothing about value. Renewal rate, expansion within existing buyers, and buyer-reported impact reveal whether the program works.
None of these is a technology purchase, which is exactly why they persist after the tooling is in place. They are operating disciplines, and they are what separates a data program that produces revenue from one that produces slide decks.
Machine buyers are the next data customers
Data products have historically been designed for human analysts: dashboards, spreadsheets, and decks. That assumption is weakening as software agents take on more buying decisions. In agentic commerce, AI agents research options, compare terms, and complete purchases on behalf of the people they represent, and they can only act on data that is structured, current, and licensed in a form they are allowed to consume.
The shift reaches the supply side too. An agentic data platform can prepare, package, and deliver data products with far less manual work, which changes the economics of serving many smaller buyers instead of a few large ones. Three adjustments follow for a monetization roadmap:
- Schemas over slides. Agents integrate with documented, stable schemas. A data product explained only in a sales deck is invisible to a machine buyer.
- Machine-readable licensing. Usage terms — permitted uses, retention limits, redistribution rules — need to be expressed precisely enough that an agent, or the system behind it, can evaluate and respect them.
- Provenance as a feature. Buyers increasingly need to know where data came from and under what consent it was gathered, and they want that answer in a form they can verify, not in a pitch conversation.
None of this replaces the fundamentals above. Demand, permission, freshness, and deliverability decide whether a data product deserves to exist; the agentic shift decides what interface it must present to survive its next renewal. Building for machine consumption early — stable schemas, explicit terms, verifiable provenance — costs little now and avoids a costly retrofit later.
FAQ
Is selling customer data the only way to monetize it?
No — direct data sales are one route among several, and rarely the first one that pays. Monetization covers any use of data that grows revenue, including internal uses such as sharper targeting, retention, and product decisions, plus indirect routes like richer partner collaborations. Internal use usually funds the foundation — clean, centralized, consented data — that any later external product would need anyway. Treat direct sales as an expansion stage, not the entry point.
Can a small business monetize its data?
Yes — although the first returns for a smaller business come indirectly, not from selling data. A concentrated customer base makes first-party data immediately useful for retention offers, assortment decisions, and win-back campaigns that lift revenue before any data product exists. They can also trade value through partnerships, exchanging segments with a complementary, non-competing brand under clear consent terms. Direct sales make sense once data is centralized, permissioned, and refreshed on a schedule buyers can rely on.