Data monetization is being seen as the next big revenue stream for brands, allowing companies to leverage the data they already to maximize value for the business and its customers.
You might be wondering how data monetization can benefit your organization, and what role a customer data platform (CDP) can play to help elevate your data monetization strategy. Here, we’ll cover some specific strategies, and how a customer data platform can help provide the foundation you need to get started.
Data Monetization Basics
There are two categories of data monetization: direct data monetization and indirect data monetization.
Direct data monetization involves the collection and storage of data before it’s sold to customers. Indirect data monetization involves the internal use of a company’s data to generate business insights.
Direct Data Monetization
The three types of direct data monetization are: Data-as-a-Service, Insight-as-a-Service, and Analytics-as-a-Service.
- Data-as-a-Service: Data-as-a-Service provides access to a set of raw data. The data is neither processed, aggregated nor summarized. Think of it as access to particular rows or columns in a relational database. To make use of this raw data, customers need to provide their own analytics capabilities.
- Insight-as-a-Service: Insight-as-a-Service adds a layer of processing on top of raw data. Insights can include summarized data, analytical insights, and predictive insights.
- Analytics-as-a-Service: Analytics-as-a-Service provides more capabilities on top of what’s available from Insight-as-a-Service. It can include data visualization tools with predetermined dashboards, reports and charts. It can also provide business intelligence tools that enable customers to create customized reports and dashboards.
Indirect data monetization
Indirect data monetization involves broader use cases that are less neatly defined by particular categories.
Let’s consider a few examples.
- Improving patient care: Kaiser Permanente analyzed patient data to improve how its doctors and staff assist them. During the pandemic, usage of their telehealth system saw an all-time high.
- Improving products for a B2C brand: A Japanese retailer wanted to leverage data from its consumer-focused daily necessities site. The retailer launched a marketing lab to make data available to CPG partners selling on its B2C website. The lab used connected first and-second-party data to better understand customers’ perceptions, interests, and buying habits. This helps brand partners understand how to improve products in terms of packaging, pricing, and product extensions.
- Sharing shopping data with merchants and suppliers: Retail giant Walmart has a data platform called Walmart Luminate. It collects and identifies shopper patterns and relays those patterns to merchants and suppliers.
Overcoming Data Monetization Challenges
Now that you understand the basics, how do you get started?
Establish a Data Foundation
The first step is to get your data foundation in order (i.e., your data strategy, design and technical architecture). This foundation helps you build your internal business case, which includes details on the technology platform needed to support data monetization.
A CDP can help companies achieve data monetization success by unifying sources of data across first, second, and third-party sources. Accessing first-party data is a challenge when distributed across different customer touch points, such as websites, apps, social media sites and more. A CDP can not only consolidate information siloed across systems, it can also manage the often-complicated and murky waters of privacy regulations. By sharing this data with trusted partners, you can generate profits, monetize owned data, and improve business relationships.
Develop an Iterative Approach
With technology, recognize that “perfect” can be the enemy of “good.” While technology-related challenges can doom a data monetization initiative before it begins, recognize that these challenges aren’t unique to your business.
Don’t let technology challenges impede your progress to a successful data monetization strategy. Data monetization should be an iterative process – one that’s measured in small steps, rather than a giant leap. Consider a phased approach, where you test the waters first with indirect data monetization. This can serve as a testing ground on internal customer data use cases before you launch services externally via direct data monetization.
Find the Right Partners
Next, look for non-competitive partners in the data ecosystem. Partners can provide complementary applications and tools. Specialized data providers can give you access to unique and proprietary data sets that you don’t have today. Don’t forget your existing customers and partners, who can help augment, enhance, and enrich the data you provide.
Getting Started with Data Monetization
A CDP can unify sources of data across first, second, and third-party sources. A CDP can cleanse and enrich data into unified customer profiles and provide a platform that’s agile and easy to scale with new data sources.
Beyond technology, data monetization success also depends on having the right organization and talent, establishing a data and analytics culture and finding the right partners.
Want to learn more about how to choose the right customer data platform for your organization? Our comprehensive guide explores the key steps needed to create a successful CDP evaluation and selection process – from the capabilities to consider, to the questions you should ask prospective vendors to make sure you’re making the right decision. Access your copy of our guide here.
How to price and package a data product
Knowing that Data-as-a-Service, Insight-as-a-Service and Analytics-as-a-Service exist does not answer the commercial question behind them: what does a buyer pay for, and on what meter? The pricing model decides who carries the risk when usage spikes or when two parties disagree about the value delivered, so it deserves the same design effort as the data product itself. Pick it before the first sales conversation, not during one.
| Pricing model | What the buyer pays for | Works best when | Common failure mode |
|---|---|---|---|
| Flat-fee subscription | Recurring access to a defined dataset, report suite or dashboard | You can draw a stable scope and the buyer wants predictable budgeting | Scope creep — buyers expect broader coverage than the contract names, and support costs climb |
| Usage-based | Metered consumption: per query, per record or per API call | Buyer volume is unpredictable and your delivery costs scale with use | Revenue swings month to month, and a heavy user can cost more to serve than they pay |
| Revenue share | A percentage of the outcome the data influenced, such as sales or media performance | Both parties can measure the shared outcome and agree on whose numbers count | Attribution disputes — decide how influence is measured before launch, not after the first invoice |
Revenue share is the oldest of the three and the model behind most affiliate marketing programs, which makes it familiar to prospective partners even when your data category is new to them.
Two rules keep the packaging honest. Price the decision the buyer makes with the data, not the rows you hand over — a feed that improves media allocation is worth more than its storage cost suggests. And pilot with one buyer segment before publishing a catalog: a data product that nobody renews is telling you the packaging failed, not that the market is absent.
Readiness checks before you monetize customer data
Selling or sharing data is a promise that buyers will build plans on top of it, so the readiness question is not “is our data good?” but “would we stake a contract on it?” Five checks, run before the first sale, prevent the failures that end data partnerships.
- Consent scope — map every field you plan to sell or share to what customers actually agreed to. A consent gap forfeits the trust the whole program depends on, regardless of data quality.
- Quality thresholds — set minimum completeness and accuracy levels per field and measure them continuously. Buyers churn on quality faster than on price.
- Identity confidence — know how each record was resolved and how confident you are in each match. Misattributed profiles poison every insight a buyer draws from your data.
- Provenance — document where each attribute came from (first-, second- or third-party) so buyers can judge whether it fits their use.
- Delivery terms — state in the contract what buyers may redistribute, for how long, and how corrections and revocations reach them.
Start with aggregated, insight-level products and graduate to raw access. Aggregated insights expose less, demand less governance, and reveal what external buyers actually want before you commit to raw-data delivery and its heavier obligations.
How to measure whether data monetization is working
Data monetization fails quietly: revenue arrives, delivery costs grow, and nobody notices a product is subsidizing itself until a budget review ends the program. Decide the metrics before launch, while you can still shape the product around them.
| Metric | What it tells you | Failure mode if ignored |
|---|---|---|
| Revenue per data product | Whether each product covers its share of delivery and support costs | One or two products carry the program while the rest never break even |
| Buyer renewal rate | Whether buyers find ongoing value or made a one-off pilot purchase | Pilots get reported as a durable revenue line |
| Cost to serve | What delivery, support and infrastructure cost per product as usage grows | Margins erode invisibly as consumption scales |
| Time to first insight | How long a new buyer waits before the data is usable | Onboarding friction kills renewals before the relationship compounds |
Indirect monetization needs a different yardstick because the value stays inside the company. Baseline the decision the data should improve — product return rates, campaign performance, supplier retention — and measure against that baseline after launch. An insight that changes no decision is a cost, whatever the dashboard says.
Preparing your data for agent-driven buyers
The next wave of data customers may never log in. Software agents that compare, negotiate and buy on a customer’s behalf — the shift usually labeled agentic AI — consume data machine-to-machine, and that demand reaches data sellers from two directions. Agentic commerce drives appetite for structured product, pricing and availability feeds that agents can act on, while an agentic CDP makes unified customer profiles available to machines through APIs and agent-ready interfaces instead of dashboards.
A data product built only for human analysts fails a machine buyer. Agents need stable, versioned schemas, a documented update frequency, and license terms a machine can parse — a dashboard export satisfies none of that. The fix is unglamorous and effective: publish a schema changelog, commit to delivery schedules you can keep, and attach a machine-readable summary of usage rights to every product.
Keep the terms a human decision and let software handle the consumption. Your team decides what the data may be used for, at what price, and which agents may buy it; the agents handle volume and speed. That division — human judgment on terms, machine execution on delivery — is what turns agent-driven demand into a revenue line instead of a governance incident.
FAQ
Do you need a CDP to monetize data?
No — a CDP is not required, but it removes the two bottlenecks that stall most programs. Direct monetization depends on consented, delivery-ready data, and indirect monetization on insights teams actually use. A customer data platform consolidates first-, second- and third-party sources into persistent profiles and serves them through APIs, which shortens both paths. Without one you can still monetize, but expect slower data preparation and more engineering effort per data product.
What is the difference between data monetization and data sharing?
Data sharing moves data between organizations; data monetization is built to capture value from it. Sharing can be informal — a partner report, a joint dashboard — while monetization treats the dataset or insight as a product with defined buyers, pricing, delivery terms and quality commitments. A practical path is to start with internal or partner sharing, then introduce priced offerings once external demand and your delivery standards are proven.