Like most umbrella terms, data monetization has different meanings for different people. Broadly speaking, it falls into three categories: direct, indirect, and product enhancement.

When most people think of data monetization, they think of direct data monetization: selling data to a third party. However, direct data monetization also encompasses validating, enriching, and adding value to data.

Here are some examples:

• Selling Data: Some companies sell data sets assembled based on consumer research, including customer trends and buying behaviors.
• Validating Data: FedEx’s and UPS’s APIs scan their address databases to confirm customers’ correct shipping addresses.
• Enriching Data: Some companies enrich customers’ transaction data to reveal merchants’ names on bank or credit card statements.

Indirect data monetization — 50–70% of Method’s data monetization use cases — means using your data to create organizational efficiencies and improve your business operations.

Product enhancement refers to meaningfully embedding data into a product or service. It improves the product’s competitiveness, making it more appealing to customers.

For example, Method recently added a data analytics feature to a hospital product popular with pharmacists but unused by their managers. The new feature gave those managers better reporting and insight into their employees’ workdays.

Now that we’ve unpacked the basics of data monetization, let’s dive deeper into each type:

Direct Data Monetization

Developing an effective direct data monetization strategy and finding customers requires a keen awareness of your surrounding data and other people’s data needs.

To get the lay of the land, look at your customers and potential customers in adjacent industries. Also, look upstream at your suppliers — and their suppliers — to determine anyone whose product lines would improve or innovate because of your data.

When choosing a direct data monetization strategy, ask these key questions:

• What data do I have?
• Who would pay for it?
• How much would they pay for it?

AI now plays a significant role in direct data monetization. It scans and synthesizes enormous quantities of customer data, finds relevant behavioral patterns, and recommends actions that improve the customer experience.

For example, Google monetizes customer data but doesn’t target ads one-to-one or give advertisers information about individuals. Instead of randomly serving an ad to one 37-year-old white man, it employs AI tools to pool the data of millions of customers with commonly held characteristics, like 37-year-old white men who make certain Google searches.

Other companies use AI to understand hyper-specific customer populations so they can tailor their products accordingly.

Take healthcare, a fragmented field that spans payers, hospitals, private practices, pharmacies, and pharmaceutical companies. To build relationships with patients and caregivers, healthcare companies must demonstrate expertise about health conditions and the people living with them. To gain that expertise, they need to know their customer data inside and out.

That’s where AI comes in. AI tools that crunch data across thousands of healthcare companies spot patterns, produce patient and caregiver insights, and allow companies to customize their offers and communications.

Indirect Data Monetization

An indirect data monetization strategy focuses, first and foremost, on efficiency.

A strong strategy identifies inefficiencies in your business, which fall into three domains:

• Automation: Automate routine operational tasks that follow set rules and go through unchanging, well-defined processes.
• Decision-making and judgment: AI tools analyze people’s decisions and their outcomes to predict the impact of making decision X versus decision Y. Based on their analysis, the tools help people make better decisions and predictions and reduce human error rates, which is especially beneficial for the underwriting and fraud detection industries.
• Strategic decision-making: AI tools formulate hypotheses grounded in historical and up-to-the-minute data. They then make recommendations supporting data-informed decisions.

Product Enhancement

Product enhancement data monetization looks across your products for opportunities to deploy data to improve customer experience. For example:

• Banks use customer data to send useful insights and updates, such as low-balance notifications, upcoming payment reminders, or trading account fluctuations.
• YouTube TV displays on-screen notifications during college football games, prompting viewers to scroll down and view the game statistics. That way, viewers don’t have to switch between the TV and another screen to access the statistics.
• Healthcare companies show how patients’ health numbers, such as cholesterol and blood pressure, have changed over time. They also contextualize a patient’s health data by comparing it with national averages and healthy ranges for people who share relevant characteristics, such as sex and age.

To differentiate your product enhancement data monetization tools, consider what buyers care about, particularly their margins, efficiency, and cost.

Moving Forward With Your Data Monetization Strategy

As you consider your data monetization strategy, think about what makes your data valuable.

Perhaps your data monetization approach lets you summarize characteristics and present a holistic view of all the data, yielding important insights. Or, it allows you to unify and summarize unique data sets so you can connect the dots between disparate points and draw new conclusions.

Then, look upstream at your suppliers and downstream at your customers for partnership opportunities. Also, enrich your data with open-source data sets.

Seek out data beyond what’s neatly organized in an Excel spreadsheet. For example, you may examine the semi-structured and unstructured data generative AI trains on or the sensor data the industrial sector relies on.

The people who win at data monetization see data everywhere. Do this, and you’ll have a significant long-term advantage over your competitors.

Method supports a wide array of businesses by fleshing out use cases, identifying top business goals and priorities, and outlining, step by step, how to succeed. Eager to craft data monetization strategies and explore future data opportunities? Reach out today.