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For decades, the phrase “time is money” has been a well-worn, if accurate, figure of speech. That calculus has evolved for most organizations, because in today’s business environment, data is money.
Data is the lifeblood of the modern business because of the extreme value it represents at all stages of commerce. Data about historical sales helps drive decisions about where to invest in the future. Data about how customers use a product helps determine what features to prioritize next. Data about customer demographics and their personal preferences helps direct hypertargeted marketing campaigns, boosting revenues. Put simply, in every area of sales and marketing operations, data has become essential.
Smart business leaders have figured this out. They’re no longer merely collecting data about their sales and customers, they’re actively using that data to create measurable business value and build a competitive advantage.
Broadly, data monetization is the practice of using information to generate measurable economic benefits. More specifically, it involves identifying valuable data and either selling that data to others, using that data for direct marketing or leveraging data-based products to create new revenue streams.
While monetizing data used to be exclusive to the world of tech giants and global conglomerates with sophisticated data science programs, today the world of data monetization has become democratized. Any organization looking to thrive can now create a data monetization strategy, using it to open up new revenue streams, enhance the customer experience or gain a competitive advantage in its industry, whether it’s telecommunications, financial services, media or something else.
So you want to start turning data into value in your own organization? Organizations can take one or more strategic pathways on the road to monetizing data. The correct path depends on your organization’s industry, your business goals and the type of data you have available.
Here are the four primary strategies that organizations can use to monetize their data assets.
Perhaps the most foundational type of data monetization, the simplest method to turn data into money is to sell it directly, for example on a marketplace. Data about your customers has value to other businesses who want to reach them, either in the form of customer records with detailed contact information and data around their purchasing habits — or in the aggregate, where information is anonymized and consolidated through statistical methods.
Raw customer data tends to be more valuable since it can be used as a direct sales lead, but this kind of data is subject to myriad privacy laws globally and may require customers to “opt in” to data sharing. This data can also be anonymized to disguise the actual identity of the customer (data clean rooms can be a helpful tool for this sort of task, for example.) Aggregated data which does not include personally identifiable information about the customer carries less of a compliance risk, but typically also holds less value. In either case, a data monetization market can be helpful as an additional revenue stream.
Data as a Service is an emerging business model that makes data available to customers on demand. DaaS services may be built around similar data types to those outlined in the previous section, but DaaS can offer advantages in both speed and data quality. With DaaS, data is available at the ready for customers to access any time and in an API-enabled format that can integrate directly into a workflow. DaaS systems also often include automated data management tools that improve data integrity and quality over what traditional data platforms (such as CRM systems) can offer.
If you can’t (or don’t want to) sell the data itself, you can analyze that data and sell information derived from it. This is of course the primary business model for analyst firms, which collect data and then sell the reports they generate after analyzing it. Insights into customer and market trends can be of great value to other companies operating in the same industry or a related one.
A final data monetization strategy is to use data to upgrade the products or services you already provide or to improve the way they are sold. This could take the form of creating a personalized product, such as a bespoke fitness program designed for an individual customer based on data about their particular health needs. Or it could include designing upsell/cross-sell activities, such as the data-driven recommendations to purchase related products that are common on online retail websites.
Data monetization does not come without its share of challenges, some of which can be daunting. The most notable challenges include:
Arguably both the most difficult and the most common obstacle to monetizing data, the tangle of privacy laws and regulations worldwide that govern the way customer information is stored and shared can trouble even the most seasoned organizations. Failure to comply with data privacy rules can lead to stiff penalties and loss of reputation — even if the failure is accidental. Ensuring the highest levels of governance over customer data is paramount for any data monetization effort.
How do you know data has value? Many organizations undertake monetization strategies only to find out after substantial work has been done that there’s no market for the data or that the value of the data is less than expected. Understanding whether the end result of a data monetization effort is likely to bear financial fruit must be a key first step in any undertaking.
Many organizations may have a treasure trove of data, only for further analysis to reveal that data is incomplete, poor in quality or stored in inconsistent, incompatible formats. Data that is improperly cleansed and standardized ahead of a monetization effort is likely to lead to a poor end result.
Examples of successful data monetization initiatives are all around us. Here are some key use cases where organizations are finding success with the business strategy.
Retail is one of the heaviest users of data monetization, because the industry habitually collects a large amount of very valuable data. Retailers leverage data monetization daily by suggesting additional items for shoppers to buy after purchasing a product. And those famously long CVS receipts with coupons at the bottom? Those aren’t random — they’re tied to items the customer is likely to purchase next, enticing them to return on another day. Many retailers, especially grocery stores, use loyalty programs to closely track what a customer buys, helping them plan promotions and optimize inventory.
All those credit card offers you receive in the mail aren’t generated arbitrarily. They’re the result of financially driven data monetization strategies that target customers based on an analysis of their purchasing habits and credit history. Data models can help prioritize the customers with the highest potential value and the lowest risk, and financial institutions use this data to target those customers with personalized financial products. Payment processors also commonly aggregate transaction data and sell this information to merchants and manufacturers to help them create targeted marketing campaigns.
Manufacturers can collect data about the way their products are used and sell it to companies in complementary industries. For example, automakers can sell information about driving habits collected by onboard computers to fuel companies and tire manufacturers. An equipment manufacturer can gather information about how its products are performing in the field and use that information to offer service plans designed to provide preventive maintenance.
Regardless of your organization’s industry, data monetization can be a strong benefit — especially if you have the right tooling in place to manage and govern the data you want to monetize.
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