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A Guide to Data Monetization, Part III: How to Craft a Direct Data Monetization Strategy

By: Robert Wood


In the first and second installments of this three-part series on data monetization, I explored the common benefits companies achieve through data monetization and the steps companies should take when crafting indirect data monetization strategies. In this final installment of the series, I explore direct data monetization strategies.

In short, direct data monetization efforts are comprised of externally focused strategic initiatives that companies use to generate additional revenue through the sale of raw, packaged, or aggregated/benchmarked data. These initiatives require strong, accountable leadership and creative solution design, as well as new internal skills and technologies.

To ensure these varied requirements come together in an effective way, a company should take the following five steps when crafting its direct data monetization strategy:

1. Inventory and evaluate all available data.

Inventorying your data and estimating its market value is one of the most critical steps in direct data monetization. A holistic understanding of the data you are able to offer helps determine the breadth, depth, and scope of your product offerings. It also helps you identify your target audience.

It can be useful to treat the data inventorying process as an opportunity to perform additional validations of your data’s accuracy and reliability. You do not want to sully your company’s reputation by creating a data product that is of lesser quality than your other products and services.

2. Find customers and survey the marketplace.

Once you know what data you have at your disposal, determine who might benefit from it and how much value it could bring them. Chances are, plenty of companies could benefit from the data your company already has on hand — even if the identity of these companies may not be immediately apparent.

Large, general datasets often have broad appeal to companies outside your industry. As such, developing a clear understanding of the market value of your data beyond your typical operating sphere can help guide your overall direct data monetization strategy.

3. Identify the level of analysis required to maximize market value.

While raw data can be valuable to many companies, realizing this value requires certain talents, technologies, and capabilities. If your company has these qualifications, consider performing your own analysis, aggregation, and stratification to generate useful insights from your raw data — this can often significantly increase the market value of your data product. Conveniently, this approach often requires your sales and marketing teams to make less of a paradigm shift in their work, as they will be able to frame your data analyses similarly to how they frame other, more traditional products.

Further, in many cases, internal data can provide useful benchmarks for other companies operating in the same (or similar) industries. Having data from another company provides significant value because it allows for the validation and triangulation of market trends, economic sentiment, and the general operating environment.

4. Select a delivery model that aligns with capabilities.

Depending on the core capabilities and resources a company has at its disposal, it can choose one (or more) models to deliver its data product. The three basic types of delivery models are the premium service model, the “freemium” service model, and the syndication model. It is highly advisable to choose the model that aligns best with your existing capabilities, as this is the model that is most likely to be embraced and supported by stakeholders across your company.

The premium service model involves selling analysis as a product. This analysis is characterized by its readiness to be consumed by end users. Often administered through a software as a service model, this direct data monetization approach takes the shape of various reports and analyses that your company creates using internal data (occasionally bolstered by external data when appropriate). Customers may pay to access these reports and analyses on a one-time or recurring basis.

The “freemium” service model is similar to the premium service model except it comes at zero cost to customers. While this may seem counterintuitive in the context of a direct data monetization strategy, this model’s value takes the shape of increased brand loyalty, market differentiation, and/or heightened switching barriers.

Finally, the syndication model is the model most people conceptualize when they think of “direct data monetization.” In this model, data is made readily available, distributed, or pulled on demand, though it may have been transformed from its raw state in some way — think: de-identified, aggregated, or segmented. Many professional research organizations like IMS and Nielsen prefer this model, as it allows data to be transferred via individual reports and data files or accessed through an in-house API.

5. Engage in rapid prototyping and testing.

Before you commit all your resources to one model, it is important to make sure the model is a viable, sustainable data monetization option. Rapid prototyping and market testing can be tremendously useful for making such a determination.

Rapid prototyping is an agile process for quickly developing and iterating on a solution in a short time period, incorporating modifications and enhancements based on user feedback and analysis. Similarly, rapid market testing is vital to ensuring that the data product you are developing satisfactorily delivers the intended value to your customer base. Through the incorporation of immediate feedback, you can alter the course of design or the fundamental capabilities of your product to increase its perceived value in the marketplace.

Data Monetization: A Fixture of Tomorrow’s Business Landscape

As this three-part series has illustrated, there are a variety of ways for companies to convert exploitable data into increased revenues and profits. Whether through indirect means, direct means, or both, companies angling to establish and maintain competitive advantages in our age of data would do well to make data monetization a central part of their overall business strategies.

The good news is that most companies are already awash in all the data they need to do this. With the right strategic guidance and expertise, they have a clear path to realizing the full potential of their highest-volume asset.

This is part three of a three-part series on data monetization. Read part one and part two.

Robert Wood


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