Eight Factors to Consider When Deciding to Monetize Your Company’s Data” explores the idea of monetizing existing data and all considerations when deciding to develop the new revenue stream. Gartner defines data monetization as “the process of using data to obtain quantifiable economic benefit”. This includes internal or indirect methods, such as using data to drive business improvements. It also includes external or direct methods, such as selling the raw data, licensing insights, or developing products/services based on proprietary data. This piece specifically discusses how to seize the ripe opportunity (projected to be as high as $64B in 2024) to leverage data assets externally, while the market for data-driven experiences is hot.
Understanding Data Monetization and its Purpose
With a plethora of data available, there is more opportunity than ever for companies to monetize data to drive revenue rather than simply inform business decisions or enhance customer experiences through this concept of ‘Data Monetization’. In its broadest sense, data monetization boils down to two core concepts:
- Indirect data monetization: Leveraging data to drive business improvements
- Direct data monetization: Leveraging data to drive additional revenue through either:
- Selling data itself
- Licensing data and insights
- Developing products or services predicated on unique proprietary data (e.g. Retail Media Networks, Predictive Models or Services, etc.)
Investing in direct data monetization is incredibly compelling for three main reasons:
- Low Cost of Goods Sold: Selling an asset already purchased seems like a no-brainer. It’s already owned and maintained, and the incremental costs to move forward with a data monetization strategy will be very low
- Scalability: Data is a product that is inherently scalable. This is because it can be used by multiple parties with a minimal expense for repackaging. In other words, you can and should sell the same data (or derivative of the data) over and over again with ease
- Uniqueness: Unique data attributes can create competitive advantage and value for organizations that leverage it. Direct data monetization positions you to capture that value as profit for your organization.
While the size of the prize is growing, so is the weight of the financial investment that comes with monetizing data. This high level framework is meant to discover if your business is ready for a data monetization transformation. Our approach is anchored in eight key considerations. Though the complexity will vary from case to case, this framework can serve as a compass through any uncharted, ever-evolving space.
Assess the Opportunity
Data Monetization Strategy and Value Proposition:
Having a clear vision around the value proposition should be the first step. This is true, whether it’s developing a new product, or selling the raw data itself. Not having a clear value proposition, could be an indication that monetizing the data is not a great opportunity at this time. Narrowing the exact value and purpose is crucial, since data is not an easy market to size. Before getting into a model for the value proposition, understanding how the data will be packaged is key. Because the value lies within the data itself, there can be several strategies for packaging the data. This includes selling it raw, licensing data and insights, creating a product, etc. All of which will affect both the opportunity size, cost, and risk factors.
A simple model for the value proposition is to distinguish between the ‘value as product’ and the ‘value to your customers’. For product value, think of net new value-adds the product will be bringing to market. Examples could be specific pain relievers, or core product features. For customer value, one must switch to a consumer mindset. Consider their current pain points, day-to-day value adds, and how it will help them achieve their goals.
Total Revenue Forecast:
Understanding the revenue potential for monetizing data will likely be complex and difficult to properly estimate. When thinking about standing up a product/service that either productizes the data or creates a service based on leveraging the data, the revenue forecasts will be harder to project.
There are 3 high level steps to completing an accurate revenue forecast:
- Craft market sizing model methodology: The methodology includes establishing:
- Formulas: CAGR, pricing, total customer base, revenue, etc.
- Data Sources: NAICS, comparable company income statements and pricing, Bureau of Labor Statistics, etc.
- Assumptions/Variables: Variables: Industry Growth, Inflation, Adoption Rate, Revenue Growth Rate, Pricing, Customer Size, Churn, etc.
- Collect data and conduct research: This step will capture all model inputs and provide data to back assumptions made in the model.
- Develop model and sensitivity analyses for different scenarios: Combine the formulas and model inputs to create a comprehensive view of what the revenue potential is. Including a sensitivity analysis will showcase what might happen to the revenue potential when a variable is changed.
- Craft market sizing model methodology: The methodology includes establishing:
Monetizing data poses an exciting opportunity filled with high potential to generate significant amounts of revenue for your company – be realistic about the size, understand the opportunity at hand.
Evaluate Cost and Risk Factors
Technology:
Monetizing data requires an investment in technology (either licensing, renting, or building) in order to protect data integrity, accuracy, efficiency, and security. However, before you think a massive investment and effort is required, chances are you already have some of these technologies accessible to leverage. The investment in technology also depends on the type of data monetization being pursued – selling data on a third party marketplace (e.g. Snowflake’s Data Marketplace) will require a much lower amount of investment in technology than standing up a retail media network.
Some foundational technology investments you may need to consider are tools that support: data collaboration (i.e. data clean rooms), data analytics, data security and privacy, data integration, data governance, and of course, cloud computing (all of which will likely have ongoing operating costs to be aware of as well).
Talent:
Investing in talent is crucial when monetizing data. On the technical side of the business, skilled professionals with expertise in data analysis to extract insights from data and develop innovative products or carry out services to clients are a must. Additionally, investing in ongoing training and development for employees ensures they have the skills and knowledge necessary to work with new technologies. On the business side of monetizing data, strategic leaders are of the utmost importance. Monetizing data typically proves to be its own business with its own business talent needs. Structuring the organization to effectively develop a product or service will lean heavily on strategic talent to connect the core business and the new business to capitalize on synergies.
Compliance:
It’s clear that data privacy and compliance is top of mind. There are 5 new state laws coming into place in 2023, with a 6th coming in 2025. Data monetization comes with added complexity in the processing and handling of the asset. Because of this, your business susceptible to data privacy risks if proper preparation is not carried out. How one leverages customer data has a profound impact on the relationship between businesses and their customers. This also influences sales and other business outcomes outside of data monetization. Businesses should factor in the full suite of compliance costs. This includes but is not limited to legal consultations, enhanced data privacy technology infrastructure, and talent to oversee the businesses data compliance program. One should also be thorough in the strategic communication of how data is being collected, processed, and monetized. Always remember to emphasize how important consumer privacy and compliance is to the integrity of your brand.
Curate the Business Case
ROI Analysis:
Break-Even Point:
Identifying the estimated timeline and necessary milestones to realize a return on investment is crucial. This is especially true if you are needing to develop a business case for senior leadership. To determine the break-even point, one must calculate an accurate revenue forecast and cost estimate for total ownership over the asset’s shelf-life.
At a high level, the break-even point can be summed up by the formula:
Break-Even Point (Units) = Fixed Costs / Contribution Margin.
Contribution Margin = Sales price per unit – Variable cost per unit
To successfully create a timeline for a break even point, number of units sold need to be aligned to a timeframe. For example, if 200 new units are sold every month and the break-even point is 800 units, the break-even point will occur in four months. The example broken down in a formula to determine the timeline would be something like this:
Ttotal = (Total # of units to break-even) X (Tunit / UnitsT)
Tunit = Specified unit of time (e.g. one month)
UnitsT = Units sold within the specified unit of time (e.g. 200 new units)
Ttotal = Total time to break-even (e.g. four months)
This is a great opportunity to set expectations and align on clear business goals for the data monetization strategy. Additionally, one can easily demonstrate the value that this will bring to the company. It’s important to note, that the break-even timeline should be reviewed regularly. While it’s typical to see monthly or annually, the cadence will hinge on business preferences. Core stakeholders should account for actual product usage and market conditions.
Positive Externalities:
New data infrastructure arguably provides value in other areas of the business as well, and is certainly a variable to consider when developing a business case and determining ROI. Here’s a simplified example that might help paint a clearer picture:
A baby diaper company, BabyCo, has valuable new parent data and are looking to monetize the data via licensing insights. In order to do this, an investment in new analytical infrastructure and talent is necessary. Because of the investments made, BabyCo is now able to sell licenses for insights on the new parent market. Additionally, they’re able to build stronger predictive models and improve the bottom line for the core business of selling diapers. The revenue generated from the number of licenses sold as a part of the new data monetization strategy is the primary section of their ROI analysis. Additional parts of their ROI analysis, looks at how the investments generate bottom line efficiencies in BabyCo’s core business.
Keeping in mind the positive externalities that stem from the initial investment in the data monetization strategy are key to accurately determining the ROI.
Profit Margin:
Though monetizing data can require significant initial investment, broadly speaking, it is a high profit margin business. In addition to low operating costs, the real value comes from existing data, which does not need to be purchased. Therefore, it does not count towards the cost of goods sold because it’s already owned. Most retail media networks operate at 50-70% margin – a number that is hard to ignore. However, it’s more than simply driving revenue with lower costs. It is about the future growth potential made possible by an additional funding source. Monetizing data should not only be looked at as an alternate revenue stream. Position data monetization as a dependable funding vehicle to unlock cutting-edge innovation in other areas of your business.
Opportunity Costs:
Reframing the decision through the cost of not taking action is a great way of putting business decisions into perspective. Because data is a foundational capability for business operations, utilizing data monetization can expand data capabilities and technology. Understanding the opportunity cost is more than looking at lost potential revenue. The value of technological innovation is a core component of the opportunity cost that should be considered as well. A mature data infrastructure, with new technological capabilities can unlock synergies across lines of business that previously didn’t exist. The implications of executing a data monetization strategy span farther than revenue. Exploring this realm can play a key role in developing the business case.
Deciding whether to monetize your business’ data is no small task and involves in-depth evaluations of various risks and costs associated with monetizing data. The benefits can outweigh the risks if the strategy is evaluated and implemented correctly. Assessing the value of the data, identifying the optimal way to monetize, and understanding potential data privacy risks and upstart costs is key to maximizing the potential of your data assets. Monetizing your data is a ripe opportunity to add substantial value to your business – make sure you seize it.
How Transparent Partners Can Help
Navigating the complex organizational, technology, and regulatory requirements related to data monetization can be scary – Transparent Partners simplifies the complex and works to unlock the full potential of your data assets and generate new revenue streams. If you’re interested in learning more, visit our solution page for more insights and approaches.
Eric Zalewski, Sr. Analyst, Data & Analytics
