Kirill Kedrinski - Fotolia
If you are one of many companies evaluating a data monetization strategy you are likely under some pressure, primarily because a data monetization effort is oftentimes perceived as easy. The mission may come from the CEO, the product team or you might be the executive who is championing the effort. You may have heard in your organization that this should be easy -- repackage data, find customers, sell product, achieve success -- mission accomplished.
In our 40-plus years of combined experience we can tell you that it isn't easy; organizations commit vast amounts of time and money, and opportunity cost, when they engage in data monetization for the first time.
Here, we share our experience by describing several key concepts for evaluating data monetization projects. This guidance on the business opportunities, data regulations and economics will provide a head start as you begin your data monetization evaluation process.
Business opportunity evaluation: Thinking beyond the obvious customer
Successful data monetization strategies start with an understanding of the businesses or industries that can benefit from your content. The best data monetization efforts evaluate both the primary customers -- those who are near to you in the industry -- as well as those customers who are adjacent and nonobvious. For example, your primary customer might be a B2B opportunity in the financial services sector, yet in your discovery process you identify a B2B use case in the health or wellness sector. As we have led teams through both primary and adjacent customer evaluations that have uncovered opportunities for business success, the following themes consistently emerge.
- First, speaking with primary potential customers of your offering should be the easiest conversation to have, but it can be the most challenging. Your view of their data needs may not be accurate, so it is important to set aside preconceived notions, obtain an accurate view of their data-centric issues, and validate that your data monetization solution, as it exists or could exist, can -- and will -- provide value.
- Second, the great thing about big data is that it can be nonobvious who might benefit from your offering; an awareness of these adjacent customers is imperative. Identifying the pool of adjacent customers is not a singular event, rather a mindset throughout your organization and the data monetization process to consider what other sectors or customers could see a benefit.
- Lastly, prioritizing opportunities is key. Successful companies often supplement their primary customer focus to work on a potentially lucrative adjacent opportunity. Conversely, there may be so many adjacent opportunities that they distract from achieving success in the primary area of focus; it is important to prioritize opportunities early in the data monetization process, saying "Yes, now" or "No, not yet," and then holding firm to those determinations.
Data regulation evaluation: Assessing the guardrails
To maximize the chances of a successful data monetization project, it is imperative to assess the actual -- not the often misperceived -- data regulations in the relevant sectors, including yours and your potential customers'. For example, data privacy rules or norms that may impede your monetization efforts should be examined early in the evaluative process so there is time to consider or resolve these types of challenges. Similarly, best practices applicable to your business and your potential customers' industries should be part of the early examination process as well, so adjustments can be made to conform to market expectations.
In the United States, data privacy is regulated on a sector by sector basis, and each sector itself is subject to a varying level of guardrails. For example, healthcare data restrictions -- such as those found in the Health Insurance Portability and Accountability Act and the Health Information Technology for Economic and Clinical Health Act -- can be either more restrictive or less restrictive than wellness data restrictions, depending upon specific collection and data use circumstances.
Companies also need to be aware of guidance issued by the US regulatory agencies relevant to their space and the space in which they see potential customers. For example, the Food & Drug Administration has stated it will take a hands-off approach to most medical device data systems, or software that conveys data to and from a medical device -- like a glucose meter, for example.
We have worked with companies that inadvertently found themselves in an unfortunate post hoc situation -- looking back at data regulations after substantial investment in a data monetization project, only to discover their planned uses of data were not allowable under current rules or regulations. Or worse, potential uses and sales were lost because misperceptions, and a failure to understand the regulatory guardrails, led to the wrong choices during the data monetization process.
Cost/benefit evaluation: Understanding data monetization economic analysis
We have seen companies struggle with understanding the entire cost of production. Data monetization efforts don't come with an obvious tangible supply chain, and most companies have a thin track record of assessing cost for bits and bytes. Evaluating the costs and benefits of data monetization efforts begins with two fundamental questions: What will this offering cost us to build and maintain over time? and What pricing and pricing models will our customers accept?
A starting point is to ask these questions internally:
- What extra data science time is required to shape the data to the needs of the potential customer?
- How will that time increase or decrease as the product matures and we get directional feedback from customers?
- What will it cost for the mechanics of delivering this product into the hands of users?
- Does the data monetization effort provide cost recovery for the initial reason, purpose or use of the data, and how can that be accounted for?
On the pricing end, your efforts should be revenue generating. The starting points for discussion about price include:
- What information, such as competitor pricing, do you have to help price your product?
- Can the business opportunity discussions that you have had with your customers provide guidance on price acceptance?
- Does your evaluation of internal cost -- both short- and long-term cost -- set a floor for minimum pricing?
You can make informed projections on price, and the market will tell you if your projections are correct. We believe in pricing experiments and our recommendation is to identify several price points and/or pricing models that you could use. Experiment with these models -- by channel, by delivery vehicle, by customer type -- over a fixed period and evaluate the results.
The data monetization prescription in a nutshell
When evaluating businesses that can benefit from your data-centric offering, and while satisfying your primary clients, it is imperative to think outside your industry. Thinking beyond the obvious customer is a starting point -- not the ending point. Once you have identified your target customer, assess and evaluate -- with a true understanding of data use guardrails --whether and how data privacy rules, norms or best practices will support your monetization efforts. Understanding the overall cost and benefits of a data monetization project shapes an informed business case and avoids a late realization that you have not met business expectations. Or worse, you have not maximized your opportunities with successful data monetization initiatives.
About the authors:
Lydia Jones and Karl Urich together bring over 40 years of experience in data and data privacy. They also have a deep understanding of the complete data monetization lifecycle, enabling them to help companies succeed in getting their data monetization efforts off the ground and to help clients recover when things go wrong. Their methods for assisting companies are tuned for the size, complexity and current state of any company's data monetization project. For more information on their practice, email them at firstname.lastname@example.org and email@example.com.
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