Data monetization is rapidly garnering attention and even urgency as a strategic business opportunity. In and of itself, data monetization is not always well-defined or fully understood.  A recent story involving Allstate Insurance provides a great example of both the possibilities and the fault lines in developing data monetization initiatives.  In a Chicago Tribune article published on June 18, 2015, “Insurer monitoring your heart rate? Allstate's patent makes it possible,” reporter Becky Yerak wrote:

“Northbrook-based Allstate, which last month floated the idea of one day selling the information it collects from policyholders' connected cars, was issued a patent earlier this month for a driving-behavior database that it said might be useful for health insurers, lenders, credit-rating agencies, marketers and potential employers.

Allstate's patent also said the invention has the potential to evaluate drivers' physiological data, including heart rate, blood pressure and electrocardiogram signals, which could be recorded from steering wheel sensors.”

Several insurance companies including Allstate already offer voluntary driver discount/sensor data programs to monitor driving habits with plug-in devices or mobile apps. Allstate’s now-patented breakthrough adds a driver’s physical data to the mix courtesy of a steering wheel device. The central debate in the Chicago Tribune’s piece revolves around consumer advocacy groups’ concerns about the ethics of more intrusive consumer tracking devices that record consumers’ locational, behavioral, and even health data.  Some maintain these tracking methods could lead to adverse financial consequences for drivers, while others submit that tracking driver data has net societal value for improving public health and safety.  What can be said, however, is that Allstate’s activities illustrate clearly high-potential data monetization efforts that could have enormous impact on its organizational goals and profits, however indirect the route. 

Conventional Data Monetization

First, collecting and selling data as a primary product is not really conventional data monetization.  Conventional data monetization requires developing a vision of why data related to an organization’s normal day-to-day activities can have value to outside parties. Nearly every industry is a candidate for “conventional” data monetization. 

Take, for instance, hardware and software technology providers who can track which customers are buying more, or less, of their products and which types of products are being purchased.  Technology utilization becomes an excellent harbinger of where and why specific industries are making investments, putting technology providers in an excellent position to 1) drive leads or deliver applications in areas where they will never otherwise likely participate themselves, and 2) cross-pollinate advances in one industry across to other industries.  This path of reasoning translates across almost every industry out there.

Intrinsic Data Monetization

Today, a firm that plans to sell its own data often makes the news—but a company doesn’t need to explicitly sell its derivative data to create profits.  It’s early on, but Allstate appears to be using both conventional and intrinsic data monetization strategies.  Intrinsic data monetization can have equal or even greater measurable increases in profit, focusing first on opportunities and understanding how these opportunities can be driven by data. The financial and other concrete business impacts of these efforts are then monitored on a regular, quantitative basis.

Traditional BI applications paint with a broad brush to serve many organizational needs, but intrinsic data monetization turns the lens directly on impacting the bottom line.  Enterprises use data to add intrinsic value to their own customers, leading to more sales of their core products:

  • Determine profitable R&D investments;
  • Identify potentially lucrative emerging markets;
  • Expand underserved niches;
  • Create beneficial partnerships and joint ventures. 

Running a business is much more complex than just selling services and products.  Intrinsic data monetization can decrease costs, optimize investments, mitigate risks, and enhance efficiency and effectiveness. As long as the use of data can be concretely connected to improved profits, revenues, and costs, it becomes data monetization.

Lead Generation

Lead generation differs from other forms of data monetization due to its singular focus—leads are not necessarily a derivative of doing business, they are an input to doing business. The internet offers multiple ways to cost-effectively generate leads.  One crucial modern facet of lead generation involves using the internet and mobile devices to enable would-be customers and prospects to self-select or be laser targeted in timely, specific situations.  Instead of employing more traditional, costly methods of seeking leads through human contact, organizations offer consumers the chance to “raise their hands” by clicking on links to express interest. This method provides organizations the means to offer targeted products or services that pertain directly to consumers’ immediate needs.

Lead generation can also be directly enhanced through Search Engine Optimization (SEO).  Consumers are flooded with options once they know what they want to do.  Crawling to the top of the pile and being the first or nearly the first to make a timely contact is both an art and a science.

Generating New Data, and Also New Value

There are myriad opportunities to immediately complement and expand an organization’s current data trove to achieve both internal intrinsic value and create a more external, sellable data commodity.  Examples include: 

  • Valuable public/government information, often freely available on the internet,
  • Public data that is not made easily electronically available—construction permit data, B2B data and even many industry specific business demographics.
  • Competitive intelligence—for instance, HealthCare and Higher Education can tap rich sources of detailed data without violating privacy rules or the public trust.
  • Collaborative repositories, like credit bureaus, that can provide data to identify consumers’ overall financial behavior.
  • Social Media data sources which afford insights and perspectives virtually unavailable elsewhere. 
  • Primary data and residual customer and prospect touch data which could dwarf conventional data collection in the not-so-distant future.  The technologies to capture and manage these data already exist. 

Delivering Data Monetization to Extract Explicit Value

As the Allstate story suggests, one of the key challenges in data monetization is in delivering data value without offending existing customers.  Sensitivities vary by situation and industry, but existing customers often conclude they are being charged for their own information or that the new, would-be data provider somehow owes them insights as standard course of doing business.

  • Traditional Methods:  There are many ways to deal with these challenges. Here, data monetization delivers data directly or only in a slightly modified form from the original business processes employed by the would-be data seller.
  • Data Summaries:  Data summaries not previously available may now be possible simply because the company seeking to monetize its data achieves unique insights simply as a matter of conducting their core business. Existing customers aren’t buying their own data. Rather, they purchase information that reflects a cross-section of activity among multiple players in their industry.
  • Applications/Dashboards:  Data can be monetized inside an application, where data are not delivered raw or simply summarized. The data are built into a more sophisticated delivery vehicle unique to a business unit, or to address common challenges and needs across specific industries. Buyers of this data are not only getting a unique view of the data—they are getting value-added insights that would not have been otherwise available to them.
  • Consulting:  If an organization has unique knowledge and skills that can be supplemented and enabled by their data, then that data become a necessary ingredient in a value chain, thus contributing to incremental profits.  This data is delivered as part of a solution with an altogether different value and pricing proposition.

Effectively Targeting and Selling Data

After determining which data to deliver at what level of potential value, an organization then needs to consider how to best target, produce, sell, and deliver its new capability.  Again, there are many ways to achieve this end:

  • Use Internal Resources:  Some Type-A organizations want to handle all the steps using their own resources.  When going this route it’s usually wise to set up a separate cost center.  Data monetization efforts do not fare well as secondary sources of value and profit if the core business is focused elsewhere.  This organizational structure also makes it much easier to quantify the value of selling data externally.
  • Joint Venture:The next level of maintaining direct control is a joint venture.  Choosing a partner with mutual and appropriate interests can allow two or more organizations to draw on each other’s strengths.
  • Third-Party Royalty Arrangements:  Third-party scenarios can be used to extract the external value of the data while making very few internal process changes.  Some of the data’s intrinsic value must be shared, but these types of arrangements remove nearly all of the infrastructure limitations and these types of partners are much better equipped to sell and deliver data cost-effectively.


The take-aways are threefold:

1. Data monetization is real and presents a strategic opportunity for many organizations. However, it requires planning in advance and initial investments and nurturing.

2. Data monetization means different things to different companies. The only common denominator is that if it does not generate marginal, measurable impacts, it is not really data monetization.

3. There are seldom two data monetization opportunities that are directly parallel. Even if two organizations generate similar data with highly correlated customer targets, the ways in which the culture and management of a given company will tackle these opportunities will likely result in different end-state delivery processes.

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