The Key to Data Monetization

While I have talked frequently about the concept of Analytic Profiles, I’ve never written a blog that details how Analytic Profiles work. So let’s create a “Day in the Life” of an Analytic Profile to explain how an Analytic Profile works to capture and “monetize” your analytic assets.

Many organizations are associating data monetization with selling their data. But selling data is not a trivial task, especially for organizations whose primary business relies on its data. Organizations new to selling data need to be concerned with privacy and Personally Identifiable Information (PII), data quality and accuracy, data transmission reliability, pricing, packaging, marketing, sales, support, etc. Companies such as Nielsen, Experian and Acxiom are experts at selling data because that’s their business; they have built a business around gathering, aggregating, cleansing, aligning, packaging, selling and supporting data.

So instead of focusing on trying to sell your data, you should focus on monetizing the customer, product and operational insights that are gleaned from the data; insights that can be used to optimize key business and operational processes, reduce security and compliance risks, uncover new revenue opportunities, and create a more compelling customer and partner engagement.

For organizations seeking to monetize their customer, product and operational insights, the Analytic Profile is indispensible. While I have talked frequently about the concept of Analytic Profiles, I’ve never written a blog that details how Analytic Profiles work.  So let’s create a “Day in the Life” of an Analytic Profile to explain how an Analytic Profile works to capture and “monetize” your analytic assets.

  Analytic Profiles provide a storage model (think key-value store) for capturing the organization’s analytic assets in a way that facilities the refinement and sharing of those analytic assets across multiple business use cases. An Analytic Profile consists of metrics, predictive indicators, segments, scores, and business rules that codify the behaviors, preferences, propensities, inclinations, tendencies, interests, associations and affiliations for the organization’s key business entities such as customers, patients, students, athletes, jet engines, cars, locomotives, CAT scanners, and wind turbines (see Figure 1).

Analytic Profiles enforce a discipline in the capture and re-use of analytics insights at the level of the individual key business entity (e.g., individual patient, individual student, individual wind turbine). The lack of an operational framework for capturing, refining and sharing the analytics can lead to:

Let’s see how an Analytic Profile works.

  Let’s say that you are the Vice President of Analytics at an organization that tracks individual purchase transactions via a registered on-line account and/or a loyalty program (e.g., retail, hospitality, entertainment, travel, restaurant, financial services, insurance). You’ve been asked to apply data and analytics to help the organization “increase same location sales” by 5%.

After executing a Vision Workshop (very smart move, by the way!) to identify, validate, prioritize and align the business stakeholders around the key business use cases, you’ve come up with the following business use cases for the “Increase Same Location Sales” business initiative:

  To support the “Improve Campaign Effectiveness” use case, the data science team worked with the business stakeholders to brainstorm, test and confirm that they needed to build Demographic and Behavioral Segments for each individual customer. The Demographic segments are based upon customer variables such as age, gender, marital status, employment status, employer, income level, education level, college degrees, number of dependents, ages of dependents, home location, home value, work location and job title.