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Many organizations are associating data monetization with selling their data
By: William Schmarzo
Jul. 13, 2017 02:15 PM
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.
Figure 1: Analytic Profiles
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.
Analytic Profiles in Action
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:
“Improve Campaign Effectiveness” Use Case
Figure 2 shows the Customer Analytic Profile for Customer WDS120356 resulting from the “Increase Campaign Effectiveness” use case.
Figure 2: Improve Campaign Effectiveness
Note: a customer will NOT be in a single Demographic or Behavioral segment, but will likely reside in numerous different Demographic and Behavioral segments based upon combinations of the demographic attributes and purchase activities.
As a result of this use case, we have created and captured in the Analytic Profile numerous demographic and behavioral segments for each individual customer. These demographic and behavioral segments are now available across different use cases.
“Improve Customer Loyalty” Use Case
As part of their analytic modeling process, the data science team decides that the Behavioral Segments created for use case #1 can be re-used to support the “Increase Customer Loyalty” use case, but find that they can improve the predictive capabilities of the Behavioral Segments with the additional data.
Consequently, the data science team completes two tasks in support of the “Increase Customer Loyalty” use case:
Figure 3 shows the updated Customer Analytic Profile for Customer WDS120356 resulting from the “Increase Customer Loyalty” use case.
Figure 3: Increase Customer Loyalty Use Case
It is critical to note that the beneficiary of the improved Behavioral Segments – at no additional cost – is use case #1: Improve Campaign Effectiveness. That is, the performance and results of the “Increase Campaign Effectiveness” use case just improved at no additional cost!
In order to realize this benefit, the analytics captured in the Analytic Profiles must be treated like software and include support for software development techniques such as check-in/check-out, version control and regression testing (using technologies such as Jupyter Notebooks and GitHub).
“Increase Customer Store Visits” Use Case
As part of their analytic modeling process, the data science team again decides that the Behavioral Segments updated for use case #2 can be re-used to support the “Increase Customer Store Visits” use case, and they find that they can again improve the predictive capabilities of the Behavioral Segments with the additional data necessary to support the “Increase Customer Store Visits” use case.
Figure 4 shows the updated Customer Analytic Profile for Customer WDS120356 resulting from the “Increase Customer Store Visits” use case.
Figure 4: Increase Store Visits Use Case
Again, the beneficiary of the updated Behavioral Segments – at no additional cost – are use cases #1 and #2 that find that the performance and results of those use case just improved at no additional cost.
Analytic Profiles Summary
Figure 5: Fully Functional Customer Analytic Profile
The Analytic Profiles also provide the foundation for identifying new revenue opportunities; to understand your customer and product usage behaviors, tendencies, inclinations and preferences so well that you can identify unmet customer needs or new product usage scenarios for new services, new products, new pricing, new bundles, new markets, new channels, etc.
Embracing the concept of Analytic Profiles creates an operational framework for the capture, refinement and re-use of the organization’s analytic assets. This enables:
Analytic Profiles help organization to prioritize and align data science resources to create actionable insights that can be re-used across the organization to optimize key business and operational processes, reduce cyber security risks, uncover new monetization opportunities and provide a more compelling, more prescriptive customer and partner experience.
So while you should not focus on selling your data (because it’s hard to quantify the value of your data to others), instead look for opportunities to sell the analytic insights (e.g., industry indices, customer segmentation, product and service cross-sell/up-sell recommendations, operational performance benchmarks) that support your target market’s key decisions. Your target market will likely pay for analytic insights that help them make better decisions and uncover new revenue opportunities.
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