Data Management


  • Companies often already spent the time and effort to collect data—the profit potential from that data lays dormant within the company’s walls!
  • Companies can aggregate data from different silo’s in the organization to make it even more useful (for example, merge marketing department data with financial department data).
  • Companies can append 3rd party data to further strengthen the customer intelligence gleaned from that in-house data.
  • Once the data is culled together in useable form, you can mine it.
  • The strategic business and customer intelligence, and marketing intelligence becomes a propriety asset to your organization and becomes your competitive advantage.

Case Study: Health Insurance Company

  • —When we met: Company had only a list of customers and type of health policy (Medicare, supplement, or Medicare Advantage).
  • —What we did: We told them the type of data they should be collecting (i.e., names of those customers that did not purchase their insurance), then we appended 3rd party data (e.g., political affiliation, home equity range, type of charitable contributions) and 40 other influential variables to predict customers’ propensity of purchase.
  • —The result: Company had a clean, enriched dataset, which led to a lead quality scoring model for their marketing call-center, projected to increase conversion rates by 250% and decrease labor costs by 80%.


  • Current data:
    • —Gather. Aggregated dataset from different departments of the organization.
    • —Program. Write software program to extract data and put into useable form (if necessary). —Missing data. Impute missing data.
    • —Your copy. Labeled dataset in a flat Excel file.
    • —Cookbook. MS Word data cookbook identifying the variables, labels, and codes (e.g., V1=equity range, 1=0-$50k, $51k-$100k, 3=$101k or more, 99=missing; V2=charitable contribution, 1=yes, 2=no, 99=missing.
    • —Algorithm. Provide algorithm for creating variables (e.g., Quality score = 10*V1 + 3*V2 + constant)
  • Future data:
    • Evaluation. A written evaluation of the data you need to collect (e.g., name, address, phone number and date for prospects that did NOT become customers) and how and where to record it.
    • In-person or teleconference explanation of findings.