B2C Medical Products Prospecting System

ICS Product/Service Area:  Analytics
The Challenge
A Michigan-based medical products company that sells to consumers through 19 retail stores realized its prospecting efforts had become stale – to the point where the ROI was upside down. ICS, along with Dziurman Dzign, Inc., recommended leveraging analytic tools to boost prospecting effectiveness.
The Objectives
Use analytics to:
  • Gain insight in regard to the demographic, geographic and psychographic makeup of “best and worst prospects”
  • Better understand “best prospect” differences from store to store
  • Utilize the latest segmentation tools to more accurately predict best prospect attributes

The Solution and Results
From a data perspective, the Company provided 25,000 customer records and access to recent mail files. Using this data, ICS created the following eight components in the total solution. Program results are highlighted with solution component.
1.Determined Best Prospect Attributes
Painted a clear picture of where the Company should focus its marketing investment.
  • Best Prospect Attributes were clustered using: single/married, rural vs. urban, income and net worth and geographic location in relation to each of the 19 stores
  • Many top segments were “under-represented” when ICS evaluated the “Prospect to Customer Index,” meaning that the Company was not targeting top prospects as strongly as it could have
  • ICS also tested the cluster performance and found insight to help the Company be more effective in targeting

2.Determined Worst Prospect Attributes
Determined that a specific cluster was “over-represented” in the prospect sampling, which correlated with the Company’s lack of marketing success
3.Store-level Personicx Profile – Geographic Analysis for Each of the 19 Stores, Key Findings:
The store level analysis did not reveal any major insights; however, this analysis pointed to the importance of wealth demographics for this Company
4.Product-level Personicx Profile – Geographic Analysis Summary of the 19 Store Footprint, Key Findings:
For each range of products (high-end, mid-range and low-end), ICS determined which consumer segments gave the Company its greatest volumes in sales and correlated each consumer segment with the detailed clustering analysis (i.e., answering the questions: “Who is buying the Company’s products, Which products are they buying and Who are these customers?”).
5.Customer Mapping
ICS created a visual map of these customers showing where these customers are located in relation to each of the 19 store locations
Customer Mapping - Displaying where customers are in relation to specific stores
6.Area of Responsibility (AOR) for Each of the 19 Stores
Once the customers’ locations were plotted, ICS assigned each customer to a store based on the customer’s distance from all store locations
7.Identifying Prospects Within the New AOR , List Selection Recommendations
Based on the previous analytic steps, ICS identified significant prospect volume falling within two major categories. Then, ICS recommended a strategy that detailed exactly:
  • Which list to use, which consumer clusters to include in each list and the quantity to include from each of the two major prospect categories
  • How to order fill best prospects unique to each stores AOR and set a quantity per store
  • How to evaluate short-term results and apply the learnings to longer-term prospecting

8.Ways to Apply What ICS Has Learned and Suggestions for New Learning Initiatives
Based on the results from the overall analysis, ICS gave specific recommendations regarding mailing initiative, tracking results, which products to push to which consumers and in which geographic location.
ICS also provided recommendations for new learning initiatives that will give even greater insight to guide future marketing efforts relating to each store location.