GeoBusiness Data Consulting

Contact us to learn how we help your business turn data into marketing intelligence.

One Site is Better Than Two

Problem:
A district sales manager for a video store chain considers adding a store in a newly constructed shopping center. The shopping center is approximately one mile west of an existing store that performs near the average for her district. She thinks the new store will perform well but is not sure how it will impact the existing site.

Solution:
We look at the existing store’s transaction data and find that 68% of the store’s customers, and 91% of its revenues, came from customers living within three miles of the store. Mapping this same data shows that most of these customers live to the west of the existing store, near the proposed new site. We conclude that most of the customers at the new store will likely come at the expense of the existing store. Based on our research, the sales manager relocates the existing store, and its talented and experienced manager, to the superior location in the new center.

Client Benefits:

  • They avoid the unnecessary expense of adding a redundant store.
  • A proven store manager will succeed at a higher level in a better location.

Better Targeted Mailings

Problem:
A non-profit agency is disappointed with the results of their recent direct mail campaigns. While they have vastly increased their mailing list (as well as the costs of their mailings) they have not seen a corresponding increase in their contributions.

Solution:
Our goal is to find the characteristics that separate donors from those who ignore the client’s mailings. We enrich the client’s mailing lists by associating each address with the demographic and lifestyle characteristics of its corresponding census block group. Then, using the client’s contributor records, we add more data fields to the mailing list that describe if and how individual mailing recipients responded. Using a wide variety of data mining techniques (from simple grouping and sorting to complex multi-regression statistics) we construct a model to predict how households in individual block groups will most likely respond to future campaigns.

Client Benefits:

  • Increase ROI by sending more pieces to areas most likely to respond and avoiding areas most likely to ignore.
  • The client has a baseline model to improve upon through analysis of future mailings.
  • Increased market intelligence permits client to design specialized marketing programs around donor sub-groups.

Market Changes Impact Store Performance

Problem:
A department store chain does not know why one of their better stores is suddenly experiencing a sharp revenue decline. Corporate management cannot attribute the decline to any internal factors - the store’s management is highly competent and motivated; customer satisfaction surveys remain high; the product mix and inventory levels are consistent with the chain’s most successful stores.

Solution:
We search for external factors that might describe the store’s flagging performance. We analyze sales records from several years ago, when the store was strong, through the present decline. Through mapping and statistical analysis, we show that the store’s trade area contracted significantly to the north and west during this period. Follow-up fieldwork confirms that rapid development is occurring in the area north and west of the store. While this new development generated higher market potential for the store, it also attracted competitors who were aggressively courting the new market at the expense of the client.

Client Benefits:

  • Our research alerts the client to new and dangerous competitive forces in their store’s trade area, as well as a new market opportunity.
  • Proper identification of problem allows for appropriate response.
  • Client has a baseline study from which to benchmark effects of remedial measures.

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