Marketing Frontier

Strategies for a data-driven marketing world


Share of Wallet – The Next Level

Posted by marketingfrontier on November 17, 2006

In the previous blog, I discussed how Janet, the fictitious Marketing Director of ABC Autoparts, developed purchase frequencies for her Best Customers for windshield wipers and oil and air filters and was able to use that information to target specific offers for those products to customers who resembled Best Customers but did not purchase like them.

The question I posed at the end of the blog was “how can we use this learning to develop a broader strategy?”   The goal is to have a broader impact on total company revenue, while still maintaining a highly targeted (and therefore efficient) customer contact strategy.

Two approaches use Share of Wallet to drive broader strategies.  Let’s say that Janet has been successful with her initial pilot (as initial pilots of this work tend to be), and now wishes to contact more customers using the “right customer, right product, right time” approach.

Janet realizes that her opportunities lie in two different areas:  (1) Customers who are missing a product from their purchase mix or seem to have infrequent purchase habits, based on comparison to other similar customers, and (2) Customers who seem to be declining in purchase frequency, suggesting that they are sourcing their requirements outside the company.  Both of these groups should respond better to product-specific offers, based on Mark’s Law of Similarities (just kidding, this is not original thinking!), “Customers who resemble Best Customers are the most likely to behave similar to Best Customers, if the right stimulus is applied at the right time.”

So Janet first identifies, for her top-selling products and highest margin products, customers who purchase that product infrequently.  For each product, she identifies an “ideal” share of sales revenue for that product, by studying Best Customer product mix.  Then she identifies the actual share for High Potential Customers, and determines the gap.  Then she starts to market to that group, from the “top down” (largest gap to smallest gap.

As a second step, Janet determines purchase frequency for each top-selling or highest margin products, year-to-date for each customer.  Then she does the same calculation for the previous year, and compares each customer’s “run rate” this year to last.  She subtracts the difference for each customer and then starts to market to THAT group, also from the “top down.”

Now she is really getting to some results.  I will talk about those results, how to interpret them and enhance future programs, in the next blog.


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