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New Ways to Reach the Customer: Scientific Micromarketing and the New Customer Penetration Paradigm

[Abstract ]

This paper approaches the scientific micromarketing opportunity through initially posing the following questions that in all likelihood would present themselves to the C-level executive suite of a company considering the benefits, costs and other considerations involved in joining the scientific marketing revolution:

a. Once I have come on board with a targeted revenue optimization strategy and implemented the necessary change to support it, what opportunities are there – beyond price – to improve the profitability of my customer interactions?

b. How can the science that powers micromarketing excellence help identify meaningful cross-selling, up-selling and other opportunities in ways that I have not thought of or would have trouble seeing among the billions of potential opportunities on my daily transaction blotter?

The ability to understand which products tend to sell well with other products, and/or what types of customers tend to purchase what types of products, is the foundation of successful customer penetration (cross-marketing): A new approach to customer penetration.

By identifying demonstrable trends showing affinity between products, or between customer types and product types, firms can undertake specific marketing and pricing actions aimed at producing a more optimized revenue stream for each transaction. The trouble is that when we have hundreds or thousands of SKUs our daily transaction opportunities can number in the millions or billions – more than the human mind can comprehend and rationally analyze for profitable action. Intuition only goes so far – we can intuitively posit that hamburgers and hamburger buns go together, or Romaine lettuce and Caesar salad croutons, and back up this intuition with targeted studies of purchasing patterns in stores and regional networks.

But that level of sophistication only permits us to tap the surface of the potential mine of product and customer affinities. To overcome the problem we need a solution that is capable of combining the insight from which products sell well together with insight from which types of customers purchase which types of products, under which conditions, and combine these insights into a single model. By sharing information from the product perspective and the customer perspective within the same model, the efficacy of making accurate predictions is dramatically improved. We are then in a position to not only answer the question of “what” in relation to product affinities, but also “why”. This model can then illuminate optimal promotional prices, or discounts, or other means to induce purchase at the individual customer level.

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