Segmentation, modeling different

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Mike McGuirk
Mike McGuirk

A baseball player would not leave for the game without a glove and bat, just as a sculptor is not expected to choose between a chisel or hammer. So why do so many marketing professionals practice their trade with an incomplete set of marketing analyt­ic tools? Two must-have tools are customer segments and predictive models. These terms are often used interchangeably when in fact they are very different and support different business objectives.

Customer segmentation is the practice of classifying your customer base into distinct groups. Properly developed, segmenta­tion insights inform a strategic road map intended to take advantage of key profit-driving opportunities within each unique customer group. This could mean shorten­ing customer purchase cycles, deepening cross-product penetration or lowering ser­vice and support costs.

Predictive modeling is the practice of forecasting future customer propensities while assigning a score or ranking to each customer that depicts their anticipated actions. A key question is how many dif­ferent models will be required. The answer is linked to the number of different profit-driving behaviors a company believes it can influence with customer data-driven campaigns.

So when is customer segmentation, pre­dictive modeling or both the best tool for the job? A single segmentation scheme has many applications such as guiding dif­ferentiated customer development plans and investment levels or directing tailored marketing programs. In contrast, predic­tive models are typically developed for a very specific purpose. A company may create several different customer purchase propensity models for each of its key prod­ucts. The insights gained from each of the models can be used independently or col­lectively to shape a well informed targeting strategy for product penetration and cross-sell campaigns.

It is common to begin the journey into marketing analytics with the development of a segmentation scheme or a single pre­dictive model. But the analytic road map needs to provide the vision for how and when the complete suite of analytic tools can be drawn upon to fully exploit the ben­efits of data-driven customer marketing.

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