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.
