How to Identify Pockets of Marketing Opportunity within Your Customer Base

Nearly every marketing professional has been faced with the challenge of breaking down a broad population into meaningful segments. The purpose of that exercise may be to identify opportunities to improve a marketing campaign, offer relevant products to consumers, or identify higher-potential targets or subsegments within a given population.

While there are various analytical techniques available to solve these marketing challenges, ‘classification trees’ can be used as a quick and efficient methodology for slicing up an audience, understanding the drivers that differentiate consumer behaviors, and ultimately “sifting out” the best opportunities to achieve a given marketing goal.

Classification and Regression Tree Analysis (CART)
CART analysis has been in use by the marketing sciences community for years. In general terms, the purpose of this analysis is to determine a set of if-then logical conditions that predict a certain outcome. The output is displayed as a tree diagram, consisting of a series of questions, and the answers to those questions trace a path down the tree. The end point (or “end node”) reached determines the classification or prediction made by the model, which can be a qualitative judgment (e.g., these are the campaign responders), or a numerical forecast (e.g., sales will increase by 15%).

The case outlined below is an example of how CART analysis has helped one company increase sales:

Uncovering High Performing Subsegments to Increase Sales

The Challenge: This durable goods manufacturer had an increasing number of customers who, based on a cross-tab of predictive models, showed a low likelihood of buying their product in the near future. This low-opportunity segment represented over half of their customer base, and the marketing goal was to increase sales.

The Solution: A number of key customer-level variables, including purchase history, purchase propensity model, product preference models, and demographics, was included in a CART analysis. Our objective was to identify differences in purchase rates across 15 key variables with the purpose of finding pockets of customers who were likely to buy the company’s products in the near future.

The Result: Two distinct and sizable customer groups were uncovered, with historical purchase rates that were twice the rate of other customers in the same segment. Customers are currently being targeted with specific marketing treatments for increased sales.

If you would like to talk about Classification Tree methods, or ways to fine tune a segmentation approach or marketing campaign, simply click the designated button below. For more information about this topic or marketing analytics in general, read our whitepaper on CART analysis, or visit our website at www.iknowtion.com.

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Posted by on 07/29/2009 12:13 PM

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