Customer clustering and segmentation are two of the most important data mining methodologies used in marketing and customer-relationship management. They use customer-purchase transaction data to track buying behavior and create strategic business initiatives. Businesses can use this data to divide customers into segments based on such "shareholder value" variables as current customer profitability, some measure of risk, a measure of the lifetime value of a customer, and retention probability. Creating customer segments based on such variables highlights obvious marketing opportunities.
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For example, high-profit, high-value, and low-risk customers are the ones a company wants to keep. This segment typically represents the 10 to 20 percent of customers who create 50 to 80 percent of a company's profits. A company would not want to lose these customers, and the strategic initiative for the segment is obviously retention. A low-profit, high-value, and low-risk customer segment is also an attractive one, and the obvious goal here would be to increase profitability for this segment. Cross-selling (selling new products) and up-selling (selling more of what customers currently buy) to this segment are the marketing initiatives of choice.
Within behavioral segments, a business may create demographic subsegments. Customer demographic data does not typically correlate to customer shareholder value, which is why you don't use it together with behavioral data to create segments. However, demographic segmenting can steer marketers in selecting appropriate advertising, marketing channels, and campaigns to satisfy the strategic behavioral segment initiatives.
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