Customers are the life-blood of an organization and all organization constituents exist to serve the customer. Yet many organizations interact with them with only a partial understanding of why they do business with them. A one-size fits all approach is inefficient and overtly expensive. Experiental understanding of customers backed with an analytical framework for understanding behavior results in superior customer interaction strategies that positively impact customer satisifaction, customer revenue and customer profitability.
Value based segmentation usually do not fit very well with demographic ones. Segmentation studies while useful for explaining why groups of customers are different are not actionable due to the difficulties in identifying them. Demographic segmentation while practical does not explain why customers are different. A middle approach is required to develop "actionable segmentation" that works on the field. Scoring models and dual-objective segmentation models are useful in constructing segmentation frameworks that combines the best of both value based segmentation and demographic segmentation to construct meaningful and actionable segments.
Investment in acquiring and retaining customers have long-lasting impact. Good customers are worth their weight in gold while bad customers have the potential to make your business unprofitable. Understanding the true value of a customer over a life-time not only helps in understanding the right size of marketing investments but also helps identify customers who tend to be unprofitable in the short term but are invaluable in the long term and vice versa. Customer Life-time Value models also provide the operating framework to decide on right campaign goals no matter the type of campaign - customer migration, acquisition of new customers, retention or re-acquisition of attrited customers.
In a hyper-competititve market, a good portion of customers do not complain - they simply take their business away to a competitor. The right customer experience whether in terms of service or additional product / service benefits are useful only if these customers are approached before they make up their mind to go. Historical analysis of churn is of academic importance. It is important for companies to invest in predictive behavior based churn models that identify behavior linked with customer attrition well before time - retention rates through offers made 3 months before predicted attrition have vastly superior success rates than similar offers made a month before predicted attrition.