Customer Lifetime Value - Multidimensional approach

Customer relationship management has become very critical for all the companies and marketers are eager to find out loyal customers for increasing their sales base. But it is critical to note that all loyal customers may not necessarily be profitable. Customer lifetime value model is a framework which allows clear segmentation of the customer base to identify targeting segments which would return maximum profits on investment in marketing efforts. CLV is used to create comprehensive approach towards targeting customers and bridging the gap of knowledge towards retaining them. The churning customers are accurately identified and strategies of retention are broadly suggested as per the customer persona. This framework can be used as a suggestive for both up-sell and cross-sell. Customer acquisition and retention has always been an issue for the sellers and CLV can be used as a tool for resolution of these business problems as well as creating focused marketing campaigns for various customer segments as per their life cycle stage.

Another aspect worthy of notice is the investment done towards acquiring the customer. The CLV framework can be made more robust and sophisticated depending upon the availability of such information as it can be used to identify ROI, or the more sophisticated Value metrics. Analysts always search for multiple attributes of information which can be used to create a more robust approach of calculating CLV. Competitive information, Marketing costs, Brand health indices, NPS, Online purchase behaviors, various cost indices for retention, marketing and acquisition etc. are a few of such aspects of data. Another dimension which can be added to the analysis is around Recency, Frequency and Monetization of the purchases done by the customer. But the assumption with this analysis is that a higher valued customer tends to become even higher valued as he is likely to continue his purchase behavior. Customer feedback or touch point data could help verify this assumption for such metrics to alone define CLV. A multi attribute framework with sophisticated data mining algorithms can provide state of the art platforms for supporting strategic business decisions. The Global BI team along with Dell Global Analytics is working on creating aforementioned CLV models using Survival analysis for retention and Random Forests for Margin Index prediction.