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.

Customer lifetime value - various approaches

Marketing problems can be analysed from various angles as there is no comprehensive solution to all the interlinked and complex questions that the business will pose for the analysts.
Strategic decision making is a very sophisticated job and requires complete focus on the validity and applicability of the "go to market" plan that the data driven approach will suggest.

Let us review some new business questions today:
"which customers are going to stay put with us", "how long will they stick to my business","how much profit can i make from such long standing customers"

Buying behaviour of an average customer is getting impacted by multiple factors, called drivers, like product quality, value of the deal, branding of the product, promotion efforts, competitive space in the market etc. Many times the companies launch price offs, combo deals etc to retain their customers. Especially in a B2B space, customer relationship management is very crucial. Loss of a single big fish may prove very costly and even result in convolution of the brand in the market.

Hence, Marketers want to predict the longevity of the customer as well as their value in terms of profit. Here Customer lifetime value analysis can play a prominent role in suggesting the targeting strategy to the sales and marketing teams.

Retention predictions are usually done using survival analysis whereas profit predictions can be done using various methods like regression, cart or random forests.

In the next post i will discuss these topics in detail. So stay tuned....