How important is it to align your analytics efforts with the customer lifecycle? Imagine you are a credit card department within the consumer banking branch of large bank. You are sending periodic mailers offering credit cards to your customers. Before sending these mail offers you do a minimum screening in a way that you only offer these to customers that have been with the bank for at-least 2 years and have maintained a balance above a certain threshold. However, you notice that the acceptance of your mail offers remains low even after a few campaigns. Why do you think is that?

The answer lies in a simple concept, but one that is often overlook by analytics teams. Are you trying to identify which life stage the customer is in? Are you trying to synchronize your sales effort with the customer lifecycle? What is customer lifecycle you ask?

customer-lifecycle

Customer lifecycle can be understood as a framework to track the relationship between a customer and a bank. It starts off with the Acquisition stage where your primary focus is to figure out ways to identify and bring on-board customers with which a mutually beneficial relationship can be created. After this comes the Development stage, where the customer is encouraged to expand his portfolio with your products through cross-sell efforts, etc. Finally, comes the Retention stage where the customer has been with you for more than a decade, so you try to enhance the relationship and monitor customer satisfaction so that the customer can act as a good ambassador for you.

These are the three basic stages, Acquire > Develop > Retain. You could break-down these stages further to target any pain-points you might be facing in a particular stage. For example, your acquisition through campaigns this year has not been as fruitful as previous years. So you break down Acquisition into Awareness > Consideration > Purchase to pin-point the root cause. Data suggests that the advertising budget is same as previous years. Marketing campaigns to tip consumers in the consideration stage into the purchase stage are also being sent in a timely manner. However, you are still loosing prospective customers in the purchase stage. You sanction a study to identify any changes that might have happened in the way you on-board a customer. Voilà! You identify that the on-boarding form has been appended with two new sections seeking a little more information about the customer before on-boarding. You weigh the necessity of collecting the information which on-boarding and decide to drop these additional sections. Few months later, Acquisition metrics start to return to previous years ballpark.

Perhaps the most important aspect in the world of data driven decision making is to align the reporting and analytical efforts with the customer lifecycle. For example, during the acquisition phase your primary aim is to provide the right product just when the prospect customer needs it. This could be achieved though an analysis such as the Best Next Offer, where you use Machine Learning techniques to match your products with profile of prospects created using demographic, psychographic, etc. factors. Similarly, during the Development stage you focus on meticulously reporting and driving cross-sell efforts to increase your product presence in the customer portfolio. Lastly, during the Retention stage your focus should be on minimizing churn through customer satisfaction and this can be achieved through churn analysis on the quality data you collected in this aspect.

To close I will reemphasize the importance of collecting good data, analytics and aligning it closely with customer lifecycle for optimal data driven decision making.

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