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How advanced analytics can impact your Customer Lifetime Value

How advanced analytics can impact your Customer Lifetime Value Banks have tons of products. This can range from current accounts, saving accounts, credit accounts as well as personal and business loans. Banks have also created more efficient platforms to facilitate these transactions primarily through the emergence of mobile banking.


So with all of these different data points, how is it that many financial services are still not utilizing and leveraging this information to better understand and predict their customer’s behavior?


Customer Lifetime Value


In business there are some golden sayings that everyone needs to appreciate and adopt. For example;

  • • Eighty percent of our businesses come from 20% of our customers.
  • • It takes 10 times less to sell to an existing customer than find a new customer.

The goal of predictive CLV is to model the purchasing behavior of customers in order to infer what their future actions will be.Whether a predictive CLV model and methodology makes sense for your use case will largely be determined by the business context.


Using IBM SPSS Text Analysis for Surveys, Bank of Ireland Retail Strategy Marketing was able to automate the manual process of coding verbatim responses. Before using SPSS Text Analysis for Surveys, the Retail Marketing Strategy Department had to manually read and code 20,000 open-ended text responses. This also involved them analyzing the results to pick up on key issues that were arising with customers. Stephen Moran, lead analyst with Bank of Ireland states, “With IBM SPSS Text Analysis for Surveys, in a matter of minutes we could pull out key aspects of customer’s satisfaction and bring customer issues to the fore immediately.”


How Big data can revolutionize your CLV


We require an approach to register a client's lifetime esteem. Let’s say a client just purchased a PC from us. Things being what they are, what sort of future incomes would we be able to anticipate from him? All things considered, he just purchased a PC so he probably won't require a PC in the following three years.


However, that’s where customer lifetime value considerations can come in. What if he has kids? When they go to center school or secondary school they may require a workstation. They at one point attend a university and there will be all the more buying included. We should know his socioeconomics and family data to time these life occasions. All in all, He may spend money on presents for others amid Thanksgiving or Christmas, as most family men do. He may want to give a PC to someone else. There could be potential yearly buys. In the event that we can recognize these occasions, these repeating needs and yearly needs we would have the capacity to evaluate his aggregate future business esteem.


Then we might be able to schedule loyalty schemes, like discount coupons, package offers, to entice him to buy more. So, creating a customer’s lifetime value and the timing of his future purchases will help us contact him at the right time with the right deals and generate more revenue.


All things considered, prescient CLV can fill in as an important resource in a company's systematic toolset to help illuminate vital, information driven and benefiting profit-maximizing choices.



Sources:

http://www.presidion.com/case-study-bank-of-ireland/

https://medium.com/m/global-identity?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Fpredictive-customer-analytics-part-iii-aeb996beafba