Skip to main content


         This documentation site is for previous versions. Visit our new documentation site for current releases.      
 

Configuring the Calculate Customer Lifetime Value objective for retail banking

Updated on September 15, 2022

The Calculate Customer Lifetime Value objective allows you to define how customer lifetime value (CLV) is calculated in Pega Customer Decision Hub specifically for retail banking.

Customer lifetime value (CLV)
The CLV is the discounted value of the future profits that will be generated by an individual customer, discounted over a weighted average cost of capital. For example, a customer that will generate $120, $80, $30, $50, and $10 of profits in the next 5 years will have a CLV of $238, if the discounted factor is 10%.
Pega Customer Decision Hub

In retail banking, CLV helps your organization reach strategic objectives, such as increasing share of wallet, improving customer retention, and increasing return on marketing investment.

By activating customer valuation models, you can place more emphasis on customer service and long-term retention, rather than on maximizing short-term sales. As these future profits are uncertain, models have to be developed to estimate the future value of customers and based on data analysis techniques instead of the traditional analysis of historical data.

To deliver a CLV in a retail banking context, use the Pega model for customer valuation based on research by Haenlein, Kaplan & Beeser, as shown in the following figure:

The formula model for the CLV in retail banking
where:CLV is a function of the profit Pi,j,t.t will be generated in the future through j.j is the product.Pi,j,t is unknown because the prediction model is built to derive this future profit based on overall segmentations for the next five years by using transition values.For the identified segment, use percentage CLV value 100 and calculate the change in CLV for the next 5 years using transition values across each segment.WACC is the Weighted Average Cost of Capital that is the discounted rate of returns expected by the bank.

    Calculate the CLV for retail banking by performing the following steps:

  1. In the Customer Lifetime Value method section, click Configure.
  2. In the Configure Calculation Method dialog box, select Retail Banking by clicking Add, and then Apply.
    Result: Additional configuration settings for the selected calculation method appear.
  3. In the Discount rate field, enter, for example, 10.
    WACC is the recommended discount rate.
  4. In the Classifications section, add a minimum of three classifications by clicking Add classification:
    Classifications
    Classifications are groupings of your customers according to a criterion. For example, you may group your customers as high value, medium value, and low value clients. The CLV calculation for retail banking supports a maximum of 20 classifications. Each classification can be represented as either a (sub)strategy or a segment.
    Classifications require the appropriate strategy rules to be created in advance. For more information on creating a strategy rule, see Using the Strategy Builder. For information about creating a segment, see Defining customer audiences with customer segments.

    An example of customer classifications in retail banking
    A screenshot showing an example of how to configure customer classifications in retail banking
    1. In the Name field, enter a name of your classification, for example, High Value.
    2. In the Sourced from drop-down list, choose if you want each classification to be represented as a strategy or a segment.
  5. Optional: In the Classifications section, to choose a specific strategy or a segment for your classification, click Select strategy or Select segment:
    • If you click Configure Strategy, in the Configure Strategy dialog box, select your strategy by clicking Add, and then Apply.
    • If you click Configure Segment, in the Configure Segment dialog box, select your segment by clicking Add, and then Apply.
    You can also create a strategy or a segment by clicking Create in the Configure Strategy or Configure Segment dialog box. For more information, see Creating strategies or Creating a Criteria Segment.
  6. In the Classifications section, configure each classification by clicking Configure.
  7. In the Configure dialog box, fill in the required fields, as shown in the following example, and then click Apply.
    For example:
    A screenshot showing an example of how to complete a configure classification dialog box.
    where:Actual CLV is the current CLV assigned to each classification.Budget CLV is the amount that you are willing to invest into the customer.Probability of transition is a chance that the customer from each classification transitions from their current classification to each of the other classifications.Customer attrition is the chance of losing the customer due to customer attrition.
    Note: The total of the customer attrition rate with all the percentages of probability of transition should add up to 100%.
  8. On the top of the Create a Strategy page, click Save.
Result: A strategy rule with your defined classifications and transition probabilities is generated. This strategy then calls the RetailCustomerLifeTimeValue strategy to run the final mathematical calculation before determining the CLV score of the customer.
What to do next: You can modify your strategy rule only in the strategy rule canvas by searching for your strategy in the Strategies landing page of the Pega Customer Decision Hub portal, and then clicking ActionsOpen Strategy Builder.
Note: Strategies that were not created through the Strategy Builder initially or have been directly edited on the canvas do not have the option to Open Strategy Builder available.
  • Previous topic Configuring the Calculate Customer Lifetime Value objective
  • Next topic Configuring the Fixed or Dynamic Action Bundle objective

Have a question? Get answers now.

Visit the Support Center to ask questions, engage in discussions, share ideas, and help others.

Did you find this content helpful?

Want to help us improve this content?

We'd prefer it if you saw us at our best.

Pega.com is not optimized for Internet Explorer. For the optimal experience, please use:

Close Deprecation Notice
Contact us