Investigating the effect of applying engagement policies

Engagement policies are a set of business rules and practices used by the organization to determine which customers are eligible for which next best actions. There is an experiment in Impact Analyzer, which measures the value and engagement loss caused by possibly too strict engagement policies. Pega Customer Decision Hub™ provides an additional optimization tool, Value Finder, which enables users to further explore this issue. The Value Finder helps users identify underserved customers and optimize their next best actions to deliver higher value.

You can use the engagement policies to specify the conditions under which an action or group of actions a customer is eligible. There are three engagement policy conditions: Eligibility, Applicability, and Suitability.

One of the experiments you can perform with Impact Analyzer is "How is Next-Best-Action performing against applying only eligibility criteria?". This experiment compares the next best action configured with full engagement policies and arbitration against the action configured with only Eligibility (without the Applicability and Suitability rules) followed by arbitration. The engagement policies need to be appropriately categorized for the "How is Next-Best-Action performing against applying only eligibility criteria?" experiment to work. Specifically, all the 'required' engagement policies need to be categorized as Eligibility criteria, while discretionary policies need to be categorized as Applicability and Suitability criteria.

You can use this experiment to identify potential opportunity gains by making your engagement policies less restrictive. Sometimes, when engagement policies are too restrictive, actions with a high potential for engagement and value capture are filtered out and do not have a chance to compete for the impression. By eliminating non-essential engagement policies, you can put these offers back in place and increase customer engagement and value capture. In this experiment, the test group receives the fully-configured next best action, while the control group receives an action with only the eligibility criteria applied.

In this example, Impact Analyzer indicates a -16 percent of value lift and -8 percent of engagement lift, and the overall status of the experiment is negative. The widget displays the recommendation that you can make your engagement policies less restrictive to increase the overall value capture.

Pega Customer Decision Hub provides opportunities to engage empathetically by identifying "under-served" customers. By using the simulation tool Pega Value Finder, you can identify areas for improvement and monitor scenarios where customers receive no or only low-propensity actions. Value Finder also suggests actions you can take to increase the value capture, such as adjusting engagement policies or creating new actions and treatments.

Consider running a Value Finder simulation to indicate missed opportunities and underserved customers.

Customer Decision Hub has various subsets of customer data available to run simulations, and the subsets of customers represent a sample of real customer data. For a Value Finder simulation, you use a subset of the customer data and apply all three engagement policy conditions.

Once the simulation run is complete, the Value Finder landing page displays a pie chart with numbers of customers in different categories:

• Receiving no actions represents the number of customers who received no actions.
• Receiving only irrelevant actions represents the number of customers who received low-propensity actions.
• Receiving at least 1 relevant action represents the customers who received high-propensity actions.
• Receiving only new actions represents the customers who received only new actions.

Value Finder also displays customer categories tabs, with the number of customers filtered out because of the given engagement policy. In this case, the first customer category shows the number of customers who receive irrelevant actions because of eligibility conditions. In this case, it is 370 customers. Analysis of the first customer category suggests that the eligibility conditions might be too strict and exclude many customers.

When you click a particular category, you can run a simulation to see which conditions influence the actions negatively. Considering the business objectives and current conditions that you want to maintain, you can decide whether to loosen your eligibility rules or not.

If you decide to update the engagement policies to increase lift and engagement values, you can perform an additional Value Finder simulation to determine the number of underserved customers that remain after you apply the new settings.

You have reached the end of this video. You have learned:

• How Impact Analyzer identifies the value and engagement loss caused by engagement policies.
• How Value Finder identifies underserved customers and provides valuable insights.
• How Impact Analyzer and Value Finder help businesses improve the optimization of next best actions and align them more effectively with their overall business goals.