Next-Best-Action in a contact center
Pega Customer Decision Hub™ is an "always on" centralized decisioning "brain" that calculates a one-to-one business case for every Next-Best-Action recommendation. To create the business case, Customer Decision Hub combines the customer profile with previous interaction results, the current call context, and business rules, and then applies predictive analytics. Next-best-action recommendations occur across multiple channels, including the contact center.
Next-best-action recommendations help to ensure that a Customer Service Representative (CSR) takes relevant actions at every step during a customer interaction.
In this scenario, U+ Bank is a retail bank that uses Pega Customer Decision Hub (CDH) and Pega Customer Service in its contact center. All the configurations implemented by the decisioning architect in Pega Customer Decision Hub are visible to the CSR in Pega Customer Service, and help them achieve the best results when interacting with customers.
Let's consider an example of a customer interaction:
A call comes into the U+ Bank service center from Sara Connor, a U+ customer. The system immediately routes the call to a CSR.
The call details from the interactive voice response (IVR) system indicate that Sara wants to discuss recent credit card transactions.
Once the CSR accepts the call, all relevant details about Sara are displayed on the main application window.
The next-best-action recommendations then guide the CSR to take the next step with Sara.
In the lower-left corner of the screen, you can see the next best action that Customer Decision Hub recommends to the CSR.
Customer Decision Hub is an "always on" centralized decision management "brain" that calculates a one-to-one business case for every next-best-action recommendation. Customer Decision Hub combines the customer profile with previous interaction results, the current call context, and business rules to create the business case, and then applies predictive analytics.
Customer Decision Hub reevaluates the next best action and delivers a new recommendation when any new information becomes available. For example, when the customer responds to the recommended action.
In this case, the recommended action is to start a service task to handle Sara's transaction dispute. So, the CSR carries out the task. But the CSR is always in control and can select other service actions as appropriate during the conversation.
Once the CSR completes the task, the system refreshes the next best action to show the next recommended action, which is to present a credit card offer.
Customer Decision Hub analyzed Sara's credit score, which indicates that she is a customer with a high credit score.
So, the highest recommendation is for a Rewards credit card, a top offer, which is relevant for customers with high credit scores.
The relevancy percentages to the right are scores that are used to rank all relevant offers. The AI model calculates the scores by balancing what the bank wants to promote with the offer in which Sara is likely to have an interest.
The CSR can view more details about the recommended offer to discuss its benefits with Sara further.
After learning about the benefits of the offer, Sara is not convinced that it is a good offer for her. Therefore, the CSR presents the other available offers recommended by Customer Decision Hub.
The CSR then presents the second top offer that is in the suggestions section. Now, Sara is interested in the offer, and she decides to accept it.
When the CSR accepts the offer, the offer fulfillment is complete, and the customer response is recorded in the Interaction History.
As a decisioning architect, you can use Pega Customer Decision Hub to input or configure data necessary to make the next best recommendations.
The recent interaction between the CSR and the customer, Sara Connor, was captured and is now visible on the overview tab of Customer Profile Viewer.
Customer Profile viewer allows you to explore customer profile details such as demographics, active customer journeys, suppressed actions, recent interactions behavioral data, decision history, and interaction history.
The Next best actions tab in Customer Profile Viewer shows what actions can be presented to the customer. You can provide some input parameters like direction, channel type, which real-time container to test, and what the next-best-action results would be.
Apart from the tailored offers, you can also examine the arbitration factors: prioritization, weighing, and propensity for the final result.
Next-Best-Action Designer guides you through the creation of a next-best-action strategy for your business. Using its intuitive interface, proven best practices, and sophisticated underlying decisioning technology, you can automatically deliver personalized customer experiences across inbound, outbound, and paid channels.
On the engagement policy tab of NBA designer, you can observe previously defined sets of actions belonging to specific issues and groups (in this case cross sell, credit cards).
You, as the decisioning architect, can also define group-level engagement policies and action-level engagement policies. All the applied eligibility, applicability, and suitability conditions are visible on the Customer offers tab.
For an action, you can also define channel-specific treatments, which means that the action can be delivered to a customer by using a particular channel. The treatment contains all the data necessary for the CSR to offer the action to a customer.
To summarize, the Customer Sales Representative offered a customer a Next-Best-Action that was selected by the "always on" centralized decision management "bran" from a set of actions defined and configured by the Decisioning Architect.