Simulating Next Best Action

You used the Pega Customer Engagement Blueprint™ tool to create a strategy for U+ Bank to cross-sell credit cards to existing checking account customers. In the topic Reviewing AI-generated Personas and brand voice, the AI created a set of customer experiences. In this topic, you explore the Pega Next-Best-Action process to identify the best offer for a customer.

In this demonstration, you will simulate the Pega Next-Best-Action process to identify the best offer for a customer.

So far, U+ Bank's Blueprint has "Grow" as the outcome. The AI-generated Personas and brand characteristics underwent review in the Blueprint, and the AI has created customer experiences.

On the Experiences landing page, I see the actions generated for each product.

Time for a closer look at one of the credit card experiences. I see the treatments, one for Email, one for Web, that I can edit.  I’ll change the marketing principle to “consistency” and see what the agents come up with.

Interesting suggestion! So, the experiences and messages are all fully adjustable to meet all U+ Bank’s requirements.

Back on the Experiences landing page, I click Simulate the Next Best Action to see how Pega Customer Decision Hub™ determines the best offer for a customer.

The simulation is asking me to select a Persona. I'll select Sarah, who manages her finances carefully.

This is the seven-stage Next-Best-Action process; that is the heart of the Pega decisioning engine. I’ll step through each stage to understand how it filters all actions down to one best action.

Stage 1: Full library. All actions are available. This stage shows the complete catalog of offers.

Stage 2: Eligibility. The system checks if Sarah legally qualifies for each offer. Factors like geographic restrictions, age requirements, regulatory compliance. The list is down to five actions now. One was filtered out because they weren't applicable for Sarah's situation.

Stage 3: Applicability. This stage checks if it's the right time and context. Does Sarah already have this product? Is the timing appropriate?

Stage 4: Suitability. This stage is about ethics and customer best interest. Does this offer truly align with Sarah’s budget?

Stage 5: Constraints. This stage prevents customer overwhelm. Customer Decision Hub enforces channel limits. We don't want to bombard Sarah with too many emails or web banners. All five actions respect our constraints.

Stage 6: Arbitration. This stage is where AI prioritization happens. Each action gets a priority score based on four factors: Propensity (so how likely Sarah is to accept), Context weighting (so what is the relevance right now), Action value, and Business Levers to set strategic priorities.

Stage 7: Next Best Action. The winner! The "Credit Card Rewards Boost" email treatment is selected as the best offer for Sarah. I can see the web treatment preview - a banner that says, "Unlock Amazing Rewards!” with a call-to-action button.

Now let me proceed to the Summary landing page.

Excellent! I see our complete Blueprint: all the Personas, brand voice, and experiences. There's an ROI calculator here. Time to get an estimate for U+ Bank's checking account customer base.

The personalization, real-time decisioning, and contextual arbitration can generate this additional value by improving conversion rates and customer satisfaction.

I’ll download the Blueprint file so I can import it directly into Pega 1:1 Operations Manager to jumpstart implementation later.