Pega Decisioning Consultant PEGAPCDC87V1 Pegasystems Study Notes

When implementing the Next-Best-Action paradigm to offer the new data plan, the mobile company should focus on "Customer relevancy and business profitability." This means that the company needs to ensure that the offers are relevant to individual customers based on their needs, preferences, and behavior, while also aligning these offers with the company's business objectives to ensure profitability. This customer-centric approach leverages Pega's predictive and adaptive analytics to personalize offers and maximize both customer satisfaction and business outcomes.

In the always-on outbound customer engagement paradigm, leveraging AI and continuous decisioning is crucial:

AI-Driven Decisioning:

Step 1: Configure the primary schedule to run daily, allowing the AI engine to continuously evaluate and select the best action for each customer.

Step 2: The AI considers engagement policies, customer behavior, and contextual data to determine the most relevant action.

Benefits of Always-On Engagement:

Continuous evaluation of customer interactions ensures that the actions presented are timely and contextually relevant.

It allows for dynamic adjustment of offers based on real-time data and customer responses.

Implementation Steps:

Step 1: In Next-Best-Action Designer, set up a primary schedule to run daily.

Step 2: Define engagement policies and configure AI models to select actions based on customer interactions and preferences.

Step 3: Allow the AI to decide whether to resend the original offer or present a new offer based on the customer’s response or lack thereof.

Example:

If the customer ignores the first email, the AI might decide to send a reminder email or a different offer based on updated engagement data and policy rules.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Configuring a recurring schedule for Next-Best-Action" section.

Pega documentation on "Using AI in decisioning".

 Finalization of Action Details:

Finalization ensures that all data from a specific run is written to a file or database.

This step is crucial for preparing the data to be shared with third parties.

 Single Run Data:

Finalizing at the end of each run means the file will only contain data from that particular run.

This approach ensures accuracy and relevance of the data being shared.

 Verification:

The documentation confirms that finalization at the end of each run results in the data being specific to that run.