Know Your Customers to Deliver the Right Experience

Nurturing your customer relationships demands far more than typical cookie-cutter marketing, sales and service strategies. But because most analytics engines are disconnected from day-to-day customer interactions, the result is unwanted, repetitive – and just downright annoying – customer experiences.

Industry analysts have described Pega’s comprehensive analytics capabilities as best-of-breed. Using Pega’s predictive and adaptive analytics, you can take the guesswork out of customer relationships by accurately determining which people are going to be your best customers, what they are likely to want, how they will react to a particular offer and how you can best align customer desires with business objectives.

Leveraging historical data, Pega’s predictive analytics enable the development of pattern-based strategies by discovering the relationships in large volumes of data. The result is highly predictive scoring models that can be combined with business rules into sophisticated and agile customer experience strategies that address the distinct interests of each individual.

But Pegasystems doesn’t stop with just static predictions. Unlike typical analytics engines, Pega’s adaptive analytics make your predictive models actionable, adapting them in real time within the process. Adaptive analytics learn “on-the-fly” to modify predictions based on a wide variety of real-time factors such as a customer’s responses, the current situation, even the customer’s mood. Combined with Pega’s real-time decisioning capabilities, customer interactions across every channel can be managed dynamically to deliver the Next-Best-Action or offer at the precisely right moment.

Using Pega’s predictive and adaptive analytics, you can:

• Align customer needs and expectations with key business performance indicators.
• Increase wallet share and retention by predicting which additional products a specific customer is most likely to buy and at what price.
• Reduce involuntary churn and bad payment exposures with models, predictions and early warning signals.
• Leverage service interactions as sales opportunities with dynamically generated offers based on the context of the interaction.

Key Capabilities

• One-to-One Customer Segmentation — Predictive analytics considers such factors as customer lifetime value, expected ROI and propensity to churn to determine individual offers, retention strategies, budgets and actions for each consumer, consumer household or B2B customer.
• Best-Practice Modeling Algorithms — Regression, decision trees and genetic algorithms along with the ability to use multiple variables and to exploit non-linearity provide consistent and strong predictive accuracy performance.
• Coordinated Lifecycle Management —Universal strategies for managing proactive and reactive customer interactions optimize processes and the customer’s experience across every inbound and outbound channel.
• Enterprise Compatibility — Easy integration ties actionable models into existing CRM and enterprise systems to leverage corporate intelligence during a customer interaction.
• Business-Friendly Modeling Tools— Visual design and control capabilities make it easy to simulate, test and execute marketing, sales and service strategies without the wait for IT resources.