What data sources feed customer insights?

Loyalty transactions from Engage 365, CRM data from Dynamics 365, service cases, marketing engagement events, and purchase history — all unified on the shared data model.

How are insights made available to frontline teams?

Through Puzzle, Yegertek's clienteling application, which surfaces the relevant insight at the point of customer interaction.

How are predictive scores calculated?

Rubix applies machine learning to the unified customer dataset to produce churn risk, LTV, and next-purchase probability scores, updated continuously as new events occur.

Customer Insights

Know Your Customers Well Enough to Act on It

Customer insights built on a unified data model — connecting loyalty behavior, purchase history, service interactions, and engagement signals into a single view of the customer that your teams can actually use.

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Overview

Customer insights programs often produce reports that no one acts on. Not because the data is wrong, but because the data is old, fragmented across systems, and delivered in a format that requires interpretation before it can inform a decision. Yegertek's customer insights capability is built on the shared data model across Engage 365, the CRM, and Rubix — so insights are current, connected to real customer behavior, and surfaced in the workflow of the team that needs to act on them.

The Challenge

✕Customer data fragmented across loyalty, CRM, service, and transaction systems — no single view.
✕Insights produced as reports delivered on a schedule, not as live signals available when a decision is being made.
✕Analysis that describes what happened but does not predict what will happen or recommend what to do.
✕Segmentation that groups customers by demographics when behavioral signals are far more predictive.

Our Approach

Customer insights are built on the unified Dynamics 365 data model — so loyalty transactions, service interactions, engagement events, and purchase history are all in scope. Rubix applies AI to this unified dataset to produce predictive scores and behavioral segments that frontline teams can act on, not just analytics teams that can study them.

Capabilities

Outcome-labeled features, not a laundry list.

Single customer view

Loyalty, purchase, service, and engagement data unified on one record

Behavioral segmentation

Segments defined by what customers do, not just demographic attributes

Predictive scoring

Churn risk, next purchase probability, and LTV predictions from Rubix's AI

Frontline insight surfacing

Insights delivered to store, call-center, and field staff in the Puzzle interface, not just to analysts

Next-best-action recommendations

AI-driven action prompts delivered at the point of customer interaction

Real-time insight refresh

Insights updated from live events, not from periodic batch recalculations

Business Benefits

Customer insights built on a unified, real-time data model inform decisions that static segment reports cannot support. Frontline staff with behavioral context close retention conversations more effectively than those working from demographic profiles. Predictive signals from Rubix allow the retention team to act before a member lapses rather than after. And because insights are delivered in the workflow rather than in a separate analytics tool, the time between insight and action compresses significantly.