YegertekAI
Most "personalization" in enterprise loyalty programs is segmentation wearing a nicer label — the same offer sent to everyone in a broad demographic or behavioral bucket, refreshed quarterly at best.
Overview
Most "personalization" in enterprise loyalty programs is segmentation wearing a nicer label — the same offer sent to everyone in a broad demographic or behavioral bucket, refreshed quarterly at best. True personalization requires a model trained on complete, current customer data and delivered consistently across every channel, which most point solutions can't do because they only see a fraction of the customer relationship. Yegertek's AI service is Rubix's machine learning layer: next-best-action recommendations, predictive churn scoring, and dynamic segmentation, all trained on the same unified customer profile that powers reporting and data unification. Because the models run on complete data rather than a sampled export, and because recommendations are delivered through the CRM, Puzzle, and the mobile app consistently, personalization here means one customer, one experience — not a marginally better segment.
The Challenge
- ✕**Segmentation mistaken for personalization.** Broad buckets refreshed infrequently, not individual-level recommendations.
- ✕**Models trained on incomplete data.** AI built on a partial export produces unreliable, low-trust recommendations.
- ✕**Inconsistent delivery across channels.** A personalized in-app experience that isn't reflected in-store or in a call center.
- ✕**Explainability gaps.** AI-driven decisions that can't be explained to a compliance or audit function in regulated industries.
- ✕**Personalization as a marketing-only initiative.** AI capabilities siloed to campaigns rather than informing service, retention, and rewards decisions too.
Our Approach
**Trained on unified data, not a sample.** Models run on the same live Rubix profile as reporting and segmentation. **Delivered everywhere, not one channel.** The same AI recommendation reaches the CRM, Puzzle, and the mobile app consistently. **Explainable by design.** Every recommendation surfaces the factors behind it, built for the compliance scrutiny regulated industries apply to automated decisions. **Outcome-tied, not engagement-metric-tied.** Every model is measured against retention and revenue, not clicks or opens.
Capabilities
Outcome-labeled features, not a laundry list.
Next-best-action engine
A specific recommendation per customer, not a generic rule
Predictive churn scoring
At-risk customers flagged before they disengage
Lifetime-value modeling
Prioritization based on predicted long-term value
Dynamic, AI-built segmentation
Segments that update continuously from live behavior
Explainable AI outputs
Every recommendation comes with the factors behind it
Real-time personalization delivery
Recommendations reach the customer in the moment
Business Benefits
AI personalization built on unified data produces recommendations teams can actually trust and act on — a next-best-action surfaced inside the CRM or Puzzle that reflects a customer's complete history, not a partial one. Churn prediction gives retention teams a genuine early-warning system instead of a lagging indicator discovered after a customer has already left. Explainability satisfies the scrutiny regulated industries apply to automated decisions, turning AI from a compliance risk into a defensible, governed capability. And because personalization is delivered consistently across every channel, the experience a customer receives doesn't depend on which touchpoint they happen to use. Industry benchmarks show next-best-offer models running on unified customer data typically produce 18–28% higher conversion rates than segment-based targeting, with churn-prediction models commonly reaching 75–85% detection accuracy at a 60-day advance warning window. These are illustrative, industry-typical ranges — not guaranteed outcomes from a specific Yegertek deployment.
Implementation Process
Unify
Rubix consolidates data from Engage 365, the CRM, and Puzzle into one profile per customer.
Train
Predictive models for churn, lifetime value, and next-best-action are trained and tuned against your program's actual outcomes.
Personalize
Every offer, message, and reward is ranked per individual, delivered through the CRM, Puzzle, and the mobile app.
Learn
Every customer response feeds back into the models, sharpening the next recommendation.
Technology Stack
- ✓**Core platform:** Rubix, Yegertek's AI/ML and personalization engine
- ✓**Models:** Next-best-action, predictive churn, lifetime-value, dynamic segmentation
- ✓**Explainability:** Factor-level transparency built into every recommendation
- ✓**Data model:** Trained on the shared profile across Engage 365, the CRM, and Puzzle
- ✓**Delivery:** Consistent recommendations across CRM, Puzzle, and mobile app
Integrations
- ✓**ERP & CRM:** Native connection to the Microsoft Dynamics 365 CRM record
- ✓**Commerce & POS:** Real-time behavioral data from POS and e-commerce platforms
- ✓**Communications:** Delivers AI-driven recommendations through existing email, SMS, and push providers
- ✓**Platform:** Native, real-time connection to Engage 365, the CRM, and Puzzle
Built for the region
Regulators in the UAE and Saudi Arabia — the Central Bank of the UAE and the Saudi Central Bank chief among them — apply real scrutiny to automated decisions made about customers in regulated industries. An AI personalization layer sold into this region has to be explainable by design, surfacing the reasoning behind a recommendation, not a black-box model that a compliance team can't defend in a regulatory review.