How does Rubix identify at-risk members?

Rubix scores members on retention risk using behavioral patterns from Engage 365 and the CRM — transaction frequency, tier trajectory, redemption behavior, and engagement history.

How are retention interventions personalized?

At-risk members are segmented by their risk profile — the intervention applied to a member lapsing due to low redemption differs from one applied to a member whose tier status is declining.

How is the effectiveness of retention actions measured?

Every trigger is connected to a retention outcome in Rubix, so save rates are measured directly rather than inferred from campaign metrics.

Retention Software

Software That Identifies Retention Risk Before It Becomes Churn

Rubix and Engage 365 combine to give retention teams real-time churn signals, predicted lifetime value, and the next-best-action recommendations needed to intervene before a customer leaves — not after.

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Overview

Customer retention management is a reactive discipline in most organizations: churn is identified from the data, a win-back campaign is designed, and the campaign reaches members who have already decided to leave. Yegertek's retention management capability flips this sequence — AI-powered signals from Rubix surface retention risk early, before the member has made the decision, and feed directly into the engagement layer that can act on it.

The Challenge

✕Churn identified after the fact, when win-back campaigns are the least effective tool available.
✕Retention signals buried in data that requires manual analysis to surface.
✕Interventions that are too generic — the same offer sent to every at-risk member regardless of why they are at risk.
✕No measurement framework connecting retention interventions to actual retention outcomes.

Our Approach

Retention management is built on Rubix's AI scoring layer, which analyzes behavioral patterns from Engage 365, the CRM, and transaction data to score every active member on retention risk. High-risk members are surfaced automatically, and the engagement layer triggers a tailored intervention — a targeted offer, a tier reinforcement message, or a service outreach — before the member decides to leave.

Capabilities

Outcome-labeled features, not a laundry list.

AI-powered churn prediction

At-risk members identified weeks before they lapse, when interventions are most effective

Lifetime value scoring

Retention effort prioritized by the value of the member relationship being protected

Automated retention triggers

Targeted offers and communications dispatched automatically to at-risk segments

Root-cause segmentation

At-risk members grouped by why they are at risk — lapse, tier decline, redemption failure — so the right intervention is applied

Intervention effectiveness measurement

Every retention action connected to an actual retention outcome, not just a click rate

Real-time risk dashboard

Retention risk across the active member base visible without manual reporting

Business Benefits

Retention software that acts on predictive signals rather than confirmed churn events is materially more effective — because the window of influence is still open. Interventions applied when a member is at risk of leaving, not after they have left, produce dramatically higher save rates. And because every intervention is connected to an outcome measurement, the retention team can continuously improve which actions work for which member profiles.