How does loyalty analytics connect to business outcomes?

The analytics layer uses the same data model as CRM, service, and transactions — so loyalty behavior can be attributed to retention, LTV, and incremental revenue.

How is points liability tracked?

Engage 365 maintains a real-time ledger of outstanding points and projected redemption, surfaced for finance teams.

How are churn signals identified?

Rubix's AI layer scores members on lapse risk using behavioral patterns from Engage 365 and the CRM, surfacing signals before members actually lapse.

Loyalty Analytics

See What Your Loyalty Program Is Actually Doing

Loyalty analytics that connects program behavior to retention and revenue outcomes — not just membership counts and points issued — so decisions about program economics are made on real evidence.

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Overview

Most loyalty programs are measured by their own operational metrics — members enrolled, points issued, redemption rate — without connecting those metrics to the outcomes that justify the program's existence: retention, lifetime value, and incremental revenue. Yegertek's loyalty data analytics service builds the measurement framework that connects program performance to business performance, using Rubix's AI layer alongside Engage 365 and the shared CRM data model.

The Challenge

✕Program performance measured in isolation — no connection to retention or revenue outcomes.
✕Redemption and earn rates reported without liability and margin context.
✕Churn and lapse signals that appear in the data but are never acted on in time.
✕Analytics that require manual data pulls from multiple systems, always trailing the current state.

Our Approach

Analytics are built on the same shared data model as the rest of the platform — so loyalty behavior, CRM history, service interactions, and transaction data all flow into the same measurement layer. Rubix surfaces churn risk, lifetime-value predictions, and next-best-action signals from this unified dataset.

Capabilities

Outcome-labeled features, not a laundry list.

Retention and LTV attribution

Program performance connected to the retention and revenue outcomes that justify it

Cohort and tier analysis

Performance compared across member segments, tiers, and enrollment periods

Points liability monitoring

Outstanding liability and projected redemption tracked in real time for finance teams

Churn and lapse prediction

AI-powered signals from Rubix identify at-risk members before they lapse

Campaign effectiveness measurement

Every marketing trigger connected to an engagement and revenue outcome

Real-time operational dashboards

Program KPIs visible without manual reporting runs

Business Benefits

Analytics built on a unified data model produce decisions that are grounded in evidence rather than based on operational metrics that do not tell the whole story. Finance teams gain visibility into points liability. Marketing teams gain attribution data that connects campaigns to revenue. And the loyalty team gains the churn signals needed to intervene before a member is lost rather than after.