The Problem
Most loyalty reports show points issued, points redeemed and member count. None of these prove the programme drives business. CFOs ask "what's the ROI of loyalty?" and the team stalls.
The Fundle Approach
Fundle's analytics layer treats loyalty as an instrument of behaviour change. Repeat rate, visit frequency, ATV growth per cohort, CLV uplift, cohort migration between segments, AI-detected anomalies, and a natural-language query interface that lets your CMO ask data questions in plain English.
Core capabilities
Everything you need, native — not stitched together from three vendors.
Repeat-rate engine: 30-, 60-, 90-day rolling rates by segment
Cohort analytics: by acquisition month, store, channel, category
CLV model: predicted at enrollment, refined every transaction
Anomaly detection: alerts when KPI deviates 2σ from expected
Store leaderboard: 20+ KPIs ranked across the chain
Natural-language queries: "show me at-risk fashion members in Mumbai who spent > ₹10k last quarter"
Liability forecasting: points outstanding, projected burn, breakage estimate
Incrementality reports: per-campaign and programme-wide
In production
CFO board pack
One-click export: incremental revenue, CAC payback, CLV uplift, points liability movement.
Store ops weekly review
Top-/bottom-10 stores with AI-generated commentary on root causes.
Cohort retention deep dive
Why did the Jan-2026 cohort outperform Dec-2025? Drill from RFM to category to channel.
Frequently asked questions
Is this a BI tool?
No — Fundle has BI built in, but it's a loyalty intelligence platform. Models know loyalty constructs (tiers, points, redemption) natively, so reports are accurate without modelling.
Can we connect Looker / Tableau / Power BI?
Yes — analytical warehouse access via Snowflake/BigQuery sync. But most teams stay in Fundle because the loyalty constructs are native.