Propensity Modeling (Churn, Purchase, Upsell)
Multi-model ML framework predicting customer behaviors with 75-85% accuracy enabling proactive retention achieving 30-50% churn reduction and 25-40% upsell conversion improvement.
Why This Matters
What It Is
Multi-model ML framework predicting customer behaviors with 75-85% accuracy enabling proactive retention achieving 30-50% churn reduction and 25-40% upsell conversion improvement.
Current State vs Future State Comparison
Current State
(Traditional)1. Customer success team discovers churn reactively: customer cancels subscription, calls to request account closure. 2. Team attempts win-back offer: '20% discount to stay', but customer already committed to competitor (too late). 3. Sales team identifies upsell opportunities manually: reviews all customer accounts quarterly, sends generic upgrade offers to entire base. 4. Low upsell conversion 3-5%: most customers not ready or interested, offers not targeted. 5. High-value customer churns unexpectedly: no warning signs detected, account seemed healthy last quarter. 6. Post-mortem analysis: customer engagement declining for 6 months (login frequency dropped 60%, support cases increased 3x) - signals missed. 7. Annual churn 25-30%, reactive retention (save <20% of at-risk customers), untargeted upsell (95% ignore offers).
Characteristics
- • Salesforce
- • SAP
- • Google Analytics
- • Python (Scikit-learn, XGBoost)
- • Tableau
- • AWS SageMaker
Pain Points
- ⚠ Data Quality & Integration: Siloed data and inconsistent formats.
- ⚠ Feature Engineering Complexity: Requires domain expertise and manual effort.
- ⚠ Model Interpretability: Complex models are hard to explain to stakeholders.
- ⚠ Class Imbalance: Rare events can lead to biased models.
- ⚠ Model Drift & Maintenance: Models degrade over time and require regular updates.
- ⚠ Scalability: Large datasets can strain infrastructure.
Future State
(Agentic)1. Propensity Modeling Agent monitors all customers continuously: login frequency, feature usage, support cases, payment patterns, engagement scores. 2. Agent detects early churn signals: 'Customer ABC Corp churn risk 75% (high) - login frequency dropped 60% over 90 days, support cases increased 3x, invoice payment delays increased from 15 to 45 days, contract renewal in 60 days'. 3. Agent triggers proactive retention workflow: assigns customer success manager, recommends retention offer: 'Offer 15% renewal discount + dedicated onboarding for underutilized premium features (detected only using 30% of paid capabilities)'. 4. CSM contacts customer 60 days before renewal (vs reactive cancellation call), addresses pain points, customer renews. 5. Agent identifies upsell opportunity: 'Customer XYZ Corp purchase propensity 80% for Premium tier - using 95% of current plan limits, frequent feature requests for Premium-only capabilities, budget approved (LinkedIn job post for analyst role - growth signal)'. 6. Sales rep receives targeted upsell alert, contacts customer at optimal timing, conversion success. 7. 30-50% churn reduction (15-18% annual churn vs 25-30%), 25-40% upsell conversion improvement (8-10% vs 3-5%).
Characteristics
- • Customer engagement metrics (login frequency, feature usage, session duration)
- • Support interaction data (case volume, severity, resolution time)
- • Payment and billing patterns (invoice delays, payment method changes)
- • Product usage telemetry (feature adoption, capacity utilization)
- • Contract and renewal dates
- • External signals (LinkedIn growth indicators, competitor intelligence)
- • Historical churn and upsell patterns for model training
- • Customer health scores and risk indicators
Benefits
- ✓ 30-50% churn reduction (15-18% vs 25-30% annual churn)
- ✓ Proactive retention (60-90 day early warning vs reactive cancellation)
- ✓ 75-85% churn prediction accuracy (catch most at-risk customers)
- ✓ 25-40% upsell conversion improvement (8-10% vs 3-5%)
- ✓ Targeted upsell timing (contact when propensity high)
- ✓ Automated workflow triggers (CSM assigned, retention offers recommended)
Is This Right for You?
This score is based on general applicability (industry fit, implementation complexity, and ROI potential). Use the Preferences button above to set your industry, role, and company profile for personalized matching.
Why this score:
- • Applicable across multiple industries
- • Higher complexity - requires more resources and planning
- • Moderate expected business value
- • Time to value: 2-8
- • (Score based on general applicability - set preferences for personalized matching)
You might benefit from Propensity Modeling (Churn, Purchase, Upsell) if:
- You're experiencing: Data Quality & Integration: Siloed data and inconsistent formats.
- You're experiencing: Feature Engineering Complexity: Requires domain expertise and manual effort.
- You're experiencing: Model Interpretability: Complex models are hard to explain to stakeholders.
This may not be right for you if:
- High implementation complexity - ensure adequate technical resources
- Requires human oversight for critical decision points - not fully autonomous
Parent Capability
Marketing Mix Modeling (MMM)
Delivers continuous MMM refresh with instant scenario testing, automated recommendations, and integrated planning achieving significant media efficiency improvement.
What to Do Next
Related Functions
Metadata
- Function ID
- function-propensity-modeling-churn-purchase-upsell