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Customer Propensity Scoring

Business Need​

Identify customers most likely to churn, upgrade, or make a purchase within the next 30 days to enable targeted marketing campaigns and improve customer retention rates.

Solution Overview​

This solution uses Abacus.AI's predictive modeling capabilities to score customer propensity across multiple behavioral dimensions:

Step 1: Connect to Data Source

  • Data Source: Customer interaction data is ingested from Azure Storage containers using our native connector
  • Data Structure: Raw data includes customer profiles, transaction history, support interactions, product usage metrics, and engagement events
  • Update Cadence: Daily incremental loads capture the most recent customer behaviors

Step 2: Feature Engineering

  • Feature Groups: Transform raw data into predictive features using Abacus feature groups
  • Engineered Features: Recency-frequency-monetary (RFM) scores, engagement velocity, product adoption rates, support ticket frequency, and behavioral change indicators
  • Temporal Features: Rolling window aggregations (7-day, 30-day, 90-day) capture short and long-term trends
💡Pro Tip

Feature engineering is critical for propensity models. Focus on behavioral change indicators (e.g., declining login frequency, reduced feature usage) rather than just static attributes.

Step 3: Build Classification Model

Using the Predictive Modeling use case, a binary classification model is trained to predict customer propensity (e.g., likely to churn: yes/no). The platform automatically:

  • Handles class imbalance through sampling techniques
  • Selects optimal algorithms (gradient boosting, neural networks)
  • Performs hyperparameter tuning
  • Generates feature importance rankings

Step 4: Generate Predictions

Daily batch predictions score the entire customer base, assigning each customer a propensity score (0-100) and risk segment (low, medium, high).

Step 5: Export Results

Predictions are written back to Azure Storage in a format compatible with the client's CRM and marketing automation tools.

How It's Used in Practice​

The propensity scoring system integrates seamlessly into existing customer engagement workflows:

Daily Scoring Pipeline:

  • Ingests previous day's customer activity from Azure Storage
  • Generates propensity scores for all active customers
  • Segments customers into risk/opportunity tiers
  • Exports results to Azure Storage for downstream consumption

Campaign Integration:

  • Marketing teams import scores into their campaign management platform
  • High-propensity customers receive targeted retention offers
  • Low-engagement customers are enrolled in re-engagement campaigns
  • Sales teams prioritize outreach based on upgrade propensity scores

✨Key Outcomes

Key achievements:

📈 6% increase in retention​

Proactive outreach to high-churn-risk customers

⏱️ Timely scoring capability​

Identify changes in customer patterns fast

Additional Information​