--- title: Chord Predictive Intelligence Overview slug: chord-predictive-intelligence-overview docTags: createdAt: 2025-11-19T16:22:00.255Z --- Chord’s predictive data science capabilities help brands turn raw customer data into actionable intelligence. With Personas, Churn Predictions, and Purchase-Likelihood modeling available across the Chord platform—and accessible through both Copilot (AI chat) and analytics—teams can quickly activate smarter marketing and retention strategies. ## 1. What Chord Predictive Models Do Chord applies machine-learning models to your customer and commerce data to surface insights such as: - **Customer Personas & Churn Predictions** — Understand who your customers are (e.g., *Deal-Seeker*, *Wellness Enthusiast*, *Loyalist*) and which customers are at risk of dropping off. Personas help you tailor messaging and product experiences, while churn predictions help you intervene proactively. - **Likelihood to purchase or repurchase** — Identify customers most likely to buy next. - **Customer lifetime revenue prediction (CLR)** — Determine who will be most valuable long term. - **Behavioral and commercial patterns** — Insights based on recency, frequency, spend, and engagement trends. ## 2. Why It Matters Predictive intelligence helps teams: - Spend smarter on acquisition - Increase retention and customer lifetime value - Personalize communication at scale - Identify the right customers at the right moment - Make decisions proactively rather than reactively Chord’s models turn your customer data into a real competitive advantage—helping marketing, product, and analytics teams drive measurable growth. ## 3. How to Use These Insights in Chord | Model Name | Predict Level | Output Metric Name | Description | Notes | Input Metric(s) | Data Source(s) | Example Use Cases | Explore(s) | | ---------------------------------- | ------------- | ----------------------------------- | ----------------------------------------------------- | ---------------------------------------------------------------------------------------- | ---------------------------------------------------------- | ----------------------------- | ----------------------------------- | --------------------------------------------------------------------------------------------- | | Customer Lifetime Revenue (CLR) | User | Predicted Lifetime Revenue | Forecasted customer lifetime revenue | Based on historical patterns + user-specific behavior | Orders data, predicted customer ML features, sessions data | Orders, sessions, ML features | Retention targeting, VIP perks | Users | | RFM | User | Recency | Recency, frequency, and monetary scoring | Days since last order. Higher = more recent | Orders data | Orders | Segmentation, churn risk | Users | | RFM | User | Frequency | Recency, frequency, and monetary scoring | Number of orders in time window. Higher = more orders | Orders data | Orders | Segmentation, churn risk | Users | | RFM | User | Monetary | Recency, frequency, and monetary scoring | Total spend in time window. Higher = more spend | Orders data | Orders | Segmentation, churn risk | Users | | RFM | User | RFM Bucket | Recency, frequency, and monetary scoring | Combined RFM grouping | Orders data | Orders | Segmentation, churn risk | Users | | Predicted Repurchase | User | Repurchase Probability | Probability that the user will repurchase | Likelihood of buying again | Orders data, predicted customer ML features, sessions data | Orders, sessions, ML features | Win-back campaigns | | | Product Recommendations | User | Recommendations 1-5 | Top 5 recommended items for user | Top 5 predicted next purchases | Orders data, predicted customer ML features, sessions data | Orders, catalog, ML features | Cross-sell, personalization | Users | | Segmentation Clustering | User | Marketing Segment ID | Unstructured hierarchical cluster assignments | Used for targeting/lookalike audiences/personalization | Orders data, predicted customer ML features, sessions data | Orders, sessions, ML features | Audience targeting | | | Revenue Forecast | Company | Forecasted Revenue | Forecasted top-level revenue | Time series prediction | Time series order data | Orders | Budgeting, forecasting | Predicted Forecasts | | New Customer Forecast | Company | Forecasted New Customer Count | Forecasted count of new customers | Time series prediction | Time series order data | Orders | Acquisition planning | Predicted Forecasts | | Returning Customer Forecast | Company | Forecasted Returning Customer Count | Forecasted count of returning customers | Time series prediction | Time series order data | Orders | Retention planning | Predicted Forecasts | | Probability to Convert | Sessions | Conversion Probability | Probability a session will convert to a paid customer | Likelihood session converts | Sessions and orders data | Sessions, orders | On-site personalization | Sessions, Marketing Attribution - Order Attribution, Marketing Attribution - User Attribution | | Predictive Marketing Attribution | Orders | Attribution % by Channel | Fractional revenue attribution by channel | Model-weighted attribution | Marketing spend, conversions | Spend, conversions | Channel optimization | Marketing Attribution - Order Attribution, Marketing Attribution - User Attribution | | Customer Personas | Users | User Persona Name | Customer segment grouping based on purchase behavior | Personas group customers with similar purchasing patterns and predicted lifetime value. | Customer email | Customer email | Personalization, audience targeting | | | Likelihood to Churn (Percentile) | Users | User Churn Propensity Percentile | Identify customers at risk of churning | Higher percentiles = higher likelihood of churning | Customer email | Customer email | Retention planning, churn risk | | | Likelihood to Churn (Probability) | Users | User Churn Propensity Percentile | Identify customers at risk of churning | Higher values = higher probability of churning | Customer email | Customer email | Retention planning, churn risk | |