Chord Predictive Intelligence Overview

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