What are propensity models: a complete guide for consumer brands

Momentum Solar's call center was working hard, but still reaching the wrong people. But after replacing their rules-based scoring with a propensity model, they hit the same appointment goal with 33% fewer outbound calls. The leads didn't change. The model just finally knew which ones were worth calling.

Today, almost every marketer has felt the same frustration: you have thousands of customers or leads, and you know some of them are about to buy, some are about to churn, and some are worth ten times more than others — but you can't tell which is which.

Propensity models solve this. They predict how likely a specific person (or household) is to take a specific action — whether that is buy, churn, reactivate, upgrade, apply for a loan — so you can treat each person according to their actual calculated likelihood to perform a behavior, rather than a guess.

This guide covers what they are, how they work, and how consumer brands use them across the full customer lifecycle.

What is a propensity model?

A propensity model is a machine learning algorithm trained on your historical customer data to predict the probability that a given individual will perform a defined action — any of the behaviors described above, and more.

At Faraday, propensity models are trained on both your first-party outcomes and the Faraday Identity Graph (FIG) — 1,400+ verified consumer data points across 240M U.S. adults — which gives the model far more signal to work with than first-party data alone.

The model learns by studying examples — customers who did the thing you care about, and customers who didn't — and identifying the patterns that distinguish the two groups. It then applies those patterns to everyone else in your database and assigns each person a score representing their probability of performing the action.

Those scores are the output. What you do with them is up to you — prioritize your call center queue, build a suppression list, trigger a retention campaign, identify lookalike acquisition audiences. The model is the engine; your stack is the vehicle.

How consumer brands use propensity models

Use case	What it predicts	Best for	How teams use it
Lead scoring	Likelihood to convert	Sales and marketing teams	Rank leads by conversion probability; focus reps on high-probability prospects and move low-probability leads into automated nurture paths or suppress them entirely
Churn prediction	Likelihood to stop buying	Retention teams	Trigger a personalized retention offer, check-in, or discount at the moment a customer starts to disengage — before they've already left
Reactivation	Which lapsed customers will respond to a win-back	CRM and lifecycle teams	Target win-back campaigns precisely instead of spending equally on everyone who hasn't bought in 90 days
LTV prediction	Long-term revenue value per customer	Acquisition and retention teams	Prioritize spend toward high-LTV customers; used alongside churn scores, a high-churn/high-LTV customer is worth fighting for — a high-churn/low-LTV one might not be
Next best offer	Most relevant offer per customer at each lifecycle stage	Financial services, subscription brands, and deep-catalog retailers	Stack one model per product and surface the most relevant offer at each point in the customer lifecycle

How propensity models work

Faraday's prediction system evaluates multiple algorithms — individual decision trees, logistic regressions, neural networks, and random decision forests — and selects the best fit for each use case at run time. In most cases, the winner is random decision forests.

A random decision forest is made up of dozens of individual decision trees. Each tree is a classifier — a flow chart that maps a series of data splits to a predicted outcome. The forest combines the outputs of all those trees to produce a single probability score that's more accurate and more stable than any individual tree alone.

Three properties make random decision forests particularly well-suited for consumer data:

• Handling missing data: Unlike neural networks or logistic regressions, which require complete values to function, random decision forests navigate missing data seamlessly. If a value is absent at one node, the algorithm moves to the next decision path — keeping the prediction accurate even when individual records are incomplete.
• Identifying collinearity: Consumer data is inherently noisy, and many attributes are correlated — household income and dwelling type, for example, or age and life-stage indicators. Random decision forests find areas of greatest information gain, identifying which of those interdependent data points gives the model the most leverage over the prediction rather than double-counting correlated signals.
• Explainability: Because the model maps discrete decision splits, Faraday can surface exactly which data points contributed most to each score — household signals, life events, spending trajectory, and more. You're not trusting a black box; you're seeing the reasoning.

The data foundation matters

A propensity model is only as good as the data it trains on. First-party data — purchase history, engagement data, CRM records — tells the model what your customers have done with your brand. It doesn't tell the model who those customers are.

That's the gap the Faraday Identity Graph fills. FIG covers 240M U.S. adults across 1,400+ verified consumer data points — demographics, financial signals, property data, life events, lifestyle indicators, and more. When Faraday builds a propensity model, it trains on your first-party outcomes enriched with FIG data, which means the model has far more signal to work with than CRM-native scoring tools that are limited to whatever happens to be in your system.

FIG also provides historical data — full historical timelines for each data point, not just current-state snapshots. This matters for training because customers don't look the same after they convert as they did before. A model trained on current-state data is learning the post-purchase signature, not the pre-purchase one. Historical data lets Faraday train on what a customer looked like just before they took the action you care about — which makes the model dramatically better at finding genuine future prospects.

Momentum Solar: 33% fewer calls to hit the same goal

Momentum Solar — one of the fastest-growing solar installers in the country — had a lead prioritization problem. Their call center was operating on a rules-based scoring system built around a handful of data points: lead source, demographics, homeownership status, location. The rules were subjectively chosen, static, and couldn't account for the full range of signals that actually predict whether a homeowner will book a solar assessment.

The result: reps were spending time on leads that would never convert, while high-probability leads got the same treatment as everyone else.

Momentum partnered with Faraday to replace their rules-based system with a propensity model trained on their historical customer base and enriched with FIG data. Leads were scored dynamically — updated as new customer examples were added and lead behavior was observed — and bucketed into three tiers: green (high probability), yellow (mid), and red (low).

The results from a clean A/B test against their previous approach:

• +191% lift in conversion rate for green-tier leads vs. baseline
• +51% lift for yellow-tier leads vs. baseline
• 33% fewer outbound calls needed to hit the same appointment goal

The red group converted below baseline — confirming exactly where the call center should and shouldn't spend its time. Read the full Momentum Solar case study.

Propensity models across verticals

The same framework applies across industries and use cases. A few examples from Faraday customers:

• E-commerce: A leading subscription box brand replaced manual product curation rules with a two-sided propensity model — scoring both customer affinity and product performance — and achieved a 5% lift in revenue per customer, a 3% reduction in churn, and 5x monthly ROI. See the full story.
• Financial services: Advia Credit Union used propensity scoring to identify which members were most likely to apply for an auto loan, built with FIG enrichment and bias mitigation for fair lending compliance. Application rates lifted from 1.19% to 5.18% in 90 days, generating $2.7M in new loans. See the full story.
• Home services: American Standard used propensity scoring to transform their contact center from a top-of-funnel filter into a full-funnel routing optimizer — moving high-fit leads to the front of the queue, mid-tier prospects into the right workflows, and lower-fit records into nurture paths. Contact rate lifted from 5.56% to 20%, and high-score leads converted at 3x the rate of others. See the full story.

Ready to get started?

If you want to talk through how propensity modeling could work for your business, talk to a Context Consultant. Or if you'd rather get started on your own, try it on buy.faraday.ai.

FAQ

What's the difference between a propensity model and a lead score?

A lead score is one type of propensity model — specifically, one that predicts likelihood to convert. But propensity models can predict any defined action: churn, reactivation, loan application, product adoption, and more. Lead scoring is the most common starting point, not the full picture.

How much data do I need to build a propensity model?

Faraday typically needs a few hundred historical examples of the outcome you want to predict — customers who converted and customers who didn't. Because FIG enriches every record with 1,400+ data points, even a modest first-party dataset can produce a strong model.

How is this different from the scoring tools built into my CRM?

CRM-native scoring tools are limited to whatever data lives in your system — typically engagement signals like email opens, form fills, and page views. Faraday trains on those first-party signals enriched with external consumer context from FIG, which gives the model far more signal to work with and produces significantly higher predictive lift.

How do I know if the model is actually working?

Faraday validates every model using holdout testing before deployment — reserving a portion of your data that the model never sees during training, then measuring how well it predicts outcomes on that holdout group. You can also run ongoing A/B tests in-market to measure lift against your existing approach.

Can propensity models be used in regulated industries like financial services?

Yes. Faraday builds bias mitigation into the modeling process — rebalancing training data to correct for historical underrepresentation and monitoring scores for fairness across protected classes. For a detailed example, see how Advia Credit Union used propensity scoring with fair lending compliance built in.