Propensity and risk models use AI to predict the likelihood that a specific individual will take a specific action, or face a specific outcome, before it happens. We build, train, and operate the models that turn those predictions into intervention windows your teams can act on.
Scoring prospects and customers on likelihood to purchase, upgrade, or churn.
CHALLENGES
The signals that predict who will convert, churn, drop out, or face a clinical risk are already in your data. The challenge is surfacing them in time to act.
Teams respond to outcomes after they happen rather than intervening before they're determined.
Equal effort goes to every individual regardless of likelihood to convert, disengage, or deteriorate.
High-propensity prospects go unprioritized while low-propensity leads consume time and budget.
Risk flags come too late or run on rules that don't account for individual behavioral patterns.
Approach
We build propensity and risk models, designing every model around your specific data environment and the outcome you're trying to predict. One priority model is deployed in production within the first sixty days.
01
Use case definition and feature engineering
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Model training, validation, and historical benchmarking
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Integration into the CRM, SIS, EHR, or operational systems
04
MLOps infrastructure that keeps predictions accurate as data and behavior patterns shift
The output is a production model that surfaces actionable scores at the moment of decision, along with the data infrastructure and MLOps practices that keep accuracy steady.
Applications
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Marketing and revenue teams identifying high-intent prospects and prioritizing outreach
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Marketing and customer success teams detecting at-risk customers before they disengage
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Higher Education teams identifying at-risk students early enough to intervene
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Higher Education advancement teams surfacing high-potential donors and informing ask strategy
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Healthcare teams identifying patients at elevated clinical risk before outcomes deteriorate
Accelerator
For organizations whose churn signals live across structured and unstructured data, we deploy the Churn Prediction Accelerator, a Snowflake-native system that pairs ML scoring with Cortex AI for context and recommendations.
What's included
A Snowpark ML pipeline combining structured signals with unstructured signals from call notes and emails via Cortex AI. Scores weighted by customer value.
Reusable pipeline patterns for common CRM, marketing, support, and commerce data sources.
Natural-language access to scoring tables, customer history, and aggregate rollups. Extendable to Slack, Teams, and existing BI layers.
Streams and tasks pipeline for alerts on score changes. Periodic retraining on new churn outcomes and AE feedback.
Process
01
Customer data audit, churn outcome definition, and source mapping across structured and unstructured systems
02
Feature engineering, ML model training, Cortex AI integration, and threshold tuning
03
Backtesting against historical churn outcomes and benchmarking against the current baseline
04
Snowflake Intelligence rollout, alert pipeline activation, and retraining loop setup
FAQ
They're closely related. Propensity modeling predicts the likelihood of a positive action, such as converting, enrolling, or donating. Risk modeling predicts the likelihood of a negative outcome, such as churning, dropping out, or experiencing a clinical event. In practice many engagements combine both, using the same modeling infrastructure to surface opportunity and flag risk simultaneously.
It depends on the outcome you're predicting, but typically a mix of behavioral data, interaction or transaction history, profile attributes, and outcomes from past cases the model can learn from. We assess your data during scoping and design the model architecture around what you have.
Scores surface inside the systems your teams already use: CRM platforms like Salesforce or HubSpot, student information systems, advancement platforms, EHRs, or marketing automation tools. Integration is part of the build so the score is visible at the moment of decision.
Accuracy depends on the use case and the underlying data. During validation we benchmark each model against the current baseline (segmentation, rules, manual prioritization) and report performance in the terms relevant to the business decision: precision at top-N for prioritization, recall for risk flagging, and lift over baseline.
CRMs typically ship with scoring built around a vendor's generic model and a limited feature set. We build custom models on your data, with feature engineering and outcome definitions specific to your business, and deliver scores into the same CRM workflows your team uses. The result is scoring tuned to how your buyers, students, donors, or patients actually behave.
