Predictive Expansion: Mapping Platform Usage Logs to Upsell Propensity
Account managers lacked visibility into product readiness for upsells. We used Reverse ETL to map Snowflake platform usage logs into Salesforce, generating a predictive Upsell Propensity Score that triggers automated expansion cadences based on gateway feature saturation.
Executive Summary
Context
An Enterprise FinTech SaaS provider needed to automate how it identified upsell and expansion opportunities within its existing Broker-Dealer client base.
What We Built
A usage-based expansion engine that syncs product telemetry data to Salesforce, using a weighted "Gateway" scoring model to trigger automated expansion cadences.
Tech Stack
- Product Usage Logs (Snowflake/BigQuery), Salesforce (CRM), Pardot (MAP), Census/Hightouch (Reverse ETL).
Not a fit for organizations with low-volume usage data or where product telemetry is not yet aggregated in a centralized data warehouse.
The Challenge
Our Approach
Impact
20% Expansion Revenue Increase
Proactive Churn Mitigation
Surgical Marketing Personalization
Developed a weighted scoring model in Salesforce that assigns higher "Expansion Points" to specific Gateway Features, so signals get prioritized by their potential dollar value.
A predictive expansion architecture mapping product telemetry to net revenue retention. It processes Snowflake and BigQuery usage logs through a reverse ETL integration layer into a weighted gateway scoring model in Salesforce. By connecting feature spikes to specific triggers, the system identifies high-propensity expansion opportunities and accelerates data-driven revenue velocity.
FAQ
We implement "Threshold Dampening." An expansion signal only triggers a sales task if the usage threshold, for example high DAO volume, holds for 14 consecutive days. That way, a temporary end-of-month spike doesn't trigger an unnecessary sales outreach.
