Vision

The goal was to shift the Account Executive (AE) and Solution Architect (SA) teams from a reactive to a proactive engagement model. Instead of waiting for a customer to complain or churn, we wanted to predict their "health" score in advance, allowing the team to intervene early, reduce churn, and identify upsell opportunities.

Problem Statement

• Lagging Indicators: Traditional metrics like support tickets or NPS are lagging indicators—by the time they are bad, the customer is already at risk.
• Manual Analysis: AEs were manually trawling through dashboards to guess which customers needed attention.
• Unknown Predictors: It was unclear if our telemetry data actually contained identifying signals for churn or expansion.

Methodology

I conducted a part-time pilot to assess the feasibility of automated health scoring:

• Data Exploration: Leveraged the existing Google BigQuery data warehouse to analyze historical usage logs, billing data, and CRM records.
• Feature Engineering: Used Python and PyPika to programmatically build complex SQL queries, extracted behavioral features (e.g., "active users trend", "feature adoption rate") from raw event logs.
• Correlation Analysis: Modeled relationships between these usage features and historical NRR/churn events.

Outcome

The project was ultimately concluded after the pilot phase:

• Weak Signals: We discovered that purely usage-based metrics had a low correlation with enterprise buying decisions, which are often driven by external factors (budget, champions) not captured in telemetry.
• Data Sparsity: The available data was too sparse to build a reliable forecasting model with high confidence.
• Strategic Pivot: The investigation saved the company from investing in a costly "Customer Health" platform build-out, redirecting focus towards improving data collection fidelity first.