# Customer Health Forecasting

> Investigation into predictive analytics for Net Revenue Retention (NRR) and customer churn

- HTML version: https://robbiepalmer.me/projects/customer-health-forecasting
- Status: completed
- Started: 2022-09-01
- Technologies: Python, Google BigQuery, PyPika

# 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.

---

Markdown index of this site: https://robbiepalmer.me/llms.txt