Case Study: Account Diagnostic Tool

Summary

To proactively improve retention, I developed an Account Diagnostic Tool to provide early indicators of account health. By identifying key metrics, aggregating data in Power BI, and collaborating with data science, we built a predictive model for retention risks. While initial scoring needed refinement, iterative improvements enhanced accuracy and insights. This project demonstrated my ability to define problems, synthesize data, and drive cross-functional collaboration for proactive decision-making.

Problem

Retention was one of the key goals of my team, but by the time retention was measured, it was too late to take proactive steps to improve it. We needed an early indicator of the health of our accounts to determine which accounts were at risk, why they were at risk, and what actions we could take to improve retention outcomes.

Process

1. Identifying Key Metrics:

- Gathered easily accessible data metrics.

- Brought together a group of customer-facing specialists to analyze key factors contributing to business loss.

- Synthesized feedback into specific actions or behaviors and aligned them with available metrics.

2. Data Aggregation & Visualization:

- Consolidated data from multiple sources into Power BI.

- Collaborated closely with the engineering and data science teams to integrate and visualize adoption health metrics.

- Developed an intuitive interface to display these metrics and track account health.

3. Challenges & Refinements:

- Initial results were promising, with assigned health scores effectively indicating account health.

- Encountered two major roadblocks:

- Some data sources were incomplete, causing inaccuracies in health scores.

- Initial data analysis indicated that individual indicators had little direct impact on retention, though a combination of multiple indicators showed potential influence.

- Worked with the data science team to refine the predictive model and adjust health indicators accordingly.

Solution

The Account Diagnostic Tool provided early indicators of account health by aggregating behavioral metrics and visualizing them in Power BI. While initial results showed promise, further refinement was needed to ensure data accuracy and validate the effectiveness of predictive indicators. Ongoing collaboration with data science aimed to fine-tune the scoring model to better correlate with retention outcomes.

Outcome

- Successfully built an early warning system for account health assessment.

- Identified key adoption behaviors that correlated with retention trends.

- Addressed data integrity issues to improve accuracy in scoring accounts.

- Iterated on predictive modeling with data science collaboration to enhance retention forecasting.

- Demonstrated the ability to define a problem, collaborate cross-functionally, and drive data-informed decision-making—critical product management skills.