Problem: Too Many Customers Leaving

Solution: Predict & Target Customers At-risk for Leaving

Outcomes:

• Machine learning algorithms accurately (95%) predict thousands of customers’ future behaviors on a weekly basis.
• Save $100K+ in accounts each year for the organization.

How We Did It

Predictive Analytics Consulting

This company with a global presence in 60+ countries wanted to retain more accounts during their yearly sales process. Working together, the team at Process Zip conducted an initial state analysis and realized that although the company had a lot of data, it was not being used to predict which accounts were at risk for leaving.

With our Predictive Analytics Consulting service, we worked with the organization to find the right data and tools to set up an automated workflow. Using 5 years of past data and predictive analytics, at-risk for leaving scores were created for each of the organization’s accounts. Scores were 95%+ predictive, meaning that they were highly reliable for targeting the leaving clients.

Automated Predictive Analytics Workflow

Now, the organization uses this automated process and its predictive scores to target the at-risk accounts with incentives to stay, and it has improved its customer experience because it knows which products can drive client retention.

Learn more about how you can save time and save your business in our popular Medium.com article To Kill a Timesuck.