Key takeaways
- • Without the right questions, data mining is just data churn.
- • Six of 24 variables, including call resolution and relationship management, drove retention.
- • Dashboards now give relationship managers early warning of at-risk customers.
The challenge
Some customers stayed only a few months, others for years. The client believed it had a retention problem, but didn't know why. "The first task was to validate if they did have a retention problem," explains Sheila Shaffie, who led the engagement using the Lean Six Sigma DMAIC framework.
Our approach
Learn the business, then form hypotheses. We studied dashboards, historical data, processes, contracts, pricing and service channels, then facilitated a session with executives to capture their views on what drives retention. That produced 10–15 potential drivers and a list of 50–60 data points to test them.
Mine the data. Using survival analysis, we iterated: run the data, validate it against reality, gather more. Of the 24 variables studied, six emerged as the crucial drivers: effective call resolution, access method, solutions provided, relationship manager, service type and the customer's industry.
Listen to customers. Interviews with current and former customers confirmed that retention issues were directly linked to missed expectations.
Results
- Dashboards and metrics for relationship managers, giving early warning of retention risk
- A revised pricing model for better customer value, and clearer relationship-management roles
- Average retention expected to improve by 2–2.5 years, doubling it, and $12 million in incremental revenue
Sheila ShaffieCo-founderBusiness transformation leader who honed her skills at three GE businesses: Plastics, Healthcare and Capital. GE Master Black Belt, University of Chicago MBA and co-author of The McGraw-Hill 36-Hour Course: Lean Six Sigma.