- Are you worried about increasing customer churn due to the coronavirus and economic downturn in 2020?
- Got data about your customers but don’t know what to do with it?
- Turn your data into ammunition for targeted engagement campaigns and anti-churn interventions!
Want to learn more? Check out my
A book about churn
To go in depth, read the only book dedicated to customer churn:
The printed (hard copy) book is expected to be released in the Summer of 2020…
Churn is the bane of subscription products and channels, and Data Scientists and analysts are commonly called to the rescue in helping companies understand what causes subscribers to churn, what is predictive of churn, and what can be done to reduce it.
While examples of churn are available in benchmark data sets, analyzing churn in the real world is fraught with challenges and pitfalls for the novice data scientist or analyst. Churn analysis begins with constructing a data set combining multiple raw data sources, and only ends when the findings of the analysis are communicated to field representatives in a way that allows them to actually reduce churn.
That’s right – a predictive AI system alone doesn’t cut it when churn is at stake: To reduce churn you need to put your scientist’s hat on, test hypotheses and communicate the result to the non-technical crowd! But don’t worry: The book and this site will cover every phase of the process, including the most dangerous pitfalls and case studies illustrating common findings. The information here will allow anyone with only a modest data analysis background to get churn analysis right the first time.