A European market leader in online survey and feedback software acquired complementary companies in
different Wester European countries, each of which had its own survey platform.
This disparity became problematic when data showed sales performance was inconsistent across platforms. One
survey product grew to 50% of the companies overall revenue over five years, while the other product was
shrinking due to customer churn at a rate of 15% per year.
The software company needed a fresh approach.
Using data analytics, they sought to determine the reason for the customer loss, find ways to stop the bleeding,
and renew product growth through enhancements that align with customer need, not guesswork.
Partnering with EastBanc Technologies, the company sought to disciver insights in the sea of product
performance and consumer behavior data that the company had collected. We employed a Minimal
Viable Prediction (MVP) approach. An MVP is an outcome that addresses a primary problem and is the
starting point for any journey to predictive analytics - whether it’s predicting consumer behavior, future
revenues, etc. in the fastest way possible.
To get quick, actionable results, we identified low hanging fruit (data that was both the easiest to analyze, and
most likely to lead to valuable insights). We focused on conservative and long-established events to study user
behavior patterns. Looking at user behavior for a specific survey product, our goal was to identify how user
activity changed over time, which product features had been used and for how long, and which features or
combination of features might be used as predictors of churn.
To boost the predictive power of data, we analyzed absolute and relative user behavior. These perspectives
provide unique insights into seasonal factors, product learning curve, etc.
To empower non technical users the data was presented with intuitive descriptive statistics and visualizations.
Dynamic modeling and unsupervised learning was used to explore and cluster user behavior patterns, while
supervised machine learning was applied to predict customer churn based on system usage logs and CRM and data
insights gathered from exploration.
Our initial findings showed that the amount of product usage was the most important predictor of customer churn.
But we dug deeper. Was it viable that if a customer uses a product X times a month or less, that they would
abandon the product at contract end? If proved correct, the first actionable insight, or MVP, is realized and
action can be taken. If a change in usage patterns occurs, the software company would have X months to act and
re-engage the user (through marketing, training, etc.).
Furthermore, distinct user groups were identified each with discrete behaviors suggesting that each group should
be handled differently.
Using a combination of agile processes, scientific insights, and a strong technology foundation, our method was
unique. With EastBanc Technologies’ help, the organization could develop a realizable goal to reduce churn from
15% to 10% within 12 months.
Future enhancements include the analysis of data usage patterns to inform product roadmaps and enhance consumer
engagement, drive product improvements, improve perceived product value, measure marketing campaign
effectiveness, and more. In addition, deep learning and data mining (intelligent assistant) will be applied to
speed up workflows and empower users.
Microsoft Power BI, Azure Machine Learning Studio, Apache Spark, Google TensorFlow