Paper Company

GOALS

  • A German media publishing company asked us to crete a solution to predict customer churn
  • As preserved revenue from non-churning customers is highly profitable and directly contributes to the topline, our mail goal was to reliably predict customer churn cohorts and make those cohorts adressable for customer success outreach

Data

  • Account master data
  • Demographic data
  • CRM data including products consumed
  • Customer review
  • topics, articles, authors

Analytics

  • Machine learning and statistical modelling algorithms
  • Big Data technologies for creating analytical boards and models
  • Python
  • Sentiment Analysis Algorithms
  • Cloud environment: AWS

Business top line KPIs focus to identify growing or declining businesses

  • Revenues growth
  • Liablities growth
  • Asset growth
  • Web traffic, relevance, news coverage, job opening, employees growth

Results

  • Our final model predicted churning customers with over 90% accuracy
  • Based on our model, we built a solution that was integratable into the existing CRM system and directly usable for customer success representatives do approach customers with a high churn-risk