Churn Analysis:
Discover Factors that Lead to Churn

Every business loses a customer at some point.

This is known as customer churn. No business wants to see this as it slows growth and reduces profits. Businesses can address this by collecting customer data and analyzing them to understand why customers are leaving or staying. The AK Analyst allows you to perform churn analysis especially if an organization collects a lot of customer data.

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The AK Miner finds patterns – the different combinations of factors and their ranges or values that have a significant effect on the probability of churning. Find patterns like:

German female customers in the age range 42-48
and use one product are 51.1% more likely to churn.
1.4% of the customers fit this pattern.

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Inactive German customers in the age range 48-66
and use one product are 69.8% more likely to churn.  
1.6% of the customers fit this pattern. 

img inactive german age range 1
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Use the AK Browser to:

  • Visually investigate different patterns
  • Create pattern lists and organize patterns based on personalized criteria
  • Download or export patterns and the data they cover