Cohort Analysis

Describing the Impact of Cohort Analysis

• Generally, segmentation should aim to reveal hidden properties about customers

  • Specifically, it should illustrate some aspect of causality, and not just provide a descriptive analysis of clustering results
• For example, summarizing any financial results of customers and segments is important
• However, determining a relationship between marketing actions and financial results is typically more actionable

• For example, quantifying how advertising influences customer loyalty and behavioral patterns is actionable

  • E.g. quantifying a customer's migration from one customer segment to another
  • E.g. linking customer demographics to increases in revenue

Segmenting Customers by using Cohort Analysis

• Cohorts should be created for customers with different churn rates

• The following features are some of the most common features used for separating segments:

  • Shopping frequency
  • Engagement frequency
  • Seasonality
  • Purchases or engagement around promotional events

• As a result, each segment should have a different churn rate

  • Because, assigning a segment with an arbitrary or fixed churn rate (used across all segments) may either:

    • Inaccurately label certain customers as churned
    • Or may miss certain churned customers

  • For example, suppose a segment contains customers consistently purchasing during a yearly promo

    • If we naively define a churn window of $1$ year, we may mistakenly treat this customer as churned

      • By having a better understanding of this customer, we can apply a more specialized targeting strategy for this segment of customers (e.g. increasing frequency promos)

  • However, if we naively increase the churn window to $2$ years for all customers, then we may mistakenly label certain customers as non-churned when they actually have churned

    • E.g. if a customer who frequently purchases every week stops purchasing all of a sudden, we wouldn't label this customer as churned for another year or so
    • This would be a big mistake, since this segment of customers would be most likely high value customers
• Then, we can assign different targeting strategies for each segment

• Each targeting strategy should achieve any of the following goals:

  1. Improving shopping (or engagement) frequency
  2. Improving size of orders
  3. Reducing churn rate

References

• Textbook for Fighting Churn with Data
• Textbook about Algorithmic Marketing
• Course about Customer Segmentation
• Course about Segmentation Analysis