Retain to Reign
Driving Customer Loyalty Through Analytics
Chapter 3 - Identify & Arrest Customer Churn
Losing customers is not just a setback; it's expensive! Consider this
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Reducing customer churn by a mere 5% can skyrocket profits by as much as 125% since Repeat customers boast higher basket values and are more receptive to upselling or cross-selling newer products
The good news? The majority of reasons behind customer churn are identifiable and actionable. In this era of data sciences and machine learning, having proactive systems in place to predict churn before it happens becomes imperative. And as we resume our series on customer retention, let's delve deeper into the second component of the retention puzzle: The Churn Prediction Model
Defining “Churn”
Before diving into predictions, we must first define churn. While it's straightforward in subscription or contractual settings where customers voluntarily opt out, in industries like e-commerce or hospitality, churn is often veiled. There needs to be a clear, personalized definition of the metric "churn."
For instance, a grocery store might consider a month of inactivity as potential churn, while a fashion e-commerce website might extend this to three months, and a boutique hotel chain could look at 2 to 3 years. It's essential to get this definition right, as many businesses assume a standard churn period of 6 or 12 months without delving into their own data to understand the optimal time frame
Cracking the Code of Churn Prediction
Now that we've defined churn, prediction becomes a classification problem. It typically hinges on several factors:
âž– Product satisfaction (via NPS surveys, reviews/ratings, etc.)
âž– Engagement (website/app visits, recent activity)
âž– Customer loyalty (number of purchases, frequency, diversity of categories)
âž– Experience (delivery delays, contact center escalations, etc.)
The output is a probability (ranging from 0 to 1) indicating the likelihood of customer churn, along with potential contributing factors. Businesses can leverage this to segment their customer base and prioritize retention efforts accordingly. For instance, proactive customer support initiatives for those with bad return experiences or coupon-led offers for those facing delayed deliveries.
Sophisticated algorithms like Logistic Regression, Support Vector Machines, and Random Forest can power these predictions. The right choice depends on your data. Need guidance? Our team of data experts is here to assist you every step of the way.
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