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Zooming out to zoom in: Behavioral Cohort Analysis

Growing a business in 2023 is easier said than done. With surging competition, economic instability, and dwindling customer loyalty, this year has been testimony to the platitude that keeping a customer is easier than getting a new one.

According to a 2022 study, customer acquisition costs increased by 60% from 2017 to 2022, with ecommerce brands losing nearly $30 for every customer acquired. In trying to expand their business, firms often exhaust their resources on customers who never return. One way to solve attrition-related losses is to adopt methods that lean directly into customer retention.

Behavioral cohort analysis is one such technique to address churn and build a loyal customer base. In this process, a large group of customers (or users) are broken down into smaller segments that display certain common characteristics over a period of time. Their shared traits and usage patterns are further analyzed to better track and understand their behavior. For instance, common region, language, preferred time, and purchasing date are some characteristics that can be observed among a group of customers.

Cohort behavioral analysis is preferred by many brands simply because it asks more specific, targeted questions, and helps business leaders make informed product decisions that will reduce churn and significantly increase revenue. This is one of the reasons it is often called ‘customer churn analysis’.

How it works: Spoggle’s cohort behavioral analysis in action

Spoggle adopts a four-step approach to cohort analysis.

  • First, a specific timeline and scope for user retention are identified. The duration could span over weeks and months, and the retention period could be classified as early, middle, and late.

  • The next step involves determining the sticky features of the product. For example, if most users do not return after 1-2 interactions with the product, a good question might be about the quality of customer service. Similarly, if customers do not return after 8-10 interactions with the product, it might help to examine how the product is engaging with loyal customers. Rewarding loyal customers can be a core differentiator between the product and its competitors.

  • The third step involves comparing various behavioral cohorts to determine how these different features are interacting with each other to contribute to churn. After all, it is rarely one feature that single-handedly influences customer retention.

  • The fourth and final step is reiterating, testing, and repeating. A combination of features that are identified in the fifth iteration may reduce churn more than a combination identified the first time around. More viable combinations may emerge with more hypothesis testing.

Why firms should leverage cohort analysis

  • Diagnose business health: A good sign of business health is generating revenue even without new customers. It therefore liberates companies from having to acquire new users and instead shift focus to improving the product for existing users, and for future users by default.

  • Understand customers better: Cohort analysis gives firms a deeper understanding of customers by tracking their behavior over certain timespan. This can help identify patterns and trends that may not be immediately evident from macro metrics. Businesses can create more targeted and effective marketing campaigns based on specific user segments, while also offering more personalized customer experiences. And of course, armed with insights on potential churn risks, firms can take proactive steps to improve customer experiences.

  • Optimize your product: With cohort analysis, firms can identify emerging trends and patterns in the customer lifecycle and optimize user experience and increase their overall CLTV (customer lifetime value, or the total net profit a company can expect to generate from a customer throughout their entire relationship).


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