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Why Cohort Analysis Can Be a Challenge in Amazon Marketing (But You Should Use It Anyway)

Discover the challenges of classifying customers using cohort analysis for Amazon Marketing teams. We explore how data distribution and customer loyalty can impact your analysis, and provide tips for making better decisions based on your cohort data.
By
Rael Cline
Last updated:
August 24, 2023

Understanding the behavior and preferences of customers is essential for any business looking to optimize marketing strategies and improve customer satisfaction. One popular method used by marketers is cohort analysis, which allows them to group customers based on their purchase behavior over a specific period. Despite its usefulness, cohort analysis has its limitations, particularly when it comes to classifying customers as new to brand or repeat buyers. In this blog post, we explore the complexities and challenges associated with cohort analysis and shed light on the implications they have on data interpretation and decision-making in Amazon marketing.

The Challenge of Classification:

One of the key challenges in cohort analysis lies in the classification of customers as new to brand or repeat buyers. When analyzing customer behavior, we need to start somewhere, and this starting point often leads to misclassification. For instance, if we only consider a six-month period from now, all customers who made a purchase in February 23 would be classified as new to brand, leading to a disproportionately large number of new customers. However, as we extend the analysis to a longer timeframe, such as 24 months, we can see that the cohort sizes become more accurate and reasonable.

The Impact of Data Distribution:

A crucial aspect of Amazon cohort analysis is understanding how customer purchases align with different cohorts. As we delve deeper into the data, we realize that customers who made purchases in a specific month might actually belong to earlier cohorts. This redistribution of customer data across cohorts has a significant impact on the overall analysis. For example, analyzing cohort retention rates reveals that the figures can differ significantly between a six-month analysis and a 24-month analysis. The redistribution of data affects not only retention rates but also sales numbers, thus influencing the interpretation of the analysis.

Complexity in Customer Loyalty:

The challenge of accurately classifying customers as new to brand or repeat buyers becomes more pronounced when businesses have a high level of customer loyalty. In the case of one seller we looked at, where the analysis shows a substantial number of customers who made their first purchase two years ago, continuing to make purchases in right up to the present (August 2023 at time of writing). This level of loyalty is exceptional and demonstrates the complexity of cohort analysis in businesses with highly loyal customers.

Implications for Decision-Making:

Understanding the limitations of cohort analysis is crucial for making informed decisions based on the data collected. Marketers need to keep in mind that the accuracy of cohort analysis improves over time, as the data becomes more robust. Adding more historical data can help overcome some limitations, but it is important to strike a balance between data volume and its impact on cohort analysis.

Conclusion:

Cohort analysis is a valuable tool for Amazon marketing teams to better understand customer behavior and make data-driven decisions. However, it is essential to recognize its limitations, particularly when it comes to accurately classifying customers and interpreting the data. By embracing the complexity of customer cohorts and considering the redistribution of data, marketers can gain deeper insights into their customer base and tailor their strategies accordingly. As customer behavior evolves, it is important to continuously refine and adapt cohort analysis methodologies to ensure optimal decision-making in marketing efforts.

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