Reducing churn - with insights from a churn analysis

Sound familiar? If your organisation offers subscription services, reducing churn is probably on the agenda. It is not the most popular topic, because we naturally prefer to look at what is going well. But what is the best way to analyse churn? Where do you start? In this blog, we explain how you can use data to analyse churn with the aim of reducing it. 

What is churn? And why is reducing it so important?

Churn is the number of customers who stop using your company’s services or products. It shows how well a company retains its customers. Churn has a direct negative impact on your organisation’s revenue and profitability. Losing existing customers means an immediate drop in recurring revenue, and acquiring new customers is often more expensive than retaining the ones you have. 

And what about your competitive advantage? Retaining customers makes your revenue more predictable, which brings stability. It lets you focus on growth instead of replacing customers you have lost. That can help you stay ahead of your competitors. Besides, what could be better than a strong product with satisfied customers?

1. Collecting and organising subscriber data over a given period

In our analysis, we focused on subscribers’ online use of two news websites and their associated apps.

Our goal sounds simple: improve subscriber retention. We want to understand why some subscribers cancel their subscriptions and identify the factors that contribute to this. Understanding these patterns allows us to develop targeted strategies to improve subscriber retention.

For our churn analysis, we began by organising a large volume of subscriber data collected previously. The data was spread across several tables, so our first task was to review them carefully and bring together the relevant information. We selected the most important factors based on domain knowledge and supported by a literature review (Belchior, L. M., António, N., & Fernandes, E. (2024).

To bring the data together, we used dbt (Data Build Tool) alongside Snowflake. Snowflake serves as the central repository for all our tables, while dbt lets us transform the data efficiently and systematically. With dbt, we can build models that select the most important columns, perform calculations and ultimately create a combined dataset. This allows us to process our data consistently and repeatably, giving us a robust foundation for further analysis.

After consulting experts in the field, we chose an analysis period of one year. This period provides a good overview of subscriber behaviour. One drawback is that seasonal factors are not included in the analysis. This could affect how we interpret the results, especially if churn patterns vary considerably by season.

Because our dataset is so large, it is essential to break the transformations down into short periods. In this case, we chose to add data to our dataset monthly, although the right interval depends heavily on the number of customers you work with. We used Conveyor for this task.

2. Defining churn

An important step in analysing churn is to establish a clear definition of it. The definition you use can have a considerable effect on the analysis, so it makes sense to align it with your goal. Because we are interested in the behaviour behind churn, we chose fairly strict rules. For example, we did not classify subscribers who returned within two months as churners. This helps us avoid noise in the dataset, as many subscribers temporarily cancel their subscriptions to get an additional discount. If you are more interested in revenue or monthly customer loss, it may make sense to include these subscribers in the analysis.

3. Data analysis and interpretation

After combining and organising the data, we identified the key variables that could influence subscriber churn. We then analysed these variables to find patterns indicating a higher risk of churn. During this analysis, it became clear that grouping subscribers into segments was essential to refine the insights.

We chose to base the segmentation on three key criteria: subscription type, the length of the subscriber’s relationship with the organisation, and subscriber activity. These segments allowed us to see which factors played a significant role in churn risk within each group. 

By segmenting the data in this way, we could better identify patterns and behaviours specific to each subscriber group.

Conclusion

Analysing and addressing churn provides the following valuable insights:

• Churn analysis reveals why customers leave, helping you improve your product.
• Identifying churn risk factors enables proactive retention strategies.
• Reducing churn pushes you to keep adding value for your customers.
• By actively tackling churn, you can retain your customer base, grow and improve your services as a subscription business. This leads to a stronger market position and sustainable long-term success.