Some Ideas and Techniques for Customer Churn Analy...

EnterpriseDNA · ‎03-25-2020

In this article, we’re going to get more advanced and dive into customer churn analysis in Power BI. The other term for this is customer attrition. In simple words, we’re going to analyze who our lost customers are. Moreover, we want to see how many we’re losing, how many are coming back, and how many are new or coming on board. Power BI is the best analytical tool for this type of analysis.

Analyzing your customer churn is key to your business, especially if you’re an online retailer, a supermarket chain, or any high frequency selling organization.

We can extract many valuable insights with this quite advanced analysis, which is why I made this blog with my tutorials around a customer churn analysis. In these tutorials, I go in-depth with the DAX formulas and Power BI techniques.

In this first video tutorial, I work out how many customers are considered lost and identify who these lost customers are. Furthermore, with complex DAX formulas, I’m able to dynamically drill into those lost customers and see the total sales that were lost from them.

[video]

In this example of customer churn analysis, I classify a lost customer as one who has purchased a product in the last 10 months but not in the last 2 months or 60 days. I combined several DAX functions, specifically CALCULATE, CALCULATETABLE, DATESBETWEEN, EXCEPT, VALUES, FILTER, and ALL, to get these results. I also utilized some variables (VAR) in my calculation, which is a great technique you can use for this analysis.

This is powerful stuff if you want to make sure your sales resources and marketing efforts are going to the right areas, where you feel you can generate the best returns. There are many great practical applications to this technique, so check out how I managed to do this inside Power BI.

Now how about we go deeper? In this tutorial, I show not only the lost customers, but also the new customers and the returning customers, using a combination of many DAX functions in Power BI. The key here is to understand the DAX functions and learn how to combine them correctly and efficiently.

[video]

In this demonstration, I first go through the customer churn, exploring how many customers are lost after a certain period. Then, I go into working out who the new customers are as well as who are the returning ones.

Existing customers are significant to the business. Marketing to them is much more profitable compared to finding new customers. Selling to existing customers is much easier than trying to sell to new ones.

With a customer churn analysis, you will understand why you’re losing some of your customers, giving you better ideas on how to keep them. On the other hand, having new customers on board is better than losing them to your competitors. Moreover, you also need to value your returning customers, who were ones considered lost but bought again.

This is an amazing insight if you’re doing some marketing and promotions. You’ll see how many of your lost customers you get back through your marketing efforts. If you want to explore more on the broader perspective of customer analysis, in relation to customer churn analysis, I highly recommend you check out these tutorials as well.

The first one features cohort analysis in Power BI, wherein I group the customers based on their first purchase, then I analyze their retention rate. Through time, I analyze how long it takes for these customers to purchase again. Cohort analysis is a quite advanced analytical technique that you can implement in Power BI in a dynamic way.

The second tutorial I recommend is about reviewing customer performance over time. Here I’m looking at the purchasing behavior of customers over different periods and put them into a visualization.

Key Takeaway

Customer churn analysis is quite an advanced topic in Power BI, but it generates very interesting insights for your business. I hope you can see how this powerful analysis can help you make better decisions in your organization around marketing and advertising as well as resourcing.

This analysis is best shown with the analytical ability of Power BI. In the past, it would cost large amounts of money and takes a lot of time to generate this information from this type of advanced analysis. But now with Power BI analytics, you could get these awesome insights through DAX formulas.

The key to achieve this is to have a good understanding of how to combine correct DAX functions and structure your data model correctly, as everything is incorporated in there.

Enjoy reviewing this one!

Sam

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