Customer Churn Analysis Insights with Advanced BI Tool
If customers stop using a service, the company usually asks for their feedback and users can share things they didn’t like. In retail, it is much more complicated. Customers who used to buy in your store and then stopped, won’t come and tell you what they didn’t like. And they won’t write emails on what you need to change and improve. That’s why it’s even more critical for retailers to understand customer churn and why it happens.
In many cases, customers do not make a specific decision to stop buying in your stores, they leave gradually. This means that customer churn in retail chains looks like a slow churn: a gradual decrease in the frequency of sales. So customers either spend less in your store or go to competitors.
Let’s say a customer used to buy groceries from you twice a week, spending $50 each time. After a year, their visits to your store dropped to once a month, and their spending dropped to $20 per purchase. So it looks like you risk losing the customer forever.
By analyzing sales data and the customer lifecycle, you can detect signs of customer churn in time and make decisions to retain customers. For example, organize promotional campaigns.
What is Customer Churn?
Customer churn is an indicator of the number of customers who have not made a repeat purchase within a certain period of time. Of course, every company and every retail chain faces this problem. The main question is how high the customer churn is and what loss of revenue is caused by the customer churn.
The opposite concept of customer churn is customer retention. After a customer has made the first purchase, they will either buy again (retained) or not (customer churn).
What is the Customer Churn Rate?
The churn rate, sometimes known as the customer loss rate, is the rate the customers stop interacting with a company over a certain period of time. The higher the churn rate, the more customers stop buying. The lower the churn rate, the more customers you retain. In general, the lower the churn rate, the better.
How to calculate the customer churn rate?
(Lost Customers ÷ Total Customers at the start of time period) × 100
Let’s say I want to calculate the monthly churn rate. We started a month in our store with 300 repeat customers. The 30 customers out of them didn’t make any purchases during this month, so the churn rate will be:
The churn rate = (30 ÷ 300) × 100% = 10%
What is a good customer churn rate?
I asked ChatGPT what is the average customer churn rate for a supermarket chain and I got the answer: “…don’t have access to real-time data, I can provide you with a general perspective based on historical industry trends. …. On average, annual customer churn rates for supermarkets can range from 5% to 20%, but this can vary significantly depending on the specific circumstances.”
So, here it is, the main takeaway is that you need historical and real-time data to analyze and understand the customer churn in the retail chain.
Customer churn analysis
Customer churn analysis is a method that helps to measure and understand how this indicator affects the business and what are the reasons for losing customers.
In order to study store customers and analyze their purchases, BI tools are used to help quickly analyze the size and structure of customers of the chain’s loyalty programs. One of the reports of the Datawiz BI solution, Loyalty Program Statistics, is aimed at studying customers, tracking conversions, and customer churn. The report also allows you to segment customers by the number of their in-store purchases.
This information allows you to manage your loyalty program and implement effective marketing activities.
A separate tab of the Customer Churn report contains a visualization and a table with a list of customers who last visited the store for a certain period.
The visualization shows how many customers made their last purchase each day during the selected period, and also shows the % of lost loyalty program customers from the total number of active customers on that day.
The BI report also collects data and shows the information on sales of customers who last visited the chain store for the selected period.
Another useful visualization of this report shows the sales funnel, i.e. the distribution of customers by the number of their purchases. The sales funnel shows how many customers made their first purchase, and how many of them came back and made repeat purchases.
In addition, you can see a list of new customers who made purchases during the selected period, as well as a list of categories and products that were received with receipts from new customers.
The report also contains all the important sales indicators related to new loyalty program customers. It includes sales data, the number of receipts, the average receipt price, and the average sales rate.
The BI report dashboard and visualizations allow you to easily view all the data and metrics on loyalty program customers, which you can easily share with your team.
In order to gain insight into customer behavior, you can learn more about why customers leave your store and what factors influence their decision.
For example, you can analyze the customer lifecycle, i.e., how long a customer made purchases in your store before they stopped buying. You can also analyze whether seasonality or the end of promotional campaigns could have affected customer churn.
The reasons for customer churn may be out-of-stock, high prices, difficulties in finding products, poor customer service, or long lines at the checkout. Either these were not your customers at all and the promotional campaigns were set up by mistake for the wrong segment of customers.
After all, there is a big difference in the reasons for churn: when a customer does not make a repeat purchase after the first purchase and another situation when a customer stops buying after 1 year of regular purchases in your store.
Therefore, you can track how many customers leave within the first three months, six months, or the first year. When you analyze churn in this way, you can determine where, on average, the biggest drop-off occurs along your customer’s journey.
You can also segment users into groups based on the metrics that are important to you. Then, study these segments to identify patterns in your churn data, so you can retain customers better.
By using data from historical sales and customer buying habits, you can identify patterns that lead to customer churn. This information can then be used for marketing activities and to target more valuable customers.
For example, if 60% of your customers continue to buy at your store every month, it means that the chain has a monthly churn rate of 40% and a retention rate of 60%.
If the customer churn rate decreases, it means that the customer retention rate increases, their lifetime value increases, and the chain gets more sales and profit. In addition, a low customer churn rate provides insight into the accuracy of marketing campaigns and improves customer experience and satisfaction.
This, however, leads to happier customers. Besides the financial benefits of keeping customers satisfied, there is tremendous value from customers who are excited about shopping. They will remain loyal customers, and the store will have trust and steady revenue.
Conclusion
Data analysis matters for any retailer. Understanding the reasons for customer churn is essential to evaluate the effectiveness of your marketing and overall customer satisfaction. Besides, it is cheaper to retain the customers you already have than to attract new ones. Analyze loyal customers with Datawiz BI and use the insights to prevent customer churn.
