Why do customers churn? - Here’s cohort analysis for the rescue

Ever wondered what keeps your customers away from coming back to your site or store? You might be roping in new customers every day but what makes it hard for them to stick with your business. Few customers might bailout from the purchase on their first visit, few might bail out on the second visit, few after a number of visits, but have you ever tried to understand the factual reasons behind it?

Retaining an existing customer is one of the most important factors when it comes to a business. Customers don’t usually stick to a store because of one attribute or one behavior, customers are persuaded by a combination of features and services.

Customers churn mainly because of two reasons, either they are not satisfied with your product or could be interested in your product but hesitant to pay the shipping or handling fee. These are the instances where customers tend to find an alternative with better offers.

But before we go ahead let's see:

What is churn analysis & customer churn?

Customer churn is a term used to describe the number of customers who stop doing business with a company over a certain period of time. Churn analysis is analyzing customer data to identify patterns in customer behavior and predict which customers are at risk of churning.

Customer churn is a critical business metric because it directly impacts the bottom line. Losing customers means losing revenue, so it's essential for businesses to identify and mitigate churn as much as possible.

Churn analysis involves collecting and analyzing data from various sources, such as customer surveys, transaction histories, and social media. By using machine learning algorithms, businesses can identify patterns in customer behavior that indicate they are at risk of churning.

Once these customers are identified, businesses can take action to prevent them from churning. This may involve offering targeted promotions or discounts, improving customer service, or addressing any issues causing dissatisfaction.

And then:

How do you analyze customer churn data?

Analyzing customer churn data involves collecting and analyzing data from various sources to identify patterns in customer behavior and predict which customers are at risk of churning.

The first step in analyzing customer churn data is gathering as much as possible. This may include customer surveys, transaction histories, and social media data. By collecting this data, businesses can better understand their customer's behavior and preferences.

Next, businesses can use machine learning algorithms to analyze the data and identify patterns in customer behavior that indicate they are at risk of churning. This may include analyzing customer demographics, purchase history, and customer service interactions.

Once these at-risk customers are identified, businesses can take action to prevent them from churning. This may involve offering targeted promotions or discounts, improving customer service, or addressing any issues causing dissatisfaction.

It's important to note that analyzing customer churn data is an ongoing process. By regularly collecting and analyzing data, businesses can stay ahead of potential churn risks and take action to prevent customers from leaving.

How do you find the customer churn rate?

For instance, say Mike is a regular customer to your store, he’s been dropping by the store for years. He visits your store twice a month and keeps purchasing products every time he visits. Now Mike is a valuable customer to your business, a returning customer.

On the other hand, there are other returning customers too, they could be purchasing products from your store but not necessarily at Mike’s rate. However, the returning customers are grouped based on the frequency of their visits and purchases. This analysis is dynamic and it works only when it is subjected to a timeframe.

A cohort is nothing but a of users grouped by shared characteristics. In business analytics, a cohort usually refers to a group of customers specifically segmented by acquisition date.

This pie chart shows the percentage of returning customers based on frequency over a period of 6 months, on feeding the data, cohort allows you to view the analysis based on various output formats.

Cohort analysis on the number of returning customers over a period of 12 months

Here’s another record where we have the number of customers who returned to a site/store over a time period of 12 months. The number of returning customers over a period of 12 months is constantly dropping, however, this doesn’t mean the business is on a low too.

New customers keep coming in but it’s a known fact that bringing in new customers is an expensive process than retaining existing customers because it involves a lot of marketing activities to start with. Whereas, the customers who already know your business can be roped back in with few offers or some deducted shipping prices.

This is where cohort analysis comes into play, it shows where customers lose interest and where they need a bit of guidance to get done with the process of purchase.

Cohort analysis is made to identify the places where customers bail out from the purchase, where they lose interest. A cohort analysis also goes beyond basic data points to suggest the reason for changes in your site visitor’s behaviour like how long they stay in the site, how long they take to find what they came for, etc.

With Cohort analysis, not just the generic single-dimensional analysis but also multidimensional analysis can be conducted. In order to find the traffic for a website not just on a single dimension (Organic or Paid) but also on the device through which the traffic comes in (Mobile or Desktop or Tablet)

Cohort analysis might not be precise but it sheds light on the right path, that allows you to correlate your business’s data into actionable insights.

As each cohort is built with different marketing strategies, it paves way to identify which marketing approach works best. There are instances where customers are forced to sign up or register their emails in order to browse a product.

Overselling or harassing the customer into buying the product ultimately ends up in customer churn. So instead of analyzing your cohorts based on whether they consistently return to your site, you can focus on the actions that improve marketing as a whole and also keeps the existing customers satisfied.

FAQs

Q: How can businesses prevent customer churn?

A: Businesses can prevent customer churn by identifying at-risk customers and taking action to prevent them from leaving. This may involve offering targeted promotions or discounts, improving customer service, or addressing any issues causing dissatisfaction.

Q: What data is needed for churn analysis?

A: To perform churn analysis, businesses need to collect data from various sources, such as customer surveys, transaction histories, and social media data. By analyzing this data, businesses can identify patterns in customer behavior and predict which customers are at risk of churning.

Q: Is churn analysis a one-time process?

A: No, churn analysis is an ongoing process. By regularly collecting and analyzing data, businesses can stay ahead of potential churn risks and take action to prevent customers from leaving.