The first step in collecting metrics about your users is all about acquisition, but we know building a customer relationship goes beyond this initial interaction. Establishing a recurring and profitable customer base needs to be carefully monitored, which is where customer retention analysis comes in.
When you’ve obtained this data and analyzed it, you’ll find it’s a powerful tool in the battle against churn. Customers can churn for a number of reasons, including frustration, failing to see value or not receiving the support they expect. Whatever the catalyst is, the outcome is the same - you’ve lost a valuable customer that it took resources to attract in the first place.
What does customer churn look like?
How customer churn manifests itself will depend on the type of business you’re operating. If you sell products or services, then churn is seen when customers stop buying from you. In a subscription model, it’s represented by users canceling their membership to your service.
No matter which industry you operate in, no organization can afford to let churn continue without tackling it. Despite this, it’s a trap many businesses fall into and positive acquisition numbers can often lead to processes further down the line being overlooked.
What is customer retention analysis?
A strong focus on attracting new users without infrastructure in place to encourage them to stay loyal can lead to a high churn rate. As a result, you may find their customer lifetime value is outstripped by their cost of acquisition, thus losing the business money. Customer retention analysis aims to create an understanding of how to prevent users from leaving. It does so through answering the following questions:
- What’s the average timeframe for each persona to stay with the company?
- How long is the intervening period before a new user returns to a product?
- Have changes to products resulted in more returning customers?
- Are there any changes that have led to a downturn in retention?
- Can you identify changes that could improve retention?
- What’s the company’s churn rate?
What are the most effective methods of customer retention analysis?
When it comes to customer retention analysis models, there are a number to choose from, but cohort analysis is popular as it focuses on identifying what makes the customers you’ve already retained stay with your business. It’s essentially based on a form of segmentation, grouping users together who have shown common characteristics over a given time period.
Even within cohort analysis, there’s more than one approach and you must decide which type of cohort model you wish to use - acquisition or behavioral cohorts. The former centers on when users first signed up for a product, while the latter focuses on their activity within an app during a specific period of time.
Cohort analysis can be used to spot the products with the most potential for sales growth, web pages that produce a high level of conversions and predict future adoption rates. Among the benefits of using cohort analysis is that it focuses on a few important retention metrics instead of trying to take a vast quantity into consideration. There include:
- Repeat rate: The share of customers who purchase from your company repeatedly
- Orders per customer: The numbers associated with users that bought again
- Time between orders: This metric can vary between hours or months, depending on the type of product
- Average order value: Identifying high value cohorts means they can be better targeted
This information can then be leveraged to devise methods of reducing churn going forward.
How to do customer retention analysis
Whether you conduct cohort analysis or another type of customer retention analysis, the key is to do it regularly. That way you can compare your data as user behavior changes and ascertain if your practices are working to reduce churn. Here are six techniques you may wish to employ for successful customer retention analysis:
1. Define and calculate your customer retention rate
While retention looks different across industries and organizations, it’s important to define its purpose within your own and set the parameters around measuring retention rate. Starting off by knowing how many customers are sticking around is vital to understanding progress.
2. Select retention KPIs
Choose the right customer retention KPIs to suit your needs, such as:
- Churn rate
- Lifetime value
- Engagement score
- Net promoter score
- Monthly recurring revenue churn rate
3. Set a monitoring time frame
Check your business calendar for a recurring event, such as a monthly meeting or a quarterly report, and use it to correspond with checking your metrics. That way you’ll be able to present your metrics, track progress and make any necessary adjustments.
4. Make adjustments
In any form of analysis, it’s the adjustments that are made as a result of understanding the data that are important. Even if you have a good customer retention rate, there’s always room for improvement, and as industries evolve, standing still isn’t an option.
5. Identify behaviors that suggest at-risk customers
Tracking customer behavior and identifying patterns is an effective way to determine which signs suggest future churn. Once you’ve identified these trends, you can implement an early warning system to take action with at-risk customers before they churn.
6. Ask for client feedback
The data you collect from clients in the form of feedback can offer insight overlooked elsewhere in the customer retention analysis process. As it’s widely accepted that disgruntled users are more likely to provide realistic feedback than happy ones, it’s worth tapping into this resource.
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