Customer retention defines a company’s ability to retain customers over a given period of time and is measured through the customer retention rate. In the big picture, this metric shows how well a company understands and serves its clients.
How do you calculate the customer retention rate?
The customer retention rate is calculated as the percentage of customers that stick around at the end of a given time period, relative to the number of customers you had at the beginning of the same time interval.
The formula is as follows:
CRR = (E – N) / S x 100
- E = the number of customers at the end of the defined period
- N = the number of customers acquired during the defined period
- S = the number of customers at the start of the defined period
To calculate your customer retention rate you first need to choose a time period that you consider relevant enough for evaluating your business performance. It can be anything from one week or one month to a quarter or even a full year.
Here’s a practical example: if you started this year with 1200 customers, and you end it with 1500, yet you acquired 900 new customers, your CRR is:
(1500 – 900) / 1200 x 100 = 50% retention rate
NOTE: We’ve built REVEAL, our Customer Lifetime Value Optimization tool, to automate processes such as RFM segmentation or retention rate analysis. If you own a Shopify store, you can try it out here.
Why is customer retention important?
Retaining a customer is less costly and more profitable in the long run than acquiring a new one. A 5% increase in customer retention can lead to a 25-95% increase in profit, and the cost of retaining a client is 5-25x lower than the cost of acquiring a new customer.
The CRR is a direct predictor of a business’ growth over time.
Companies investing in retention programs for 1-3 years are 200% more likely to increase their market share compared to those spending more on acquisition.
At the same time, businesses in which retention is a strategic pillar are 36% more likely to see no increases in customer churn year-over-year, and twice more likely to understand the impact of customer lifetime value on revenue and growth.
This number is in line with our findings from the Magento Live Europe 2018 event, where only 28% of the respondents – 52 e-commerce companies – had a dedicated person working on customer retention.
Additional benefits of having a customer retention strategy
In small and mid-sized companies, customer acquisition programs are often seen as more important than retention strategies. However, the benefits of a retention-focused approach go beyond the mentioned financial aspects.
88% of companies that focus on retention also achieve their acquisition goals. On top of this, customers who do repeat business with a company are likely to become referrals and to recommend it to others.
Also, the costs of marketing to existing customers is lower than the cost of pushing a product or service to new customers.
Repeat customers perform better in terms of average order value (AOV) and customer lifetime value (CLV). The probability of selling to an existing customer is 60-70%, while for a new customer the number goes down to 5-20%.
It’s therefore quite obvious that from a business point of view, retaining a client is more profitable in the long run.
How does customer retention relate to CLV?
The CLV or customer lifetime value is a prediction of the net profit attributed to the entire future relationship with a client.
It can be measured for all your customer base or within a segment, and it can be calculated using different formulas.
For example, the predictive CLV formula is built based on predictive analysis and takes into account the previous transactions made by a customer as well as the average customer lifespan (in months). The formula is:
CLV = ((Avg. monthly transactions * Avg. order value) * Avg. gross margin) * Avg. customer lifespan
It is therefore obvious that retaining a customer for a longer period and receiving repeat orders from the same client contributes to a higher CLV.
But let’s look at a practical case, to understand this relationship better.
An industrial webshop has A-rated customers who place on average 1 order per month, with an AOV of 250 euro. The average gross margin is 28%, and the average customer lifespan is 36 months. The CLV in this case is:
CLV(A) = 1 * 250 * 28/100 * 36 = 2520 euro
The same webshop has B-rated customers who place on average 3 orders per month, with an AOV of 80 euro. These customers stick around for 48 months on average. The CLV in this case is:
CLV(B) = 3 * 80 * 28/100 * 48 = 3225,6 euro
Now, if the average order value and the yearly income are calculated for 1 year only, it may seem like A-rated customers are more valuable, because they generate 250 euro every month.
However, we see that B-rated customers are actually more valuable because they remain customers for an additional 1 year on average.
Is there any correlation between CRR and NPS?
NPS or the Net Promoter Score reflects the likelihood of a customer to recommend your company to someone else after doing business with you.
The client is asked to rate his willingness on a scale from 0 to 10, and the answers are interpreted as follows:
- A score between 0 and 6 places the responder in the Detractors group
- Then, a score of 7 or 8 places him/her in the Passives group
- A score of 9 or 10 places the responder in the Promoters group
The final value of the NPS can be anything between -100 and +100. A high score means that your customers are generally satisfied and very satisfied with the service received.
Although the NPS measures an intention, it seems to be an accurate indicator of the actual behavior of a client.
Moreover, customers who fall in the Promoters segment are more likely to generate word-of-mouth sales, and more likely to place repeat orders.
Running customer surveys to understand customer happiness, repurchase intentions and customer expectations helps to build strong loyal customers and obtain a greater customer value that increases profits.
Still, if you’re only focusing on this metric and neglecting the customer retention rate, you’re focusing on a potential future action of a client instead of looking at his past and current behavior. So the two metrics shouldn’t be taken out of context and interpreted separately.
The role of segmentation in a customer retention strategy
Along with the CRR and the CLV, the CAC – customer acquisition cost and RFM segmentation are the most important metrics to look at when evaluating the efficiency of your customer retention strategy.
- R for Recency
- F for Frequency
- M for Monetary value
This model helps you segment your customer base by evaluating their purchase behavior. By looking at how recently, how often and how much a customer spent with you, you can identify your most profitable segments and focus your retention strategy around them.
To make sense of the RFM model, you need to choose a time interval for your analysis – let’s say the past 3 years. Then, for each metric, you need to define a scale for rating your customers’ behaviors.
For example, you can choose to assign values from 1 to 5, where 1 defines the least performing behavior, and 5 defines the most desired behavior.
A practical example of RFM segmentation
Let’s look at a practical example of rating the Recency. If you take a time interval of 3 years, you can rate the customer behavior as follows:
- 30 days since the last order placed = Recency score 5
- 90 days since the last order placed = Recency score 4
- 180 days since the last order placed = Recency score 3
- 270 days since last order placed = Recency score 2
- More than 270 days since last order placed = Recency score 1
You can use the same logic for F and M.
Another approach for Recency is to first sort your database based on the recency of the last order, and rank it as shown below. The last step is to assign a recency score by clustering the rankings.
Ideally, you should aim to group a similar number of rankings in each cluster. For the example below, you could make 4 clusters, each containing 2 rankings.
With this approach, you will end up with 4 recency scores: 1 4 for the top-performing cluster, and 1 for the least performing one.
If you want to learn more about this technique, head over to our blog: we’ve detailed the step-by-step process of building a model for RFM segmentation in a previous article.
Customer loyalty programs create more engaged customers and helps to build relationships that are positive and get better results in your retention program and in the end increase revenues.
A data-driven framework for improving customer retention
With so many different metrics involved, creating a customer retention strategy is an overwhelming task even for an experienced marketer.
Yet, it is the right thing to do when you reach a point where customer acquisition stagnates, revenue and profit don’t grow as fast as expected, and you’re starting to lose clients.
How do we approach this challenge to make sure our clients are seeing the desired growth in their key metrics?
You can learn more about our approach by reading the Otter Shoes success story on our blog.