AI-Based Customer Retention Strategies

Over the last several years, there has been an increase in the cost of acquiring new customers. The cost of ads on Facebook, Google, and other well-known ad platforms has increased by around 25% since 2019. As a result, many brands are using customer retention strategies to reach revenue goals and grow their businesses.

More recently, however, customer retention has proven to be complicated. Studies from Kroger’s 84.51 and TechSee report that customer loyalty is at an all-time low. Brands need to find ways to reach new customers without spending too much money — and keep the customers they have.

Clearly, customer retention is essential for brands. AI and predictive analytics can help by predicting customers’ behavior and needs.

What is customer retention?

Customer retention is the rate at which a customer stays with your business during a specific period. Customer retention is typically measured by customer churn rate. The fewer customers who leave, the lower the customer churn rate and the higher your retention rate. In other words, you’ll have more loyal customers.

Why is customer retention necessary?

The goal of increasing customer retention is to build a loyal customer base. Brands with a loyal customer base are more likely to outperform those without. On average, the sell-through rate to an existing customer is 60-70%, which is considerably higher than a prospect.

In addition, when customers feel positive about your business, they will likely recommend you to about 11 people. Word of mouth (in person or through social media) is a powerful selling tool.

What are the critical customer retention metrics?

There are a few metrics to consider when measuring customer retention, with the primary metric being the customer retention rate. This metric is the rate at which you retain customers over a stated period.

The customer retention rate (CRR) is typically calculated by taking the customers you have at the end of a period and subtracting the customers you acquired during that period. Then, divide that number by the number of customers from the start of that period.

customer retention rate
Customer Retention Rate (CRR) formula

Here is an example of this calculation in practice. Say you have 1,000 customers at the start of the month. You lost 10 customers but gained 30 from customer acquisition initiatives during the month. As a result, you have 1,020 customers at the end of the month.

Based on the above calculation, the CRR for your business is: 99%

CRR = (1020 – 30)/1000 = .99 X 100 = 99%

Some other key metrics to consider when looking at customer retention include the following:

  1. Repeat customer rate measures the percentage of customers making a repeat purchase. To calculate the repeat customer rate, take the number of customers who purchased more than once and divide it by the number of unique customers.
  2. Purchase velocity measures how often customer purchase from your company. To calculate purchase velocity, take the number of orders and divide it by the number of unique customers.
  3. Average order value (AOV) is the total revenue divided by the total number of orders from a given period.
  4. Customer lifetime value (LTV) is a combination of purchase velocity multiplied by the average order value. LTV calculations help you understand the value of each of your customers.
  5. Customer churn rate measures the number of customers who have stopped purchasing your product or service in a given period. Understanding why customers churn is critical for retention and other critical KPIs like CSAT and Net Promoter Score. In addition, identifying the reasons for churn can help support product and customer experience improvements.

How is AI helping brands retain customers?

AI and machine learning are helping to make customer retention more manageable and less costly than in the past. Emerging predictive analytics platforms allow businesses to create highly accurate models that can help them win and retain customers. Some of the top models brands use to inform strategies to reduce customer churn are:

  1. Customer lifetime value: It is now possible to predict customer lifetime value at the start of any new customer relationship. Predictive LTV software allows you to deepen customer relationships for exclusive offers and special incentives.
  2. Predictive analytics can help you identify when customers will likely upgrade their service with your business. This prediction allows you to focus your efforts on selling to those who are most likely to be interested. This focus increases sales efficiency.
  3. Predictive churn solution: Predicting customer churn is a great way to maintain high retention rates. Predictive churn software can help detect 85% of customer churn and improve retention rates by as much as 35%.
  4. Customer winback: Winning back past customers is a great way to improve your MRR. Predictive analytics improves winback campaigns by as much as 260%.


Looking to learn more about how predictive analytics can help you retain customers? Feel free to contact us or schedule a demo.

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