5 Key Customer Retention Metrics Every Ecommerce Marketer Should Know

5 Key Customer Retention Metrics Every Ecommerce Marketer Should Know

Customer retention is not always an easy process. Okay, that's a bit of understatement. But you don't need to be a rocket scientist (or a data scientist, rather) to retain your customers. What you do need is the ability to develop and implement a plan.


Customer retention rate, customer churn rate, net promoter score, average revenue per customer and customer lifetime value are five key customer retention metrics every marketer should now. Let's examine the definitions for these retention metrics, how they are calculated, and how AI and machine learning can help with retaining customers.

Metric #1: Customer Retention Rate (CRR)

Simply defined, a customer retention rate is the percentage of customers a business has kept over a certain period. This rate is an indication of how satisfied a customer is over a given time.


A customer retention rate is calculated as the following formula:


Customer retention rate = (E-N/S x 100)


E is the number of customers at the end; N represent the number of new customers; S is the number of customer at the start of that period being measured.


Consider using this metric as a benchmark for what your business should aim for in terms of customer retention. The most ideal CRR is 100 percent but that's not realistic because that means none of your customer would leave you. While one should aim for a CRR rate of 90, according to Salesforce, this is often difficult. Therefore, according to the same source, a CRR of 85 is acceptable dependent on the industry, product type, and service.


Download Now: How to Target Your Best Customers and Keep Them

Metric #2: Customer Churn Rate (CCR)

Customer churn rate, which is the flip-side of customer retention rate, is defined as the rate at which your customers stop buying from you. As you know, if they stop buying that's potentially bad news for your cash flow.


A customer churn rate is calculated with the following formula:


Annual churn rate = Number of customers at the start of the year / Number of customers you lost during the year


This metric is important because it measures the rate of customers not buying from you. Ideally, and in an almost perfect world, a business wants a CCR of zero percent, but that's highly unlikely. An acceptable CCR in today's marketplace is 5 percent-ish, according to a HubSpot article.

Metric #3: Net Promoter Score (NPS)

Net promoter score is a measurement to gauge customer loyalty with a business based on the NPS survey, which asks questions such as: "How likely you would recommend our company/product/survey to colleague?" The answers typically are scored on a 1 to 10 numeric scale.


Since this metric is predicated by answers to the NPS survey, calculating the score is different from the other formula-based metrics covered. According to Satmetrix, respondents are categorized based on the following buckets:


  • Promoters (NPS score 9-10) — These are loyal brand/company customers who will keep buying and refer other customers.
  • Passives (NPS score 7-8) — These customers are satisfied, but still vulnerable to competitors.
  • Detractors (NPS score 0-6) — Detractors are unhappy customers who can damage your brand with negative reviews.

Metric #4 Average Revenue Per Customer (ARPC)

Average revenue per customer is the rate of revenue, on average, gained from each one of your customers.


This metric can show you the current worth of your customers and used to predict growth, according to Recurly, whenever there is a revenue increase. However, according to the same source, these spikes in revenue can be caused by few but very large customers.


Average revenue per customer is calculated with the following formula:


Average revenue per customer = Total revenue for the month/ Subscribers contributing to that revenue


Metric #5: Lifetime Value (LTV) & Customer Lifetime Value (CLV)

Lifetime value

Lifetime value is a metric that estimates the profit made from your average customer over the period that they remain one of your customers, according to Recurly.


Here's how it's calculated:


Lifetime value = Average revenue of one of your customers x Gross margin percentage/ Customer churn rate

Customer lifetime value

Customer lifetime value (CLV) is the projection of the entire revenue a customer brings during the complete “lifetime" time of engagement with your business and brand.


Customer lifetime value is calculated by this formula:


Customer lifetime value = Margin x CRR / (1 + Discount Rate - CRR)

To break it down more: Customer value equals average order value X purchase frequency. Remember, X is "your" variable, but you probably get that.


The higher a CLV, according to Mailchimp, the more satisfied your customers are. Conversely, if the CLV score is low, you will know that you must take action to increase customer purchasing.

Using AI To Measure Retention Metrics

Today, artificial intelligence (AI) is a mainstream business tool with practical benefits for marketing organizations. In fact, these days, AI needs to be a part of every marketer's core toolkit.


Customer data is at the heart of all five of the key retention metrics, and AI can help transform that data into strategic and actionable insights.


Key benefits of using AI to improve retention metrics include:


  • Predicting customer churn: AI can help analyze massive amounts of data in real-time to predict which customers are at risk of leaving.
  • Automating processes and calculations: AI can help automate data processing, speed analysis, and improve the efficiency of calculating retention metrics.
  • Improving accuracy: AI can help improve the accuracy of retention metrics, and reduce the potential of human errors from manual calculations.

Conclusion

Marketers today face the challenges of changing customer behaviors and emerging technologies. But understanding how to use customer retention metrics will help you better understand and engage with your customers.


Data analytics and tools, such as KI can help you glean better insights from your data. Armed with these insights, you'll be better prepared to monitor the pulse of your current customers, and you'll have the insights you need to more effectively keep those customers from churning into former customers.



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