In a market where consumer options are at an all-time high, attention spans are at an all-time low, and customers demand individual attention when it comes to their brand experiences…retention is king.
Marketers have long agreed that loyalty efforts are a much better investment than acquisition, as research shows the cost of acquiring a new customer is nearly five times as much as retaining one and a mere 5% increase in retention efforts can turn more than 25% in profit.
There are a variety of KPIs you can use to measure the success of your customer retention efforts, but potentially none better than customer lifetime value (CLV).
According to a 2019 Econsultancy report, 76% of respondents claimed that driving customer lifetime value was a priority for their organization, but only 33% reported that it was a measured KPI. This shows that marketers understand the benefits of CLV, but many are still trying to understand the basics and work through how to measure it.
What is customer lifetime value (CLV)?
Customer lifetime value is a calculation that determines the total revenue you can expect from an average customer over their lifetime with your business.
It takes into account both the revenue that your customers have generated to date and the revenue they are likely to generate in the future, thus helping you determine a reasonable cost per acquisition.
For example, if the CLV of your average retail shopper is $500, but you’re spending more than that on advertising and marketing efforts to acquire a new customer, your efforts are neither profitable nor sustainable. This is a clear indicator that you should pare back your acquisition costs until you find new ways to improve your CLV.
Ultimately, this number will tell you a rough estimate of how much profit you can expect to receive from a customer relationship — which you can use to then gauge how much is worth investing to maintain the relationship.
Why is CLV important to know?
Your CLV is not an extremely important number in and of itself; it’s not something you should necessarily compare with other businesses. Rather, it can help you estimate how much profit you can expect to receive from a customer relationship so you can more easily gauge how much is worth investing to maintain the relationship. CLV becomes even more useful when you break it down to identify the customer segments that return the most value to your company and invest your time in those that are most likely to invest back.
As an example, in calculating your CLV for Product A vs. Product B, you may discover that consumers who first purchase Product A are much more likely to become repeat customers, and thus have a higher CLV, than those who first purchase Product B. This could indicate that it’s much more profitable for you to focus your acquisition efforts on Product A.
You may also discover that customers who visit your retail store have a higher CLV than those who only shop online, which means you could be doing more to incentivize in-store visits from your online customers.
Whether you choose to look at men vs. women, teens vs. adults, or coffee vs. tea drinkers (whatever makes the most sense for your business, of course) — these could all uncover helpful insights that can improve your decision-making. That way, you can begin focusing your efforts on the most profitable customers, products, and channels or encouraging specific types of purchases or behaviors
How to measure CLV
Calculate CLV by taking the average value of a purchase, multiplied by the number of times the customer will buy each year, multiplied by the average length of the customer relationship
- CLV = (Avg. value of purchase) • (Avg. purchase frequency rate) • (Avg. years of a customer relationship)
Average purchase value can be determined by your total revenue in a time period, divided by the number of purchases over the same time period. So if you make $10M per year over 100K purchases, your average purchase value is $100.
Average purchase frequency rate is calculated by taking the total number of purchases across a time period, divided by the number of unique customers that purchased over that time period. Using our previous example, if you have 100K purchases in a year and 50K of them are unique purchases, your average purchase frequency rate is 2.
If you multiply your average purchase number with your average purchase frequency rate, you can determine your average customer value, which in our example would be $200. But we want to know the average value of a customer over their entire lifespan of a customer, not just in a year, so we need to factor in the average length of a customer relationship.
Average customer lifespan is found by looking at all your customers and calculating the average number of years your customers continue to purchase from your company.
If we assume, for the purposes of our example, that our company’s average customer lifespan is 5 years, our CLV calculation will be: $100 x 2 x 5 = $1,000
Difficulties of determining CLV
Calculating CLV itself is not an extremely difficult task, but it does require accurate data. A single customer view will be your most helpful tool in working through these calculations — or potentially your largest barrier.
The 2019 Econsultancy report on customer lifetime value notes that data limitations is one of the top three challenges for 38% of organizations — both in general and in measuring or increasing CLV. 70% say that their data is not sufficiently integrated to be able to accurately measure and focus on CLV as a KPI.
But if customer lifetime value is as important as we believe it is, companies would do well to invest in their data. Ultimately, CLV is much more than its calculation — it’s about understanding the customer experience and what key drivers can impact your company’s profitability. Without accurate and easy-to-access data, this task is much more difficult. A data solution that can help you better understand your customers at every touchpoint across their entire lifecycle will be imperative to understanding what is driving your CLV and how you can improve it.
Take control of your data to build a more accurate CLV.