Read a bit about customer lifetime value, and it might sound too complicated for those of us in the scrappy ecommerce merchant demographic.
But there's no reason to be intimidated by customer lifetime value, or CLV. Is it complex? Sure. Was it created with small online stores in mind? Most likely not. But customer lifetime value can help you make informed decisions about your profit margins, as well as how to allocate your energy between retaining existing customers and finding new ones.
Understanding CLV matters because it reveals the true worth of keeping buyers around. When you know what shoppers bring to your business over time, you can decide smarter ways to spend on getting them and keeping them happy. This metric shows you the total earnings you'll collect throughout your connection with each buyer.
This post will explain, in the simplest terms possible, how to calculate CLV and why it matters. Then we'll look at what you can do to move your store's average customer lifetime value in the right direction.
What Is Customer Lifetime Value?
There are different formulas you can use to calculate customer lifetime value. There are even different ways to calculate the variables that make up those formulas. Today we'll keep things basic, looking at simple formulas for historic and predictive customer lifetime value.
Here's how historic customer lifetime value looks:
(Annual profit per customer x Average customer lifespan in years) - Cost of acquiring customer
As an example, say that you have:
- $750,000 in annual revenue
- $600,000 in annual non-acquisition costs
- 1,500 customers
- An average of three years per customer
- An average cost of $50 per acquisition
With that as our backdrop, we can start filling in the blanks.
Annual profit per customer = ($750,000 - $600,000) / 1,500 = $100
Average customer lifespan = 3
Initial cost of acquiring customer = $50
($100 x 3) - $50 = $250
This formula is retroactive and it doesn't account for so many of the variables that might help inform future marketing decisions. But there are ways we can play with historic customer lifetime value to get more out of it.
Why Calculating Customer Lifetime Value Matters for Your Business
Before diving deeper into calculations, it helps to understand why CLV deserves your attention. This metric offers three major advantages:
- First, it helps you encourage repeat purchases and boost revenue. By identifying which buyers contribute the most to your bottom line, you can understand their preferences and create strategies to keep them coming back.
- Second, CLV helps you manage the balance between what you earn from customers and what you spend getting them. Bringing in new buyers can cost between $127 and $462, depending on your industry. A healthy ratio sits around 3:1, meaning you earn three dollars for every dollar spent on acquisition.
- Third, the strategies you use to improve CLV naturally enhance customer loyalty. When you focus on delivering better experiences, improving products, and rewarding repeat buyers, people stick around longer and spend more.
Predictive Customer Lifetime Value
Predictive customer lifetime value is where things get complicated. For this, we account for both retention rate and discount rate. But we can only do that after we've come up with a number for "gross margin per customer," which itself is only available after we determine our average gross margin.
First, let's find that gross margin per customer, which is foundational to the predictive customer lifetime value formula. Gross margin per customer looks like this:
GML = ((T x AOV) AGM) ALT
Okay, that's a lot of letters. Let's take a step back and look at it piece by piece.
- T = Average number of transactions (per month)
- AOV = Average order value
- AGM = Average gross margin
- ALT = Average customer lifespan
T and AOV are pretty clear. AGM and ALT could use a bit of unpacking.
AGM, or average gross margin, is your total sales revenue minus the costs of goods sold, then divided by total sales revenue. Here's an example:
($75,000 total sales revenue - $60,000 cost of goods sold) / $75,000 = 20%
If this example store with $75,000 in monthly revenue has an average of 5,000 monthly orders and an average order value of $15, then the equation would look like this:
((5,000 x $15) x 0.2) ALT =
($75,000 x 0.2) ALT =
$15,000 x ALT
We need to make this customer lifetime value calculation per customer. So, we would divide $15,000 x ALT by the number of customers you have. Let's say we have 2,500 customers making those 5,000 purchases, and these customers have an average lifespan of four years, or 48 months.
($15,000 x 48) / 2,500 = $288
Now let’s cover average customer lifespan, or ALT. This one is especially tough for ecommerce stores.
For starters, your store might not be old enough to really calculate an average customer lifespan. Even if you have, say, four years under your belt, how much do you trust that first-year data? Chances are that your customers in Month 1 had a different experience than a customer who converted for the first time last week. And who knows, maybe your products are completely different than they were at the beginning.
The bottom line is that ALT is tricky to calculate. Just remember that changing this variable can have a huge impact on the lifetime value of your customers. A good customer for three years is more valuable than a good customer for one year.
The next step to predictive future customer lifetime value, if you want to take it, requires dropping this gross margin per customer (GML) into a formula that accounts for retention rate and discount rate.
You can see how deep this customer lifetime value rabbit hole goes. Retention rate all by itself is an absolute monster, as explained here. Add the discount rate to that, and we're looking at loads of work to find customer lifetime value.
At any rate, if you want to go further down the rabbit hole, here's the path:
Customer lifetime value = GML (R / (1 + D - R))
R is monthly retention rate, and D is monthly discount rate. Both of these numbers are tricky to calculate. For example, do all of your customers receive the same discounts? If not, do the ones who receive more discounts have a higher retention rate?
The traditional approach relies on past performance to estimate future spending. But there's a challenge: CLV constantly shifts. Marketing campaigns might influence how much certain buyers spend, while supply problems could discourage others from purchasing.
A forward-looking method helps you better estimate future spending patterns. The formula stays the same, but the numbers you plug in consider real-time information and trends rather than just historical records. This requires current data collection and analytical tools to make accurate projections about market conditions, changing buyer habits, and shifting costs.
What You Need Before Calculating CLV
Before you can work out customer lifetime value, you'll need to gather several pieces of information. These metrics form the building blocks of your CLV calculation.
- Average Order Value represents how much buyers typically spend each time they place an order. Find this by dividing your total revenue by your total order count.
- Purchase Frequency shows how many orders each buyer places on average. Take your total orders and divide by your total unique buyers during the same timeframe.
- Customer Value combines these two metrics. Multiply your average order value by purchase frequency to get this number.
- Average Customer Lifespan measures how long relationships with buyers typically last before they stop purchasing. This varies between different business types.
Most online stores are non-contractual, meaning each transaction stands alone. The tricky part is figuring out when an active buyer becomes inactive forever.
Some businesses, like subscription services, have contracts that make this easier. You know exactly when someone becomes inactive because they cancel their subscription.
If your store is new, you might lack enough information to determine lifespan accurately. Here's a workaround: Calculate how long each buyer's relationship lasts from first to final purchase, then divide by your total buyer count.
Customer Lifetime Value: Playing With the Variables
Alright, so we know the elements that make up customer lifetime value. Or at least the elements that make up certain versions of customer lifetime value. Now let's look at how you can play with the variables and how that would impact your store.
First let's revisit historic customer lifetime value:
(Annual profit per customer x Average customer lifespan in years) - Cost of acquiring customer
There are ways to dig deeper into this formula. We could, for instance, look at the "initial cost of acquiring a customer" variable per channel. That would tell us the customer lifetime value of customers acquired via Facebook, via SEO, and so on.
After all, not all channels are created equal. Maybe your blog drives tons of SEO traffic to your site on the cheap, while on Facebook your cost-per-click continues to climb. Then again, maybe your Facebook customers spend more money once they convert.
With customer lifetime value, we can determine if the accelerated acquisition costs on a certain channel are worth it.
Here's how a historic per-channel calculation might look:
- 1,500 customers: 750 from Facebook, 750 from search
- $750,000 in annual revenue: $500,000 from visitors acquired via Facebook, $250,000 via search
- $600,000 in annual non-acquisition costs: $400,000 for Facebook, $200,000 for search
- Average of three years per customer
- Average of $50 acquisition cost: $90 Facebook, $10 search
Facebook:
Annual profit contribution = ($500,000 - $400,000) / 750 = $133
Average number of years they are a customer = 3
Initial cost of acquiring customer = $90
($133 x 3) - $90 = $309
SEO:
Annual profit contribution = ($250,000 - $200,000) / 750 = $67
Average number of years they are a customer = 3
Initial cost of acquiring customer = $10
($67 x 3) - $10 = $191
As we see here, even if an acquisition channel is more expensive, it might be totally worth it if customers acquired from that channel spend more money. Obviously there is no rule that says customers acquired via Facebook will spend more than customers acquired via search. But we can see how a per-channel look at customer lifetime value can impact your marketing spend.
Here's how customer lifetime values would look if customers acquired via search spent the same as those acquired via Facebook.
Facebook:
Annual profit contribution = ($375,000 - $300,000) / 750 = $100
Average number of years they are a customer = 3
Initial cost of acquiring customer = $90
($100 x 3) - $90 = $210
SEO:
Annual profit contribution = ($375,000 - $300,000) / 750 = $100
Average number of years they are a customer = 3
Initial cost of acquiring customer = $10
($100 x 3) - $10 = $290
All of a sudden, search drives a bigger customer lifetime value.
Let's play with customer lifespan. What if your Facebook campaigns led to more one-time, impulse purchases, while your SEO-driven purchases resulted in loyal, longer-lasting customers? Let's say Facebook customers last one year, and SEO customers last five years. The annual spending is the same, but not the length that they are your customer. What does that do to the customer lifetime value of people from these channels?
Facebook:
Annual profit contribution = ($375,000 - $300,000) / 750 = $100
Average number of years they are a customer = 1
Initial cost of acquiring customer = $90
($100 x 1) - $90 = $10
SEO:
Annual profit contribution = ($375,000 - $300,000) / 750 = $100
Average number of years they are a customer = 5
Initial cost of acquiring customer = $10
($100 x 5) 0 $10 = $490
SEO crushes Facebook. Of course a one year/five split might be unrealistic. But this goes to show how turning the dial on one of these variables totally changes the expected customer lifetime value.
We could also apply a country segment, or identify cohorts whose first purchase was three years ago, two years ago and this year. There are all sorts of ways to adapt this equation to get richer historic insights.
The same math games can be used for the predictive customer lifetime value formula. One unique element of the predictive formula we looked at is average gross margin. Let's take a look at how changes to average gross margin might impact the overall customer lifetime value.
We could increase our average gross margin by finding a cheaper supplier, or by increasing prices. Let's try raising prices. That would make our total sales revenue higher.
Here's the original:
($75,000 total sales revenue - $60,000 cost of goods sold) / $75,000 = 20%
Here's with 20 percent higher prices:
($90,000 total sales revenue - $60,000 cost of goods sold) / $90,000 = 33%
Now our store has $90,000 in monthly revenue on its 5,000 monthly orders, good for $18 per order.
((5,000 x $18) * .33) avg. customer lifespan
($90,000 * .33) ALT
$30,000 x ALT
Of course turning the dial up on one thing, like profit, might turn the dial down on another, like average customer lifespan. If you are charging more money for the same products, then your customers might not stick around as long.
There are tons of tradeoffs like this. For example, you could get more aggressive with social media advertising to drive up the average number of monthly orders, but doing so might drive down our friend AOV, average order value. It might also be possible to increase the average customer lifespan by offering discounts to returning customers. That, though, could take a bite out of sales average margins.
Calculating CLV for Individual Customers
Sometimes you need to know the lifetime value of specific buyers rather than just averages. This comes in handy when handling complaints or deciding how to respond to refund requests.
For instance, someone with low CLV might be asked to return an item for a refund, while high-value buyers might receive a refund without requiring a return to maintain their loyalty.
To calculate individual CLV, multiply how much that person spends annually by your average lifespan figure. If someone has spent $500 over two years, their yearly average is $250. Multiply this by a five-year lifespan estimate, and their projected lifetime value becomes $1,250.
Using RFM Analysis to Calculate CLV by Segment
RFM analysis helps you organize buyers from least to most valuable based on three factors: recency, frequency, and monetary value. By grouping buyers this way, you can analyze each segment separately and identify which groups have the highest CLV.
Here's what each factor means:
- Recency tracks when someone last made a purchase. Recent buyers are more likely to purchase again than those who haven't bought in a while.
- Frequency counts how many purchases someone makes within a timeframe. Frequent buyers are more likely to continue than rare purchasers.
- Monetary value shows how much someone has spent during that period. Big spenders are more likely to return than those who spend less.
To run an RFM analysis, assign each buyer a score from 1 to 3 for each factor. Think of these as categories: 1 represents the least valuable third, 2 represents the middle third, and 3 represents the most valuable third.
Add up the three scores for each buyer to get their total RFM score. Sort your list by these totals and divide results into high, medium, and low groups. Your highest-scoring segment represents your most valuable buyers. Look for common patterns among these top buyers to understand why they provide more value and how to target similar people.
Putting Your CLV Calculations to Work
Understanding CLV helps you build smarter, more efficient marketing campaigns. Here's how to use these calculations:
Maximize Return on Investment: When you know the total lifetime value of typical buyers, you can identify which segments are most profitable. Focus your marketing efforts on reaching similar people to improve your returns.
Set Budgets for Paid Advertising: CLV helps you determine how much to spend on paid campaigns across platforms like Google, Instagram, or TikTok. You can calculate your maximum bid based on CLV and conversion rates. For example, with a $100 CLV and 10% conversion rate, you could bid up to $10 per click without exceeding budget.
Optimize Pricing and Offers: CLV calculations inform pricing decisions and help maximize profitability. When you know how much buyers typically spend during their relationship with your business, you can determine a product's perceived worth and create promotions that encourage repeat purchases.
One subscription box company increased CLV by 40% in a year by restructuring their pricing tiers. After noticing a major drop-off after three months, they reworked pricing to reward longer commitments. One-month subscriptions cost more per box, while annual subscriptions offered significant discounts plus extra perks like early access to limited products. This simple change boosted average subscription length from five to eight months, adding nearly $150 in CLV per buyer while reducing churn by 18%.
Identify Upselling Opportunities: Customers with elevated CLV demonstrate stronger commitment, greater brand affinity, and show increased receptiveness when presented with additional purchase opportunities. You can increase your CLV figure even further by focusing on these valuable buyers. Use purchase history, preferences, and behavior data to create offers that are relevant and appealing.
As one data strategist explains, you need to be mindful about what leads to better buyersâthose who stick around longer and spend moreâand put more emphasis on reaching those people while spending less on those who won't return.
Conclusions on Customer Lifetime Value
You don't need perfectly accurate numbers to get insights from customer lifetime value. Sure, what we've looked at here wouldn't impress a data scientist. But there are still nuggets that you can take from these customer lifetime value equations.
- Historic customer lifetime value is super easy to calculate. While you might have to do some guessing on the average customer lifespan, especially if you have a young shop, you can still get a ballpark estimate for how much you have gotten to date from existing customers.
- With the predictive customer lifetime value formula, we're still guessing customer lifespan. Meanwhile, retention rate and discount rate introduce a whole web of either (a) guesswork or (b) seriously hardcore math that you might not want to dive into.
- Even with the imprecise nature of these customer lifetime value formulas, they offer us a valuable guide. Customer lifespan might be unknown, but the amount of money your customers spend, the source that brought them to your website, the country their order shipped to â that data is all available. Your marketing spend per channel is also recorded. Use the concrete numbers at your disposal and make your best guess where necessary.
Success isn't just about finding buyersâit's about finding the right buyers. Now that you understand how to calculate the value of your customers, you can start crafting campaigns that target and win over those customers who really make a difference to your bottom line.
Customer Lifetime Value FAQ
What Do You Need To Calculate Customer Lifetime Value?
The three things you need to know before you figure out your customer lifetime value are:
- How often a customer buys something from your store.
- How long a customer shows brand loyalty generally.
- Your customer’s average purchase.
What Is the Formula To Get to Customer Lifetime Value?
As you’ve seen, there’s a lot of complexity you can add to your historic CLV calculations, but the most basic version is: (Annual profit per customer x Average customer lifespan in years) - Cost of acquiring customer.
How Do You Increase Your Customer Lifetime Value?
A healthy business is one that retains its customers, rather than constantly chasing new ones (that’s more expensive. Once you know your CLV, you can increase it by offering more personalization to your shopping experiences, use customer data to recommend products or build rewards and discounting strategies, and consistently engage with customers via social media and email campaigns.