February 16, 2017 | Emily Winsauer

Customer Lifetime Value: Why, How, and WTF

If you feel like you're behind the curve on calculating customer lifetime value, you're not alone. A surprising number of businesses have been hesitant to wade in to these murky waters, and even those who do often don't know how to make the most of this metric or what benchmarks to aim for.

Yet figuring out your customer lifetime value (CLV, or LTV) can be well worth the effort. For one thing, it gives you a clear portrait of how effectively you're reaching each customer. Since it's so much less expense to retain a customer than to earn a new one, low CLV indicates a lot of money left on the table.

Best of all, when you calculate CLV for your different customer segments or buyer personas, you can compare the real value of each part of your business. It's invaluable for determining the ROI of your marketing investment and making strategic choices about where to put your effort.

(Have you already calculated your CLV, but are looking to make improvements? Try our Google Sheet full of tactics for increasing CLV for all kinds of businesses, along with a bunch of other resources about customer lifetime value.)

What Is Customer Lifetime Value (CLV)?

CLV is a measurement of the net profit or revenue (depending on how you calculate it) earned over the lifetime of your relationship with a customer.

This distinction between revenue and net profit is important. If two customers both spent $40,000, but one of them required far fewer staff hours or overhead, the two have very different net profits. Revenue doesn't show that disparity; net profit does. For some businesses, this won't matter much, and calculating things with revenue will be much easier. For other businesses, it will make or break the validity of the metric.

You have been warned. Moving on.

Each customer has a unique lifetime value, each buyer persona or customer segment has an average CLV, and your entire customer base has an average CLV. When people talk about CLV, they're mostly speaking about the average of all of your customers, though it's far better to calculate it separately for each segment or persona.

One of the most commonly-cited examples of CLV comes from Starbucks. While the average person spends $5 on a fancy coffee drink once in a while, their total lifetime value is something like $14,000. That's a lot of Starbucks.

Britney drinks a lot of Starbucks.

Britney knows all about that.

Yet, if they were to break it down (and I'm sure they have), they would probably find that people like me who go fewer than a dozen times a year and mostly during the holidays contribute far less than the black-coffee-every-morning people. If my CLV is $1,2oo and Joe Caffeine's is $22,000, that would surely inform marketing strategy. And even if they still heavily advertise the pumpkin-mocha-gingerbread-frappa-latte, they may add incentives to convert me into a Joe.

It's worth determining which customers have the highest CLV. According to one recent study, the top 1% of ecommerce customers are worth up to 18 times more than average customer.

For some businesses, these top performers are the people who make regular small purchases, while for others, it's those who commit to a big purchase once a year—more likely, it's something in between. Regardless, identifying your best customers is a matter of ROI, not just for a single purchase, but over the life of your relationship with that customer.

Customer Lifetime Value Formulas

Customer lifetime value is traditionally very difficult to calculate accurately. Buyer behavior is complicated and can seem unpredictable, and most businesses are fairly complex. But, well, we do our best.

There are a number of different formulas you can use to get started, though you'll probably end up creating your own hybrid or custom formula. This infographic should give you an idea of the impact different CLV formulas can have.

While the complexity is totally justified, let's start with a simpler approach. To calculate the average customer value per year, you'll need these metrics for whichever group you're measuring:

  • Average order value (total sales within the year/order count during that year)
  • Purchase frequency (total orders within 1 year/total customers during that year)

That allows you to calculate average yearly customer value, by multiplying the average order value by the purchase frequency within that time period. Then, just take that and multiply it by the average customer lifetime to get CLV. Easy, right?

Woman thinks over the Customer Lifetime Value formula.

Oh, wait. We need to figure out customer lifetime.

In my opinion, this is the trickiest part of determining CLV. How do you know when someone is gone for good, instead of just taking a break from your products or services? Even subscription-based businesses may have a significant number of customers with gaps in service—or those people may be outliers.

For the answers, you'll have to turn to your customer database and your team's experience. Needless to say, if you know your customer retention or attrition rates, start there.

Look at your current processes surrounding inactive customers. How long does a customer have to be idle before they're considered inactive? Is it relatively accurate, or do inactive customers often become active again? You can also look at the length of time between purchases, and compare the upper range to your cutoff for labeling a customer inactive. If people frequently have longer gaps between purchases than your cutoff for inactivity, you probably need to adjust that process.

Once you have a reasonably correct cutoff for inactive customers, you can generate a list of inactive customers and measure the average length of those relationships before the customer became inactive, broken out by segment if possible.

The key to reaping the rewards of customer lifetime value is thinking long and hard about the impacts of different ways of calculating CLV and of slicing and dicing your contact list, so you can create the metric that is the most useful tool for your business.

This isn't as objective as it seems. How you define "customer" and "lifetime" are both subjective, and honestly you could really go down the "value" rabbit hole and calculate the value of referrals, too, if you're really a glutton for punishment.

There are certainly many formulas out there that you can use. Here's one from Wikipedia that makes me want to carve my eyes out:

Customer lifetime value ($) = Margin ($) * (Retention Rate (%) ÷ ([1 + Discount Rate (%)] - Retention Rate (%))

But hey—research different CLV formulas and find one that fits your needs. You may want to look to industry sources or those with similar business models for formulas that work well.

Lastly, if you're comparing your CLV to others' (which you should really never do, because you are almost certainly not calculating it the same way), be sure to check whether you're comparing revenue or net profit before you get all excited/terrified that your numbers are so much higher/lower.

Calculating Actual CLV for an IRL Sample of Customers

You can do this either as a starting point, or as a way to check your estimated CLV against a sample of real customers. Either way, start by identifying the most valuable segment of your business. If your business is all one buyer persona, even better!

Then, choose a sample of customers. You have several options:

  • Random sample - If your database is large enough, a sufficiently-sized random sample can give you statistically significant results.
  • Representative sample - Choose a range of customers who represent your customer base both in type and proportion. This option is good for smaller databases, but you'll need to be careful about bias.
  • Extreme samples - If you want to get a sense of where your average CLV stands against your best and worst performance, take samples from both ends of the spectrum and calculate your maximum and minimum CLV.
  • "Mode" sample - Remember grade school math? The mode is the most commonly occurring number in a series. Calculating the CLV of your most commonly occurring customer may be more valuable to driving business improvement than an average sample skewed by the extremes.

Once you have your sample, calculate the actual CLV for each person, and average those numbers. This should be relatively easy—you'll probably be able to find the total value of each customers' purchases in your CRM or ecommerce platform, or at least be able to create a custom report with that information. Just be sure to use the same measurements (e.g. revenue or net profit) that you do in your CLV formula.

If you're a new-ish business and don't have enough customers in your sample who have run the course of their "lifetimes" (i.e., are no longer customers), you'll have to decide whether to limit yourself to inactive customers, or to estimate the lifetime and project out the value of your sample customers. Work with your team to come up with an educated guess, do a little research to find an estimated lifetime in your industry, or use general estimate, like the 3 years that Shopify suggests for new businesses.

If you have more than one major segment of your customer base, you'll want to calculate CLV for each one. If they aren't what you expect, you have an incredible opportunity to make your business more profitable.

Remember when I said the goal was to create the metric that would be the best tool for your business? Here's an example. If you have a number of one-time purchases that are skewing your results, think about whether you'd be better off removing them from your calculations and addressing them with a separate set of tools.

If you do this, you'll be able to calculate the CLV of customers who do return, possibly eliminating customers who weren't a good fit in the first place. This may help you narrow and refine your buyer persona, and focus your efforts on those customers.

However, you shouldn't eliminate the one-time-only people with giving them due attention. Did you spend valuable marketing dollars to attract them, only to discover that they weren't your persona? Or are they potential long-term customers who dropped off as a result of poor customer service, poor retention efforts, or some other avoidable issue?

If they should be long-term customers, you may want to keep them in, because they represent a growth opportunity within that segment and correcting that problem will legitimately increase CLV.

Never forget that CLV can change quite quickly. Adding a new product or service, improving your customer service, and changing features can all dramatically improve CLV in a matter of weeks, though you may not be able to detect it in the data for a while.

Once you've calculated CLV, what can you do to improve it? 

We created a whole Google Sheet full of tactics for increasing CLV for all kinds of businesses, along with a bunch of other resources about customer lifetime value.

Just click below to get started!

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Emily Winsauer

Emily Winsauer

As VIEO's content director, Emily Winsauer was responsible for content strategy for VIEO and our clients for over 5 years. She recently moved to Seattle where she's still creating compelling content in her new role.

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