Categories: Data Science
Categories: Data Science

Keyword: Lifetime

Your Customer Lifetime Value (Customer LTV or just CLV) represents the total amount of revenue you’ll receive from a customer during the whole duration of their relationship with you.

All the money they spend with you, all time. High LTV = Good. Low LTV = Bad. Seems simple right?

Well, it’s actually very easy to get confused when you dive into the details, as we’ll see shortly. This article aims to give a simple explanation of the how we see LTV here at Whirl.

As a matter of taste, we’ll use the acronym LTV instead of CLV for the rest of this article. Many businesses call their customers ‘Guests’ or ‘Partners’ or ‘Parents’ or ‘VIPs’ or some other vertical-specific term to highlight the fact that their relationship is important. Shifting focus away from transactional thinking is the first step to increasing LTV.

Value: Revenue or Profit?

The definition we just listed says that LTV refers to revenue, not profits. You could argue profits are more important as they easier to align with your customer acquisition costs (CAC) to determine if you have a viable business model, which is true.

In our case, we’ll use the definition of revenue as it is simpler to calculate and think about, but we don’t want to lose sight of the fact that businesses aim to drive profit.

Who wants a bunch of customers with $1,000 in LTV if serving them costs you $2,000?

That said, when taking a deep dive into LTV, it makes sense to evaluate gross margins in different products/services if you are comparing the LTV of two vastly different segments. For simplicity, we’re assuming the two segments have comparable gross profit margins. Therefore, a higher LTV is better.

Market Size?

Imagine you’re a hair salon and you’re lucky enough to have found a segment of your customers that will stay for life and are willing to pay you $1,000,000 in their lifetime for haircuts. Caveat: there’s only one person in that market and it’s your mom. Obviously this is hyperbole, but the key insight is that you can’t over-optimize on LTV because you end up shrinking your total addressable market (TAM).

As we dive deeper into the LTV, we must also remember the balance between LTV and TAM. Admittedly though, a business with half the customers but twice the LTV is typically operationally easier to manage. This assumes all other things are equal in both businesses (such as the same gross profit margins). Therefore, as a rule of thumb, it is a good idea to increase LTV.

Calculating LTV

The most intuitive way to think about LTV is just to say it’s the sum of all purchases made by a customer all time. Customer #1 spent $1,000, Customer #2 spent $500, etc. Then you’d average it out for everyone. If your business has been operating long enough, that may be a close enough approximation. However, maybe you launched your business a year ago. In this case, imagine Customer #1 is still actively using your product/services. Maybe they’ll spend $5,000 with you before moving to a new city. Due to the loyalty of your customer base and the fact that you’ve only seen a portion of their lifecycle with you, you’re under-estimating your LTV if you just average out sales per customer.

To have a better idea of the LTV, and read into the future, you can start breaking LTV down into its intrinsic parts:

LTV = Average Spend Per Visit * Average Number of Visits

Average Spend

It’s usually easy to figure out the average spend. You can average out a customer’s purchases, regardless of how many they made. It doesn’t matter if they are still active clients or not, the average can be computed.

Alternately, you can look at your price list and say most people visit my business to get a massage and they spend $100 for one massage. You could tweak the number based on your product mix (massage durations, etc.) to get a more accurate representation of spend.

For simplicity’s sake, let’s ignore the impact of future price increases or inflation.

Average Visits

As for average number of visits, it gets trickier. Math to the rescue! We’ll get to an average number of visits by evaluating retention.

After a visit, there exists a portion of your customers who will eventually come back. This is your retention %. Everyone else who you haven’t been able to retain is your churn %. You can read into the future if you know how good you are at retaining customers on average.

This is because, you’re able to compute the average number of visits (and thus your LTV) if you have your retention % thanks to the following formula:

Average Number of Visits = 1 / (Churn %)


Average Number of Visits = 1 / (100% – Retention %)

We’ll dive into computing Retention % in a minute, but for now just think of it as looking at your full customer list and seeing what % of those folks visited more than once. That would be your retention % after the first visit. From those, you can see who visited at least 3 times. That would be your second visit retention %. You can then do a weighted average over all the visits to see your overall retention rate.

Note: Some industries like to look at retention % as the % of folks who visit again within a certain time period. For example, salons often talk about 90-day retention which indicates the % of customers which will return for service within that 90 day window. That flaky customer who visits once or twice a year wouldn’t count as retained.

The Math

Skip this section if you trust us and don’t want to get into the weeds, otherwise read through the following example.

We’ve found that some folks are surprised by the formula for the average number of visits, so we figured we’d add some meat around the bone and make it more visual.

Let’s assume 80% of your customers will eventually return to your business. Based on the equation above, they will visit an average of 1 / (100% – 80%) = 1 / (20%) = 5 times.

Here’s a quick visualization in table format.

Above shows a hypothetical store with 50 initial customers (an arbitrary number). For simplicity, assume we have no new customers ever finding the store and we focus our attention on how things evolve for these 50 initial customers.

By looking at columns left to right, we show how many customers visited at least once, at least twice, at least three times, etc. Assuming an 80% retention rate, you will have 40 of those 50 who visit a second time. Then, 32 of those 40 will visit a third time, etc. Theoretically, this table could go on for a countless columns (a very long time!) but we see there are only 4 customers out of our initial group who make it to 12 or more visits. We’re showing a nice round number of customers for each visit for simplicity, but let’s expand:

Another way to look at this is to say 10 customers visited once and only once. 8 customers visited exactly twice, etc. This is the 20% of people who churn after every visit. Here we left the decimal numbers to give it a bit more precision in this theoretical model.

Thus, if you are trying to count how many total visits there were, you would sum up: 10 customers * 1 visit + 8 customers * 2 visits + 6.4 customers * 3 visits + …. = 250 total visits. Again, we’re not rounding here and we’re assuming a large enough number of columns so we can see the full lifetime of this segment of customers. And if you say 250 visits divided by 50 customers, that’s an average of 5 visits per customer, which is what we had in our formula.

Using a more actionable perspective on retention

Great! We now know how to compute our LTV by looking at our average spend and our retention %.

LTV = Average Spend / ( 100% – Retention % )

Up until now, we’ve demonstrated this formula by talking about average spend per visit, and % retention from one visit to another. However, you can use the same formula if you switch to average spend per month and % of retention from one month to another. Here at Whirl, we prefer to analyze retention on a month-over-month basis. We feel it’s easier to talk about customers and say this group spends $100 a month and will stay with us on average for 24 months so the LTV is $2,400.

We come from a franchise operations background. We know that month-over-month talk helps us operationalize our insights with ease. Talking about the customer journey in a timeline is natural. You can illustrate the journey for hourly staff easily when you explain that the first month is the most critical period for a new customer, the next 3 months they’re still becoming acclimated with your service and then they become regulars. It’s unnatural to do the opposite and say between visits 4 and 11 customers is a critical period in the customer lifecycle.

The great news is thus that you don’t need to have a ton of data to get a general feeling of your retention % or your LTV. You can look at a cohort of folks who visited in your first month of operation, break it down as per above and compute the 90-day retention rate. The rate will evolve over time, but you get a decent peek in the early days of your business. Over time, tracking the evolution of your retention rate can become one of your most powerful operational tactics.

What do you do with your LTV?

Overall, the LTV is the basis of a lot of customer intelligence analysis. The LTV can become your north star to compare different operational/marketing strategies you’re deploying in your business.

I’ll give a few concrete examples:

  • Who do you think has the biggest LTV… a customer of Blockbuster where you’d rent individual movies or Netflix where you have a subscription service?
  • Groupon brought you a ton of new customers in exchange for a heavily discounted first visit. Is that cohort profitable for your business in the long run?
  • You do an aggressive Instagram campaign ahead of your new store opening. You get lots of new customers, but do they stick around and offer a good LTV?
  • Anecdotally, I know customers love working with Jessica and they always come back. Could we look at per-staff LTV to see if there anyone on the team that’s driving customers away?

In a multi-unit environment, it can help identify which locations offer inconsistent service or have poor facilities. By comparing a unit with their peers, a regional manager can see if a unit doesn’t have the required growth due to insufficient marketing or due to poor retention (the “leaky bucket” problem).

There is so much we can accomplish once we have tools in place to track our LTV. It is cumbersome to compute from point of sale data but, you guessed it, it’s baked into the Whirl platform so you don’t have to do any of the heavy lifting.