Shortly and bluntly: no!
Customers don’t make decisions reasonably. They buy emotionally then think about their purchase.
That’s why, too many smart people are trying to sell products that people need instead of focusing on building something that people want.
This is where the “Customer Lifetime Value- LTV” comes in. This is a very important concept that tries to show how much every customer will be worth to you over the period of their lifetime with your business.
Why Is Customer Lifetime Value so Important?
LTV is especially important when it’s coupled with another also important metric which is: Customer Acquisition – CAC. These two metrics are opposites and actually determine the success of your business.
So, theoretically, you could have 3 different scenarios:
1. CAC > LTV: Business Failure
In this scenario you are paying more money to get your customers than they are delivering you, so in this case the more customers you get the more money you will lose. Similarly, the faster you acquire customers, the faster you will run out of money.
2. CAC = LTV: Business Stagnation
In this scenario you are paying the same amount to acquire a customer as they are paying you back over their lifetime with your business. Here, your business is essentially flatlining.
3. CAC < LTV: Business Growth
In this scenario you are repaying your CAC over the course of your customer’s lifetime with your business, however, you are also generating additional revenue. Your business will grow, and you should continue to drive further customer acquisition. If the equation holds true you should accelerate your customer acquisition as that will grow your business faster.
It’s very common that entrepreneurs will either overestimate LTV or underestimate CAC, which lead David Skok from Matrix Partners to claim: “the second biggest cause of startup failure: the cost of acquiring customers turns out to be higher than expected and exceeds the ability to monetize those customers”.
Modelling LTV is a recommended solution to prevent your business from facing the common LTV problem. There are different approaches, and which one you go for is determined by how long you’ve been trading and how much data you have.
What if I Don’t Have Any Data?
If you have no data, then the only choice you have is to base your LTV calculations on a lot of assumptions. Try not to be too eager with these assumptions as you will most likely need to justify them when speaking to potential investors. First, we need to set some LTV constants.
- s = average spend per booking
- c = average number of purchases per year
- a = average (gross) customer value per year
- t = average customer lifespan (years)
- r = customer retention rate (what % of customers this year will be customers next year?)
- p = average margin per customer %
- I = rate of discount
- m = average gross margin per customer lifespan (a*t*p)
Unknown Discount rate, very basically, tries to model the fact that money is worth more to you now than it is in the future.
This becomes important when modelling LTV as predicting a future value doesn’t match correctly against a present CAC. In order to be able to accurately compare CAC against LTV you need to use the net present value of LTV.
Using the constants in the table above we can then build up some LTV formulas:
How Do I Use These Results?
LTV is a projected figure and is not going to be accurate. With any type of modelling the further into the future you go, the less robust your results become so be wary modelling over five years (especially with no or very limited data). As you may have noticed the equations will, most likely, have given you a number of different results.
One method to deal with these differing results it to average them all out, however I don’t recommend this method. The reason is; modelling produces error and by simply averaging a lot of erroneous values together you’re not really solving anything, in fact you’re just losing the clarity of where the error might have come from. If you stick with one of the equations above you should be able to explain how you got to your end result, what the assumptions are, and where the technique falls over. That’s a stronger and more justifiable position than just averaging all of them out.
What if I Do Have Some Data?
If you have some data you’re already in a much stronger modelling position, and now the key is to get the data to work as hard as it can for you.
At the core of any LTV modelling in a position when you have data is cohort analysis. Cohort analysis studies the behavior of groups of customers over time.
Your customers would normally be grouped into acquisition month and then studied over their time with your business. The first step in any cohort analysis is to structure your data in a way that facilitates easy pivoting. This will save you a lot of time going forward when you do this on a monthly (or more frequent) basis.
My recommendation is to format your data like this:
The example above is for a business with 3 months of data. Month 1 in January 2021 (2021-01) and month 3 in March 2021 (2021-03). With the data in this four-column format you can create a pivot table which will create a revenue array. You can see this demonstrated in this spreadsheet.
1st purchase month is what we call a cohort. Once you have your array, you can begin to accumulate revenue for every cohort you have. Your data could look something like this:
You should also know how many people transacted in their first month with you:
With these two data sets you can now start to organize customer lifetime revenue evolution.
Divide the cohort revenue by the customer number for each cohort: You should also know the amount of money you spent acquiring these customers, this data could look like this:
Combine this cost data with the number of customers in that initial cohort to get the Customer Acquisition Cost (CAC):
Now use your previously calculated cohort LTV alongside the CAC to understand your payback amount and Return On Investment (ROI):
An evolving LTV positive business will be able to chart their data, so it looks something like this:
This chart shows more recent cohorts having negative ROI, whereas cohorts from ‘m-10’ onwards have positive ROI.
If this trend continues this business, in an LTV sense, is doing ok. The key for this business is to try and work out how to pull the ROI breakeven time (or Payback Time) closer to the cohort start time. Doing this will expedite the business’ success.
Thank you, and hoping you found this article useful.