Article written by Nicolas Galland, with Loïc Jalmin. Thanks to Madeleine White and Maria Frih for their fantastic reviews and feedback.
You might have heard the term Customer Lifetime Value (CLV or LTV) being tossed around more and more in the digital publishing universe. Its application, though, can really differ. Some media outlets might only use it every now and then, perhaps in an occasional analysis, while others, like the Financial Times, have truly made it their guiding light (AKA North Star Metric).
There's no denying the buzz around it, but the topic can get a bit tricky. If you start asking questions, you might get a mixed bag of answers. Everyone seems to have a different opinion on what you can do with it or how to calculate it. In fact, the popular methods of calculation have even been strongly criticized by academics.
Used properly, this metric helps companies to understand the medium and long-term effects of their actions, and steer their digital subscription business model. Misused, it can lead to harmful decisions.
The goal of this article series is therefore to try and clear up as much as possible about LTV, and ensure you're not making any of these harmful business decisions!.
1. How LTV can help you make better marketing, product and business decisions
The most common use case for CLV is to use it to cap Customer Acquisition Cost. The idea: If a customer will bring us $x in their lifetime, we can spend $y on acquisition.
This use is possible in the media world, but it's not the most useful. LTV is one of the best tools at your disposal for taking into account the medium and long-term effects of your decisions. That's why it's a good idea to use it for a variety of purposes:
- Comparing different pricing strategies. Pricing not only affects acquisition, it also affects short- and long-term retention. LTV is a tool for better understanding the full effect of a price change, and sometimes makes you realize that a price decrease can, under certain conditions, also be profitable.
- Estimating the ROI of promotional offers and introduction offers: The long-term view offered by LTV enables you to better appreciate the value of your promotional offers.
- Estimating the ROI of future projects: If a project can help you acquire X number of new subscribers, reduce churn by Y% or increase your conversion rates by Z%, then the various forms of LTV can help you quantify the expected financial gain over different time horizons.
- Estimating the damage caused by an outage/bug (especially if it's the responsibility of a service provider). For example, if a partner causes a problem that costs you X number of acquisitions, then the long-term financial loss can be calculated using LTV
- Perform a customer-based corporate valuation
- Make revenue forecasts: using LTV to forecast future revenue by estimating the expected value of current and potential customers over a given period
- Emphasize the importance of loyalty and evaluate the financial impact of retention actions
- Serve as a metric for product management. The Financial Times refers to “[LTV as] a way for us to measure how successful we establishing longer term relationships and keep them with us for longer”
- Sizing up Customer Success efforts: Knowing a customer's potential LTV can enable the customer service department to better size efforts for that customer.
- Customer Segmentation: Using LTV to segment customers based on their predicted lifetime value. This allows media companies to target the most profitable customer segments and tailor marketing strategies accordingly.
- Advertising Campaign Optimization: Using LTV to optimize advertising campaigns by targeting audiences with the potential to generate high lifetime value and adjust advertising messages accordingly.
- Partnership Evaluation: Using LTV to evaluate the profitability of partnerships with other companies or brands. Media companies may choose to collaborate with partners who have access to high LTV customer segments.
- …
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2. But it's critical to properly define your LTV
There are many ways to understand the term “Lifetime Value”.
The first thing to ask yourself is what are you going to put behind the word “value”?
You've got a few options here. Some consider it to be the sum of revenue generated by the customer (also known as Customer Lifetime Revenue). Others will subtract the cost of acquisition (CAC) from this figure, and others still will also consider all other variable costs (setup costs, server costs, …). This last definition seems to be the one adopted in the academic world, but not necessarily the most common in practice. The most important thing is to be clear about the choice you are making within your company.
For digital publishers, an interesting definition is sum of revenues (Customer lifetime Revenue), because CAC is an unclear notion for a media that generates a lot of traffic on its own. Conversely, for a print-based medium, a definition that includes the cost of print production will maybe be more relevant for analysis purposes.
The second thing to define is what we mean by “lifetime”.
Some customers may stay subscribed for more than 10 years, but does it make sense in your analysis to take into account the revenue that a customer will generate in 10 years time? Sometimes it does, sometimes it doesn't, depending on how you're going to use CLV. It's not uncommon to limit the CLV horizon to 2 or 3 years. This is known as “Finite Horizon CLV”.
Defining CLV vs LTV
Most people use the terms CLV (customer lifetime value) and LTV (lifetime value) interchangeably, but others make a difference between the two. In this instance, CLV would be the value of a specific customer, while LTV would be the average value of a pool of customers. In this article, we'll use CLV in the “average value” (=LTV) sense.
Finally, CLV can be separated into 2 parts The value that has already been collected (Historical Value or Earned Value) and the future value (Residual Value). We'll investigate both these use cases below.
In a nutshell, using CLV correctly sometimes starts with choosing a more precise term.
3. For each use case a different definition
We've seen that CLV can be used for a wide range of decision-making purposes, but we also understand that a single definition won't be enough to cover all uses.
Let's take a few examples:
- If you want to know whether your CAC is correctly sized, you won't be interested in what a customer will bring in over their WHOLE lifetime, because you want your marketing costs to pay for themselves over a few months or years at most. A more appropriate CLV in this case would be a Finite Horizon CLV of 12/18 months for an average media or 6 months for a younger company.
- In the case of the estimation of the financial damages that a partner has caused, on the other hand, you'll be concerned about the money lost over a longer period, and a Finite Horizon CLV of 4, 6 or even 10 years (depending on the maturity of your market) will be more appropriate.
- As a final example, for revenue forecasts or estimates of pricing changes, we can use Customer Lifetime Revenue By Age, which we'll discuss in another article.
Rounding up the tour
As you can see, a single definition of CLV won't cover every need because the needs can vary across companies, among teams within a single company, and even within the same department. So, whilst it's acceptable (and normal) to have several definitions coexisting in your company the important thing is to choose the right definition for the job and name things precisely.
For example, the acquisition department may decide to use a “finite horizon lifetime revenue of 2 years”, the product department may opt for a “cumulative net profit over 4 years”, the legal department may occasionally opt for a net profit over 8 or 10 years, the customer success department may use the “earned value” of each customer to better gauge the effort needed for a specific customer.…
This is the end of part 1. In future posts, we'll look at the other keys to using CLV properly: how to avoid magic formulas, how to calculate it properly, how not to over-interpret it, knowing when not to use it... Stay tuned!
We are Loïc Jalmin and Nicolas Galland, we have both been helping media companies optimize their revenues and make decisions using data for 8 years (Le Monde, Radio France, Mediapart, CMI, Les Inrocks, ...)
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