This algorithm lets us better regulate small changes, as it looks at how LTV is trending instead. Also, by looking at trends instead of using other methods to deal with highly variable data like lifetime capping—saying that the average customer will only stay with a company for a certain number of months or years a number that will be different for each company —we can also better predict an LTV for all types of companies, regardless of their size, retention numbers, or growth rates.
Figure 2. LTV calculated with the ProfitWell secret sauce in green. At the same time, the up and down fluctuations are maintained. Overall, our model of LTV provides more actionable data, while still ensuring that the information is presented in a reasonable and accessible way, leaving you with actionable insight, not fussing over peaks and valleys. Who needs more charts without a clear purpose? Discounts can have the opposite effect that you want on LTV.
A discount rate can offer short term gain. However, it can be damaging to your long term gain and plan for success. There are three reasons why discounts can hurt your company's prosperity. Customers that are attracted by discounts tend to be users that:. The table below shows a discount comparison group. One has had minimal discounts, while the other has had aggressive discount pricing.
You can see that the 3-month churn rate is significantly higher for the larger discounted group and shows a decrease in LTV , while the minimal discount had no negative effect on LTV.
Read more about how discounts affect LTV here. The CAC reflects the cost of bringing in a new customer. Combine these two metrics to create a ratio of revenue per customer to the cost per customer. If you are averaging a ratio, then you are looking at a losing bottom line.
Simply put, you are paying more to acquire a customer than that customer is paying you to keep the lights on. Lifetime value is a testament to the success of your SaaS business. It gives you an aggregate measure of upsells relative to downsells and churns across your entire customer base.
In an attempt to find better alternatives to using Net MRR Retention in the revenue expectancy element of LTV, Zuora is currently experimenting with a few new methods aimed at improving the state-of-the-art by attempting to estimate revenue expectancy individually, by customer.
These methods attempt to go beyond using just standard Net MRR Retention to a more personalized estimation. The idea holds promise. For example, Apple Music offers three plans:. Inherent in this packaging structure are two predictable upgrade paths Student to Individual, and Individual to Family.
Modeling those upgrade paths into individualized estimates of upgrade or downgrade likelihood could prove quite effective at improving LTV accuracy beyond a wholesale average net retention factor. The third element of lifetime value is estimating how much it costs you to deliver your product or service.
Each of your subscription products has a particular contribution margin associated with it that needs to be estimated. Contribution margin represents the variable costs that go into providing your product and is a measure of the profitability of your subscription products.
Some businesses leave out cost expectancy from LTV, but doing so leaves out an important element if you plan to make spending decisions based on LTV.
The final element of LTV is estimating how much risk your future revenue streams face. Risk expectancy is critically important because LTV is an estimate of what will happen in the future. The primary reason LTV is so important for your SaaS business is that it drives what you can spend to acquire new customers.
Generally speaking, users on your lowest-priced plans will also have the highest churn, making it your most dismal LTV compared to the other plans. And remember what we said earlier? LTV drives what you can spend to acquire customers. Knowing your average customer lifetime value is nice. But just looking at a number on a graph won't help you grow your business. So next, we're going to talk about how to use lifetime value analysis it's easier than it sounds and other tactics to improve your LTV.
All the tactics I'm going to show you can be done directly in Baremetrics. If you want to follow along, you can sign up for free here. When your goal is to improve your customer lifetime value, a good place to start is speaking to your existing customers. Step one is to find out what your current average LTV is. I'll grab ours from our Metrics dashboard. Could you spare 15 minutes for a quick call to answer a few questions? Hope to hear from you soon!
And always include a link to schedule a time for the call with something like Calendly or Doodle. Then just start gathering the feedback and organize it in a spreadsheet.
Look for trends that you can use to improve the LTV of your other customers. For instance, if you notice most of these customers came from a specific channel SEO, Google Ads, email, etc. Or maybe most of them use a specific feature of your product.
You can start marketing that feature more since customers get the most value from it. The interviews can be a huge helping hand in how you develop and market your product going forward. Looking at your LTV as a single number is nice to give you a 10, foot view. For example, for an eCommerce company this could be the average value of each cart, while for a subscription service this could be the cost of the subscription.
This tells you what part of each customer purchase is profit and what part is cost. This is the average number of transactions a customer makes over a given time period usually a year. Purchase frequency can be calculated by dividing the average number of purchases by the average number of customers.
For example, for a monthly subscription service, the number of purchases made over a year is This is the length of a typical customer relationship. To make calculations easier, this is generally measured in multiples of the same period as the purchase frequency. Improving customer lifespan is often a very effective way to improve your CLV. Note, customer lifespan calculations vary for different types of businesses.
For a SaaS product that relies on fixed contracts, the lifespan ends when a customer fails to renew their contract. For a consumer app, however, the product team will have to figure out when they consider a customer to have churned for example, after 2 weeks of no recorded activity. This article can help you understand the intricacies and nuances of calculating CAC.
Once you have the above information, calculating CLV is easy.
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