The LTV: CAC is one of the most important tools that startup founders can use to assess their business and secure external funding. However, it is often misunderstood.
Toptal Finance Expert Ryan Downie explains in this article how to correctly perform the LTV: CAC calculation and how that should be adapted or interpreted based on the type of business model and sector.
In my work with startups, I have met dozens of brilliant and creative individuals. So, I was surprised by how much education they needed to prepare for investor pitches and to understand the importance of key indicators to include in financial plans. Even clients who were financially savvy or well-informed struggled to create compelling and informative narratives that showed mastery of forecasting nuance. Investors pay close attention to unit economics and other metrics, such as customer acquisition costs (CAC) or lifetime value (LTV). Google search results are often insufficient and can lead to disaster when the financial models are presented and challenged. A recently published article on the topic outlines some of the dangers associated with using metrics incorrectly. This is why I believe that founders and those who are interested in becoming founders will benefit from a tutorial on the calculation and method of KPI forecasting.
Cost of Customer Acquisition
Management teams in any business that has a marketing budget need to forecast and measure how much it costs to acquire each new user. This metric has a special importance for startups who create consumer-facing sites, mobile apps, and SaaS. It isn’t easy to evaluate the effectiveness of marketing strategies and plan capital-raising growth without this information.
In its simplest form, the customer acquisition cost (CAC) is calculated by adding up all sales and marketing costs in a period and dividing them by the number of new users that were added during this period. Anyone can find this information by doing a simple internet search, but an advanced model can tailor the metric according to the realities of the business.
Customers are defined as:
CAC is a complex equation that can be broken down into several different components depending on your business model. Google reports traffic acquisition costs (TAC), while Netflix tracks subscriber-acquisition costs (SAC). Installs, subscriptions, or in-app purchases could all be used to define a mobile app’s converted user. A company’s monetization plan determines each definition’s relevance. There are some subtle but important differences between these metrics. They all measure the amount the company has to pay in order to attract a new customer onto the platform. The founders should spend time selecting the best definition of “customer,” one that provides the most value.
It is not always obvious which expenses or user additions to include in formulas, even after the customers have been defined. The two main sources of user growth are usually sales or marketing activity and word-of-mouth from existing users. Many modelers hesitate to include marketing expenses in CAC because they do not directly influence word-of-mouth growth. In most cases, however, viral evolution can be attributed to paid change, as new users will drive their word-of-mouth ads.
In these cases, it is prudent to consider nuance and track multiple CAC numbers. CAC can be calculated for users who are marketing-driven, and this will give you a better idea of the effectiveness of a marketing campaign. This is especially helpful for understanding what happens when organic growth rates drop and the user base increases. A blended KPI that includes all new users can be a valuable complement to the analysis of the overall operation.
Definition of Acquisition Expense
The CAC expense component also presents challenges. All marketing and sales expenses are included in the simplest and most comprehensive methodology. Often, however, these expenses are more about building a brand than driving traffic. There may be very different types of users on the platform with LTVs that are vastly dissimilar. The best solution, once again, is to embrace nuance. It is important to track the broadest definition of acquisition costs. However, this should be supplemented with more granular data that provides specific insight into specific marketing campaigns for different types of users and user groups.
Lifetime Value and Revenue
Many businesses consider lifetime revenue (LTR) another important KPI. LTR helps companies to quantify the value of their average users. It is useful for calculating the return on investment in marketing and also to determine the optimal capital expenditure per user. The LTR formula multiplies the average lifetime of a customer by their average revenue.
LTV is the same as LTR in that it measures each platform’s value. LTV subtracts the direct expenses from LTR. This is often done by multiplying LTR with gross margin. LTV is useful because, in many cases, revenue cannot be generated if there are no direct expenses.
Determining user groups and defining users.
LTR and LTV can also require nuance, as with CAC. This is to ensure that the analysis is most effective. Businesses that have multiple monetization methods should also consider tracking separate LTRs for each segment in addition to the overall combined figure. It is important to follow LTVs for different groups if there are several pricing models (e.g., freemium, SMB/enterprise). Consider a mobile application that offers free features and in-app purchases but also generates revenue through a premium subscription. Management should be aware of the reasons why blended LTR is increasing or decreasing. This could be due to a shift in the rate of premium subscribers, a change in behavior for purchases made within apps, or alterations to subscription pricing. Each scenario will have different implications for performance evaluation and future strategy.
Measurement of Average Lifespan
LTV requires that the average lifetime of customers be measured or estimated. According to many sources, lifetime is determined by the churn rates of customers. A formula is often cited for calculating average lifetime: (lifetime = 1/churn rate). The data from installs uninstalls, and a mobile app can analyze sessions to determine how many months an average user is likely to engage with the app actively. This number is multiplied by the subscription fee if there is a fee per month to use the service of the app. This metric can also be used to track the average number of in-app purchases over some time or reflect a one-time fee for download.
This is a useful method, but in some cases, it’s oversimplified. This formula assumes all users will eventually leave, as well as a uniform rate of churn over time. In many cases, neither of these assumptions is valid. This can have serious implications for forecast accuracy. As services improve, new power users join, low-hanging fruits are consumed, and competition increases, churn rates change. The average lifetime should also include users who return to your platform after leaving, so it is important to track both new and returning customers. Some user lifetimes can be measured better in terms of units other than time. eCommerce platforms, for example, may measure the lifetime of customers in terms of total orders rather than months or even years. This is especially true if returning clients have long periods without placing orders.