Answers from the CLV Experts
Got questions? We’ve got answers! Check out our FAQs below. If you’re looking for more resources, check out the Customer Lifetime Value Academy.
Frequently Asked Questions
Customer Lifetime Value (CLV) is a metric that estimates how much value (usually revenue or profit margin) any given customer will bring to your business over the course of the total time they interact with your brand—past, present, and future.
Customer Lifetime Value (CLV) is the single most important metric for you to know because it demonstrates who your best customers are and what they have in common. Using this metric effectively revolutionizes the approach to both acquisition and retention marketing, separating true industry disruptors from the rest of the pack.
Think about it—how would your marketing, sales, and product strategies and budget allocations change if you could predict which audiences will remain loyal to your brand for years to come? And which will visit your site and make just one highly-discounted purchase before falling off the radar completely? What if you knew that some customers you are already planning to spend lots of your budget to retain actually aren’t likely to bring lots of value to your organization anyway?
There are many formulas available to calculate lifetime value. However, calculating predictive customer lifetime value at the individual level requires a complex model. The steps include:
- Locate the necessary data
- Forecast every existing customer’s behavior
- Split the data into two sets for training and testing
- Add customer features and attributes to the model
- Train and validate the model
- Put the model into production
Read our whitepaper to learn 8 steps to calculate CLV.
Check out this blog post to learn how to make three types of CLV models: analytic aggregate CLV, analytic cohort-based CLV, predictive CLV using statistical models.
Not all CLV models are created equal. If you’re reviewing CLV from your organization or a vendor, start by asking yourself a few questions to see if the metric is accurate and useful.
- Is the CLV at the aggregate level or individual level? If it is aggregate, is it the mean or median? It’s pretty easy to calculate CLV at the aggregate level. Because most business use cases require individual level CLV, you’ll want a model that calculates CLV for each customer.
- Is CLV historic, predictive or some combination? If CLV is provided at the individual level, you want it to be predictive and not just historic.
- How much of CLV is already observed vs predicted future revenue/profit? The danger of using only future-predicted revenue is that you can no longer compare future-predicted revenue of a highly active current customer with a previous cohort of customers.
Read more about the CLV questions you should be asking in this blog post.
Customer Lifetime Value is calculated at the individual level, while Lifetime Value is an aggregate metric.
The most popular and commonly used customer lifetime value (CLV) models benchmark their strength on aggregate metrics. However, these models are incredibly inaccurate at the individual level. This becomes an issue because most business use cases require a strong CLV model at the individual level.
Aggregate CLV simply does not allow you to adjust who you’re targeting with re-engagement or acquisition efforts to maximize the benefits of each campaign.
Read more about the importance of individual CLV in this blog post.
Yes, customer lifetime value is not a static metric. It changes over time as you observe new data about a customer. Knowing this, you can adjust your retention and product strategies to increase customer lifetime value.
Learn more about how CLV changes with time in this blog post.
Customer lifetime value is an important tool for marketers: CLV can help you target, retain, and provide exceptional service to your best customers.
Once you know what attributes and behaviors make up your high-CLV customer, you can use that information to build lookalike audiences. Read more in this blog post.
If you’re ready to start using CLV to inform your marketing strategy, start here.
It’s relatively simple to segment your current customers by attributes and/or past behavior. But if you want to predict the future behavior of your customers, it’s better to segment by features and attributes that impact customer lifetime value. Read more about the process in this blog post.
Customer lifetime value (CLV) is the metric leading companies use to understand their customers’ purchasing habits. Simply put, customers who purchase higher-value products, who purchase more frequently, and who continue to purchase on an ongoing basis are your high-CLV customers.
Once you’ve calculated CLV, you can use it to determine your target customer acquisition cost (CAC). Instead of simply targeting customers that bring in high initial revenue, focus your acquisition efforts on high CLV customers. Even if their first purchase is small, they will bring your business more value in the future.
A customer acquisition is unprofitably only if CAC exceeds CLV. For most businesses, your CLV to CAC ratio should be 3:1 for each marketing segment. If you spend too much (e.g. 1:1 ratio), acquiring those customers won’t be profitable. But if you spend too little (e.g. 7:1 ratio), you will be missing out on profitable customers whose acquisition cost is above your current bid cap.
You can take your annual discount rate and turn it into a daily discount rate, and apply a discount factor, which is: 1 / (1 + X)^Y where X is the discount rate and Y is the time variable.
Typically, we compute the lifespan of every single customer using survival analysis. Once we’ve done that, you get a sense of what the longest life is of a customer. Based on that, you can choose the truncated time spans for computing customer lifetime value. We will typically calculate CLV for multiple year horizons, depending on the business. Sometimes, we will also compute full lifetime horizons as well.
Recency, frequency, and monetary value (RFM) is a simple method to determine customer value. Retina models take into account data beyond RFM, including demographic and behavioral attributes, customer journeys, quiz and session data, and more. All of these data points make up a comprehensive data set that allows us to use machine learning to impute missing values from messy data and make accurate customer lifetime value predictions early in the customer journey.