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Customer lifetime value (CLV) is a measure of the customer’s purchases over a the course of their relationship with a company.
CLV can be calculated as a projection (predictive CLV) or using historical data (historical CLV).
CLV is affected by a variety of factors, including industry, pricing models, and frequency of customer purchases.
Many companies calculate the ratio of CLV to CAC to determine where to invest their marketing and sales efforts.
Customer lifetime value (CLV, or sometimes written as CLTV) is a metric that tracks the total amount a specific customer has spent, or will spend, with your company over the lifetime of their relationship with you. It can be a measure of their past purchases, or a projected estimate of their future spending. How you calculate that number may vary depending on the type of product or service you sell, but it will generally include the average cost, the average purchases per year, and the average number of years.
For example, a streaming service’s CLV might be:
CLV = Basic tier monthly service * 12 months per year * average length of relationship
CLV =Â $10/month * 12 months * 5 years = $600
This streaming service may have ad-supported tiers at a lower price, or premium tiers at a higher price, so this is the CLV for one specific customer using their most basic tier.
CLV applies to specific customers, while lifetime value (LTV) is a metric across all customers. Where a CLV can help sort individual customers into different segments, the LTV can help make business decisions about the entire customer base. The calculations are similar, using the customer’s average annual spend and the average customer lifespan. The difference is how that spend is calculated.Â
For an individual customer’s CLV, take their average purchases in a given year.
For all customers’ LTV, divide annual sales income by the number of sales.
In other words, the key difference between CLV and LTV is scale: CLV is one customer, LTV is all customers.
Customer acquisition cost (CAC) is a metric that tracks how much it costs to convince a prospect to become a customer. This includes the cost of your sales team’s time and bonuses, any samples or promos, and the marketing work done to convince that customer. It is typically calculated by adding the total cost of all those efforts (cost of marketing + cost of sales), then dividing it by the number of new customers.Â
(cost marketing + cost sales) / # of new customers = CAC
CLV is the amount of money that a company earns throughout the customer journey. This means the two numbers are often in opposition to each other — you want your CAC to be low and your CLV to be high. Many companies calculate the ratio of CLV to CAC to determine the high-value customers in which to invest their marketing and sales efforts.
CLV can be calculated based on purchases the customer has already made, or based on projected spends. Calculating these can help inform different models, which help companies predict income or identify behaviors to promote or segments to target.
A predictive CLV is a CLV based on projected (or guessed) future behavior. It uses the average amount a company believes a given customer will spend, and their estimated relationship length. This figure may not turn out to be accurate, but it can provide a useful estimate for what the organization’s income could look like. Of the two, historical CLV tends to be the more complex CLV to calculate.
The historical CLV is the amount calculated based on the customer's actual purchases — based on the length of the relationship so far and the actual dollar amounts spent. This figure is useful for determining actual amounts and gaining insights into customer spending habits. Because it relies on concrete, historical data, this CLV tends to be more straightforward to calculate.
A customer lifetime value metric provides insights into a customer’s behavior, which can provide marketing teams with a variety of information for decision-making and resource planning. Tracking CLVs can help an organization with:Â
Audience targeting - Customers with similar CLVs may belong in the same segment, depending on your segmentation model. Targeting a customer segment with similar spending habits may give you ways to increase their spend.
Identifying behaviors that raise CLV - By tracking CLV, teams can identify what the business can do to increase a customer’s average spend or purchase frequency. For example, a sports retail store may only see a customer two or three times per year, but holding a sale could entice one of those customers back an extra time. Identifying those ways to drive up CLV could help identify ways to move customers into higher value tiers.
Measuring and maximizing return on investment (ROI) - CAC is the measure of how much money is spent to acquire a new customer. This represents a business investment in that customer relationship, and CLV helps you measure and maximize that ROI. The CLV : CAC ratio gives you a clear picture of how much of a return you are getting, and tracking the CLV helps you identify the most valuable customers and which customer relationships are worth spending more resources to maintain.
Increasing customer satisfaction and loyalty - A customer with a high CLV suggests they are satisfied, because they keep returning for more purchases. This lets you identify which promotions or tactics to continue to keep the loyal customers returning, as well as search for methods to improve customer experience with lower CLV.Â
Implementing retention strategies and reducing churn - A customer whose recent spending behavior is lower than what their historic CLV suggests may be unhappy. Pinpointing customers at risk of churning lets you begin targeted efforts for improving their experience and customer retention rates. Identifying customers with a lower CLV also allows your teams to reach out and ask questions and find out and try to fix why they are unhappy.
The key variables in the CLV equation are:Â
How much the customer spends per purchase.
How frequently they make a purchase.
How long customer relationships typically last.
These factors vary depending on several things, including the business or industry, sales figures, and your marketing strategies to keep customers returning. For example, a monthly streaming service will have a different calculation than a grocery store where customers return on a weekly basis, or a car dealership that has customers coming back once every 5 to 10 years.Â
However, if you can determine an average spend, the purchase frequency, and a relationship length, you can calculate a CLV.
The general customer lifetime value formula is:
CLV = average spend * frequency of spend * customer lifetime
First, you should decide what units you are working with. Typically, CLV is calculated based on years: Starting with the number of purchases a customer makes in a given year. If the business has shorter customer relationships, the calculation may consider weeks or months instead. However, most CLVs will use annual calculations.
You need these three pieces of information to calculate a CLV:
Average spend - A sum of all the revenue attributed to that customer. How much the customer spends per purchase, on average. This will be a dollar amount. This will include large one-off purchases, skipped purchases, and regular purchases within a year. Don’t forget to subtract refunds or discounts. Remember that this is for one specific customer, not an average of all customers, which would be the LTV.
Spend frequency - How frequently the customer makes a purchase. This will be a number of instances, usually per year. For example, a customer who makes one purchase every three months would total four purchases per year. The units for this variable (years, months, weeks) must match the units in the relationship length, (as in, “per month,” “per week”, or “per year”) or be divided to match (as in, 52 weeks in a year, 12 months in a year).
Relationship length - How long customer relationships typically last. This number is typically measured in years.Â
You can also calculate CLV based on the customer’s actual purchases, or based on profits made after factoring in other costs, such as overheads and staff time. The actual purchase numbers may be easier to obtain, while the costs will provide a clearer picture of what an organization makesfrom each customer. In this calculation, a customer who spends $1000, but costs $500 to service, would have an average spend of $500.
Both approaches are valid, but organizations generally use the profit variable, and account for other revenue-related costs in other metrics, such as CAC.Â
Ultimately, pick the valuation that makes more sense for your organization. A business with a variety of per-customer costs may benefit more from the profit-centered approach, while businesses that have very uniform costs may benefit more from the revenue-focused version.Â
With a clearer picture of each variable, here is the CLV formula again:
CLV = average spend * frequency of spend * customer lifetime
Returning to the streaming service example:Â
CLV = Basic tier monthly service * 12 months per year * average length of relationship
CLV =Â $10/month * 12 months * 5 years = $600
The CLV for the streaming service’s customers at the basic tier will be $600 over five years.Â
However, remember if the $600 customer has a refund on their service — for example, for one or two months due to service outages — then the refunds also need to be subtracted from their total CLV. For a historical CLV, there will be records of these refunds. For predictive CLVs, you may need to assume or average in some refunds to account for a potential variance.
If the ad-supported tier is $5/month and the premium tier is $20/month, then the company can also calculate the CLV for those customers the same way:Â
CLV =Â $5/month * 12 months * 5 years = $300
CLV =Â $20/month * 12 months * 5 years = $1,200
Because different businesses interact with their customers in different ways, the metrics that go in to each CLV calculation may vary. A grocery store is likely to have people returning several times per month, and may be that customer’s go-to store for as long as they live in the neighborhood. On the other hand, a retail sports store is likely to see the same customer once or twice a year, and may not have as long a relationship with that customer.Â
The following examples illustrate a variety of different business models, and some of the adjustments those companies might need to make to calculate the CLV.
A family of three who spends about $150 on groceries every week:
Monthly spend: $600 ($150/week, and there are about four weeks every month)
$600/month * 12 months/year * 10 years = $72,000
An athlete who purchases equipment at the start and end of their season:
Because the costs of sports equipment can vary, take an average purchase price such as $400 per purchase.
$400/purchase * 2x/year * 3 years = $2,400
A customer purchases a monthly bookkeeping service and annual tax preparation:
Bookkeeping service: $20/month
Annual tax prep: $150/year (Divided by 12 months, $12.50/month)
$32.50/month * 12 months/year * 7 years = $2,730
A customer who upgrades their phone once, and pays for a family plan:
Family plan: $75/month
Phone upgrade: $850 / 5 year term (Divided by 5 years, then 12 months, is around $14.16, but round up to $15 and call that interest)
$90/month * 12 months/year * 5 years = $5,400
What a “good” CLV looks like will vary depending on the industry, business, and a variety of other factors. As illustrated in the examples above, the CLV for a grocery store may be a lot higher than the CLV for cell phone providers. However, as people rarely want to travel too many miles to the grocery store, it has a smaller customer base, while a cell phone provider with national coverage could sell service to anyone within a given country.
A more useful metric may be the CLV to CAC ratio, which compares how much money a company spends to attract customers, versus how much money the customers spend. A ratio of 1:1 means that all your marketing and sales work to attract customers costs the same amount as the customer spends. Multiple sources say that a CLV to CAC ratio of 3:1 or higher is a strong ratio.Â
When discussing an individual customer’s CLV, the focus falls on roles that work on those specific customer relationships. The sales or customer success teams can find a use for both historic and predictive models for calculating a CLV. It is also useful for the data team to track and use in various models.
CLV can also inform marketing efforts. The marketing team’s campaign work is part of the CAC variable in the CLV-to-CAC ratio calculationMoreover, marketing teams can use CLV as a variable for creating marketing segments. This means that managerial or directorial marketing roles, or roles that work with market segmentation (such as performance or growth marketers), may focus directly on CLVs or LTVs.Â
While the customer lifetime value formula is relatively simple, there can be some challenges in identifying the correct numbers and uses for the metric.Â
When looking at historical data, the biggest challenge will be correctly identifying the customer’s habits. If a customer makes a large, one-off purchase within a given year, failing to account for that being an outlier (a point of data that does not fit an established pattern) could inflate the numbers.Â
Additionally, it may be difficult to definitively attribute purchases to a customer directly instead of family members or friends using their account. Marketers should keep in mind that CLV is usually associated with a customer’s account, and does not factor in non-addressable marketing channels, like radio or television ads. Identity resolution solutions can help with this.
Predictive models use averages to try and guess at future behavior. Customers may end their relationship early, or make fewer purchases than anticipated. When using a predictive CLV to make decisions, remember that a predicted CLV may differ from the actual CLV in a few years’ time.
When it comes to predicting, calculating, and tracking CLVs, there are technical challenges to consider. A lot of technical and data infrastructure goes into calculating the CLV, including customer data management systems, data warehouses, and identity resolution solutions.
You can increase CLV by improving any of the variables that make up the equation: average spend, spend frequency, or length of relationship. Again, ways to do this may depend heavily on the business and industry. Here are some general best practices to consider when working to increase the number of high-value customers:Â
Personalization and audience segmentation - By grouping customers, or creating personas, you can engage with those customers more directly, and tailor your marketing to address their concerns. A customer who feels engaged may also increase their average spend.
Upselling and cross-selling - Not all products solve all problems, but your company may have a second solution to related problems your customers face. Convincing a customer to use a higher-tier service or second product can increase a customer’s average spend.
Loyalty and referral programs - Getting customers to return is one of the best ways to maintain a high CLV, and incentivizing those repeat purchases can help increase their likelihood to return. Having a customer invite their friends can also increase the loyalty of the customers sending the invitation. These kinds of loyalty programs can increase the average spend or the spend frequency.
Subscription models - While one purchase is good, recurring purchases are usually better for CLV. Moving a customer from a few purchases per year to a monthly or annual subscription can increase the spend frequency and makes the CLV easier to calculate.Â
Post-purchase engagement - If a customer feels supported and engaged through the lifetime of the products they buy, they may return to buy, increasing the length of the customer's relationship.
Good customer service - Unfortunately, products don’t always work as expected, or customers run into issues getting the most out of their services. Providing good customer service can lead to high customer satisfaction and increase the length of a customer’s relationship.
Predictive analytics and retargeting - If you can meet a customer’s needs proactively or refocus your marketing efforts toward them, this may mean they’re getting more services they need. This can increase the length of the customer's relationship but may also increase their average spend.
Winback campaigns - When customers churn, you lose that relationship unless you can find ways to re-engage a lost customer through a winback campaign. While this most directly increases the length of the customer relationship, it can also be an opportunity to cross-sell, upsell, or provide additional support.
‍Note: The bolded variables are most likely outcomes, but some products or industries may see different variables change in response to these outcomes.
