Customer lifetime value (a metric of many abbreviations, including CLV, LTV, and CLTV) is fundamental for consumer brands to understand, track, report on, and work to increase over time.
We've built this guide to provide an authoritative resource on a variety of LTV topics. We'll cover what it is (and what it is not), how to calculate it, how to analyze it, how to increase it, predictive LTV, LTV:CAC, and more.
What is customer lifetime value?
Customer lifetime value is gross margin per customer over their lifetime with your brand. Gross margin is what’s left after you subtract your landed cost, or what it costs you to manufacture (product cost) a product and ship it to your warehouse (freight costs, taxes, duties, insurance).
In a nutshell, customer lifetime value tells you how good you are at retaining customers who purchase more than once, and although it does not indicate true profitability (i.e., net profit), it is a necessary profitability metric to guide decisions.
We see confusion around CLV because it is labeled and calculated in a variety of ways, almost all of which are completely or partially incorrect.
1. It is not total sales or predicted sales value. Respectively, these metrics are lifetime revenue (LTR) and predicted revenue. Many companies end up relying on these metrics without realizing it because they don’t understand how to calculate CLV.
2. Many brands calculate CLV as revenue minus product cost. This is an inaccurate calculation of gross margin, and it can cause major problems in understanding how much you can invest in growth. For example, if you think that you are going to make $100 in gross margin from the average customer over a 12-month period (when you’re actually only making $75 in profit), it greatly impacts how much you can afford to invest in marketing.
Timeframe is everything with customer lifetime value
When someone asks you, “What’s your LTV?” your answer should not be a single number. Because there’s an inherent time component in the calculation, and the answer can change over time, you always need to provide context.
A correct counter-response to that question would be, “For which group of customers, and at what point in time?”
After that, you can answer the question by saying something like, “Among customers acquired last year, our 1-year LTV is $40.”
How to calculate customer lifetime value
CLV is time-dependent. Therefore, you calculate it for a particular time period, which is technically average customer lifetime value.
The time period you choose should be specific to your business and take into consideration factors like frequency of purchase and seasonality. Standard windows many brands use to measure LTV are 6, 12, 24, and 36 months.
We recommend a two-step customer lifetime value formula to calculate it. First, calculate your gross margin for the specified time period (don’t forget to include landed cost!). Second, use gross margin in the formula, as illustrated below.
However, the number (or numbers) that you end up with should not be the end of the story. Calculating average customer lifetime value is only a snapshot. To really dig into it, you need to see trends by way of graphs and other visualizations.
Customer lifetime value visualizations are your best friends
We believe the most useful representation of CLV is not as a stand-alone number (even with context) but as visualizations: specifically, with graphs. A graph gives you the business-critical information to see how the value changes over time.
Over time, you want to see an increase in CLV. In the example below, you can see a positive trend for this brand’s CLV.
Visualizations allow you to quickly understand what's going on with your business. When you are calculating customer lifetime value manually, you may, for example, assume a standard percentage to calculate gross margin across all your products. While this will give you some useful information, not knowing true gross margin at a product level prevents you from gleaning insights to inform more effective (and scalable) pricing and marketing strategies, such as for promoting higher margin products. This is just one example of how if one portion of this calculation is off it can greatly impact the takeaway.
At Daasity, we aggregate all your data in one place, automatically update it every day, and provide these out-of-the-box CLV visualizations for you to assess your brand's performance.
What customer lifetime value visualizations tell you about your business
With a clear understanding of your CLV through visualizations, you can easily answer a number of questions. For example: are our customers profitable? Are they profitable enough compared to their acquisition cost? Is our CLV trending up or down? Are our more recent customers better or worse than customers we acquired last year?
Beyond average customer lifetime value, there are ways to segment on an CLV basis that you can leverage in your business. We recommend using visualizations to look at segments. Visualizations need context to be truly valuable, and for that, you need to compare several curves on a single graph to find the golden nuggets. Below are three LTV visualizations/breakdowns to track (and that Daasity provides):
- LTV by time since first purchase
- LTV by first product (or SKU) purchased
- LTV by marketing channel
LTV by time since first purchase: A great way to segment LTV is based on how long it has been since customers’ first purchase. For this brand, LTV growth significantly slows down by the 2 year mark.
Insights: Evaluate customer behavior in the three months after acquisition (no matter what month or year they were acquired). For example, what’s the 90 day LTV of customers the business acquired 1 year ago versus 3 years ago? Are you getting better or worse with customer retention? To avoid this type of trend, it’s important to stay on top of repurchase rates and AOV in order to keep customers engaged and buying for as long as possible. That first three months is critical to determining their long-term value.
LTV by first product (or SKU) purchased: It’s also useful to break out LTV at the product level, as you may find that customers who buy certain products or categories of products tend to stick around longer and purchase more than average. If you can understand some of the characteristics these customers share, you can tailor your marketing to acquire similar customers. You also may want to promote those products/product categories more heavily.
Insights: Analyze customers who bought product X compared to those who didn’t to help you determine which products/product categories lead to higher CLV for a specific time period. With this data update your Acquisition funnel to target customers who buy that product first. Acquiring customers that lead to a higher LTV over time gives you more bandwidth to more heavily invest in new customer acquisition and accelerate your growth.
Real-world example: Using Daasity's visualizations, bra brand Harper Wilde found that their customers who ordered non-underwire bras as their first purchases had higher LTVs than those who ordered underwire bras.
LTV by marketing channel: Breaking down LTV by marketing channel can be insightful because you may find a trend that may change how you allocate your budget—specifically, to channels that deliver more profitable customers.
Insights: You might find that customers acquired through Channel A actually spend more money throughout their lifetime than customers acquired through Channel B. Therefore, while Revenue and ROAS numbers might make Channel B look more successful it’s actually the Channel A customers that are worth the greater investment. When you identify the product and channel combinations that lead to a higher LTV you can re-architect your acquisition strategy around what will drive the highest long term value for the brand.
You can unearth a wealth of information for your business by using segmented data to calculate customer lifetime value—and you can basically segment customers however you wish to explore different questions around LTV.
Also, no matter how you segment, when analyzing the curves, always be thinking: what did we do differently during this time that may have impacted the customer and increased or decreased their LTV? Did we raise prices? Launch a special email campaign? Launch a successful SMS campaign?
These visualizations can be very powerful in helping you determine what’s working to keep customers coming back to purchase again.
How to Increase Customer Lifetime Value
As shown in the visualizations, consumer brands can increase LTV by working to acquire more customers from more lucrative marketing channels and by better understanding customers who purchase particular types of products from your brand.
However, there are numerous other ways to increase LTV. Consider trying some of these strategies:
- Optimize your customers' shopping experience to offer compelling upsells ("Do you want a 10-pack instead of a 6-pack?"), cross-sells ("Would you like these complementary socks with the pair of shoes you're buying?), and product bundles ("Customers often purchase Y, Z, A, and B with X").
- Introduce product line extensions. Can you offer a luxury or upmarket version of a product you sell? Can you add some modifications like a new flavor, organic version, new style or color options, or increased functionality? This can increase the value of new customers' first purchases over time.
- Offer free shipping (or free shipping with a minimum purchase total). Customers love free shipping, and encouraging them to spend more to get it will boost the value of their first purchase along with average order value (AOV).
- Personalize the customer experience. Collect and leverage Zero-Party Data to tailor experiences (marketing and onsite). For example, you can implement targeted pop-ups depending on what channel a customer is coming from. Or, base marketing material on survey/poll results and other preferences. Or, can you improve your CS team to solve potential customer problems through SMS, or their preferred contact method?
- Implement a loyalty or rewards program. Loyalty programs are table stakes now: 90% of companies now have some kind of loyalty program, and 75% of customers prefer companies that offer rewards. Making customers feel special, offering compelling rewards and points systems, and building loyalty among them will increase the chances they will continue buying from you over time (which means a higher LTV!).
- Offer a subscription option. If you sell items that can be sold through a subscription model (e.g., beauty products, certain home goods, food or beverage, among others), a subscription can increase the amount of time a customer spends with your brand. If you offer annual or longer-term subscription options, you'll also have guaranteed revenue.
What is LTV:CAC Ratio? Why is it important?
The ratio of customer lifetime value to how much you spend to acquire a customer (i.e., customer acquisition cost, or CAC) is a critical metric to know and monitor. It indicates whether your business is earning enough money to offset your acquisition costs over time.
The challenge is that your CAC will keep increasing over time, as advertising costs (along with other costs) go up, so you will have to constantly work to increase customer lifetime value to maintain a healthy LTV:CAC and a profitable business.
A young company may not be profitable in its early days. However, with healthy enough numbers, financial partners tend to be more willing to infuse cash to help the company become profitable sooner.
An average benchmark for a healthy ratio is above 3 after three years. This means that LTV is at least three times greater than CAC. Ideally, the LTV:CAC ratio should improve over time.
How to leverage customer lifetime value for your brand
Customer lifetime value doesn’t represent the pulse of your business, but you can consider it the health metric.
We recommend reviewing CLV trends on a monthly and quarterly basis. If you do see big changes (i.e., negative ones), it’s time to review your customer retention strategies and determine what changes need to be made.
Using data and knowing your numbers can empower you to test new acquisition and retention approaches to see if they positively impact LTV.
For example, analyzing LTV can help you explore questions like:
- Should we restructure how we acquire new customers based on which channels, offers, or products attract high LTV customers?
- Should we spend more on retention (e.g., free shipping, VIP events) versus acquisition (e.g., advertising, deep discounts) tactics?
- Should we cut budget for channels that don’t deliver the same quality of customer?
- Can we better optimize our paid media budget, e.g., use characteristics of our high-LTV customers to target look-alike audiences for Facebook ads?
- How can we ensure we’re profitable on first purchases, particularly if our products/services don’t have a high rate of repurchase?
- Does our welcome email series for new customers increase LTV?
- What are we doing for High Value Customers–special incentives, limited additions, experiences–and are they effective?
To help you understand how to target your most profitable customers, check out this article on RFM—Recency, Frequency, Monetary—scores and how Daasity can help you segment out your best (and worst) customers.
What is predictive customer lifetime value?
This post has discussed calculating LTV using historical customer data. There’s another way to calculate LTV that you may hear about: predictive customer lifetime value.
Predictive customer lifetime value is a whole separate beast.
It uses complex modeling to predict future customer behavior and profitability. Predictive customer lifetime value modeling may come into play for very young companies without a lot of historical customer data.
However, in our experience, for most businesses, calculating the metric based on historical data gives them more actionable data to inform their strategy to optimize for a healthy business over the long term.
While predictive LTV is a great metric to understand how much value customers will create over a given period of time, historical CLV is a better indicator of when to send that next message to customers.
The more historical data you have, i.e., the more times customers have purchased, and the shorter timeframe you’re looking at into the future, the more accurate your historical CLV estimate will be.
Bonus Section: Don't make the mistake of conflating customer lifetime value and customer loyalty
A common misunderstanding we see is when brands assume that high CLV means high customer loyalty. This is understandable, as it does make sense at a high-level: if a customer has spent a ton of money at your brand, they probably love you, purchase from you often, and are super loyal customers.
But this may not be true.
A customer may have an incredibly high LTV, but they may not have purchased from you in 6 years. Although at one point, they may have been a loyal, engaged, and active customer, if they haven't purchased in that long, they're well past churned.
This is both why predictive customer lifetime value is so challenging and why we recommend using RFM as a truer indicator of customer loyalty: because RFM factors Recency (in addition to Frequency and Monetary) as dimensions to evaluate customer behavior, it is a more reliable indicator of future customer behavior.
The time to figure out customer lifetime value is now
We can’t overstate the importance of customer lifetime value trends and why you should analyze it in different ways to help you find the insights you need to keep your business healthy over time.
The name of the game is to acquire and retain your most valuable customers and optimize your efforts and (cut) budget for customers who will not have high CLV.
Daasity helps make calculating and monitoring customer lifetime value more streamlined and understandable, with visualizations that bring the data to life for teams.
Interested in seeing your LTV trends? Contact us.