Customer Lifetime Value (CLV): A Complete Guide and How to Calculate

High prices might earn side-eyes, but bad experiences end relationships. In fact, according to the latest State of the AI Connected Customer report, 40% of customers stopped buying from a brand in the last year due to inconsistent product or service quality. 

That’s why I think about customer lifetime value as a measure of customer satisfaction and commitment. How engaged are they? Are they expanding their usage and renewing each year?

The answers to these questions are strong indicators of whether a customer is likely to stay and if the relationship is worth investing in.

Understanding and improving customer lifetime value can help your teams focus on the right customers, reduce churn, and drive more sustainable revenue growth. When tracked effectively, it becomes a powerful lens into customer experience, product adoption, and long-term profitability.

What is customer lifetime value (CLV)?

Customer lifetime value (CLV) is the total revenue a business can expect from a customer throughout the entire relationship. It’s a forward-looking metric that helps you understand not just how much a customer has spent, but also how much they’re likely to spend in the future. That forecast is based on patterns such as renewal, product adoption, engagement, and predictive insights from AI for sales tools.

This makes CLV a powerful metric for identifying high-value customers, guiding your sales and marketing efforts, and increasing profitability.

Why is customer lifetime value important?

Most customers expect companies to adapt to their changing needs. To meet that expectation, you need to know who your customers are — not just today, but over time.

Customer lifetime value provides a clearer view of where to focus your efforts. It helps identify your most valuable accounts, uncover upsell opportunities, and spot risks early to prevent churn. It’s about maximizing revenue while also building longer, stronger relationships.

According to the latest State of Sales report, 42% of sales leaders cited recurring revenue as their top revenue source. Keeping your most valuable customers happy is just as important (if not more) than finding new ones — and much more cost-effective. 

Factors that impact customer lifetime value (CLV)

Each customer experience can influence whether they stay, grow, or churn. A range of factors shape customer lifetime value, from the level of customer satisfaction to the cost of retaining them. Here’s a look at some of those factors:

• Customer satisfaction: As I mentioned earlier, product and service quality is one of the strongest indicators of whether a customer will stick around. Poor onboarding, unresolved service issues, or frustrating product experiences can quickly weaken loyalty. Tracking satisfaction through direct feedback, usage patterns, or service cases enables teams to intervene before the relationship falls apart.
• Ease of doing business: Consistency and clarity go a long way. When customers experience friction after a deal is closed, unclear communication, or slow response times, their likelihood of churn increases no matter how strong the initial sale. 
• Product usage and adoption: Customers who fully adopt a solution and grow their usage over time tend to be more loyal, vocal, and profitable. CLV increases as customers expand to new features, products, or use cases.
• Acquisition and support costs: Revenue only tells part of the story. To fully understand the value, consider what it takes to win, onboard, and support a customer. Some accounts require significant internal resources but fail to scale, leading to lower overall profitability. That’s why the full CLV formula considers both revenue and costs to help prioritize which relationships are truly worth the investment. 

How to calculate customer lifetime value (CLV)

There’s no single formula for customer lifetime value, but this is the one most widely used: 

CLV = (Average Revenue Per Customer × Customer Lifespan) − Total Costs to Serve

This model works well when you have reliable historical data and a consistent pricing model. For example, if a customer spends $10,000 per year and stays with your company for five years, their gross CLV would be $50,000. If it costs $15,000 to support them during that period, their net CLV would be $35,000.

Customer lifetime value formula and models

More advanced models go beyond simple revenue calculations. They may factor in variable costs, discount rates, or predictive analytics based on usage trends, industry benchmarks, or potential for upselling. The goal is to capture not just revenue potential but also the long-term profitability of each account.

You might also calculate CLV using a predictive model, especially if your sales cycle is long or renewal behavior varies by segment. For example, let’s say a customer starts with a $5,000 contract, but based on similar accounts, you know that it typically grows to $15,000 within two years. You can factor that projected growth into your CLV forecast if usage data and engagement trends support that trajectory.

Some teams also apply a discount rate to account for time value or risk. A customer who grows quickly but churns after two years may be less valuable than a slower-growing account with steady expansion and strong retention. The model you choose depends on what you sell, the consistency of your customer behavior, and the amount of historical data available.

Metrics that impact CLV

These metrics give you a simple starting point for understanding CLV and segmenting customers by their value. Tools like Sales Cloud and Revenue Cloud make it easy to track things like revenue, renewal history, and product usage in one place. From there, you can add more insights, such as engagement trends, upsell patterns, or account health scoring, to refine your view. Then, use those insights to spot high-value accounts, flag churn risks, and guide decisions around renewals, upsells, or service investments. 

• Average purchase value: Total revenue divided by the number of purchases
• Purchase frequency: How often the customer buys within a set timeframe
• Customer lifespan: The average length of a customer relationship
• Customer churn rate: Percentage of customers lost over time
• Customer profitability score: Revenue earned vs. cost to serve (especially useful for comparing high-touch vs. low-touch accounts)
• User adoption rate: Number of users actively engaging with the product over time
• Engagement score: Logins, event attendance, or interactions that signal ongoing interest
• Product expansion: How customers grow across solutions — not just how much they spend

How to increase customer lifetime value

To improve customer lifetime value, I start by looking at the entire customer journey. Are we making it easy for people to buy, get support, and expand their relationship with us? Or are we adding friction that pushes them away?

Here are a few tactics I’ve used or seen work well:

• Keep communication consistent and proactive: One of the biggest CLV risks I see is when communication drops off after the sale. It shouldn’t take multiple emails to get an update — or a support ticket to find out who owns their account. Make it easy for customers to know who their contact is, what’s coming next, and how to escalate if needed. A sales engagement platform can help standardize outreach, track activity across teams, and ensure customers always know where to turn.
• Create growth maps by segment: Examine how customers typically expand — by company size, industry, or use case — and equip your teams with offers or outreach strategies to initiate next steps earlier in their journey. Since upsells and cross-sells account for 31% of revenue, according to sales leaders, this isn’t just a revenue driver — it’s an opportunity to help high-value customers get more of the right solution at the right time.
• Use feedback as an early warning system: Surveys help, but I don’t depend on them alone. I incorporate other listening channels, such as reference programs, product advisory groups, and check-in calls, so we’re prepared when something goes wrong. These offer early insight into issues that might lead to churn later on.
• Track adoption and usage data closely: When customers use more of what we offer and get value from it, they’re more likely to remain loyal. Monitor usage drops in your CRM, stalled feature adoption, or missed onboarding milestones, and use that data to inform customer success outreach.
• Build CLV into renewals and playbooks: Your most valuable customers shouldn’t receive the same level of service as a one-time buyer. Build CLV signals‌ — ‌like product usage, renewal history, and feedback into your CRM so teams have a clear view of account health. Use automation to surface that data in prep documents, renewal workflows, and account reviews. When insights are built in, it’s easier to know where to focus their efforts.
• Follow up on feedback: Whether it’s an NPS survey or a casual comment on a call, I make sure we close the loop every time. For high-value accounts, that might mean a personal message from the rep or AE. For others, we use automated follow-ups to acknowledge the input and share how we’re acting on it. Either way, the goal is the same: let customers know they’ve been heard, and make it clear their feedback is shaping the experience.

Customer lifetime value (CLV) examples

CLV becomes a lot more useful when you move beyond the numbers and apply it to real customer behavior. I’ve noticed two patterns recurring again and again: customers who appear valuable on paper but quietly churn, and those who seem insignificant at first but grow steadily over time.

High spend, low engagement 

Take a long-standing customer who spends $10,000 every year. On the surface, they seem loyal. But if they haven’t added new products, have stopped attending events, and no longer engage with your team, their future value might be limited. CLV helps flag that risk so you can decide whether to re-engage or reallocate resources.

Low spend, strong signals 

Now compare that to a newer customer who started with a small contract but added two more solutions within the first 18 months. They’re logging in frequently, submitting thoughtful feedback, and responding to outreach. Their current revenue might be lower, but their CLV is increasing quickly.

That kind of trend tells me a lot more than raw spend. I’ve even worked with teams that use CLV to optimize account assignments and provide high-potential customers with more personalized support. They use sales planning software to align resources based on long-term value — not just initial contract size.

How to predict and manage risks to CLV

Sometimes, signs of customer churn are clear. Other times, it’s easy to miss — especially when the customer seems active on paper. I’ve learned to watch for both.

Here are a few signals I pay close attention to:

• Decreased spend: If a customer who once expanded regularly is now scaling back or stalling renewals, it’s worth asking why.
• Lower usage or engagement: A drop in logins, feature adoption, or event participation can point to declining interest, even if they haven’t said anything directly.
• Missed check-ins or silence: If a customer who used to respond to outreach suddenly becomes quiet, it’s a sign that something may have changed.
• Increased support cases or complaints: A rise in frustration — especially if it’s new or unresolved — can quietly erode satisfaction and loyalty.
• Major business changes: Mergers, acquisitions, or strategic shifts on the customer side may alter their priorities. These moments are a cue to reconnect, not retreat.

How to track customer lifetime value with technology

Tracking CLV becomes less reactive and more strategic when you connect the right data in the right systems.

A customer relationship management (CRM) tool like Sales Cloud provides a good foundation. I’ve worked with teams that use it not only to log activity, but also to track revenue, product adoption, service history, and feedback in one place. This kind of visibility makes it easier to see which accounts are growing, which are stalling, and which need support.

Here’s how sales software can help:

• Real-time data integration: Sales, support, and product usage data should flow into a single system. When you’re not relying on manual inputs or siloed spreadsheets, it’s easier to maintain accurate and up-to-date CLV calculations.
• Customer segmentation tools: Segment customers based on spend, engagement, or risk level to focus outreach and customize account strategies.
• Lifecycle tracking: Follow the full customer journey — from first purchase through expansion or renewal‌ — ‌using revenue lifecycle management software to stay informed on what works, where value increases, and where drop-offs usually occur.
• AI-powered insights: AI tools built into your CRM can spot churn risk or surface next-best actions based on actual account behavior. I’ve seen them recommend the right time to reach out, flag gaps in adoption, or suggest relevant cross-sell opportunities without the guesswork.

Measure your customer lifetime value, and drive your business

Customer lifetime value is a shared lens into customer success for sales, service, marketing, and product. CLV not only offers insights into future revenue, but it also helps you understand customer growth, engagement, and long-term impact. With this knowledge, you can invest in the right areas and strengthen customer relationships to be more profitable. When everyone has access to the same data, it becomes easier to make decisions that benefit both your business and your customers over time.