The Complete 2026 Guide to Customer Lifetime Value (CLV)

Customer lifetime value (CLV) is a measure of customer satisfaction and commitment. How engaged are your customers? Are they expanding their usage and renewing each year? Would they recommend you to others? 

The answers to these questions are strong indicators of whether a customer is likely to stay with your brand and business. 

Bad experiences can 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. 

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’s a powerful lens into the customer experience and your long-term profitability. This guide will show you how CLV works and how to optimise it for success.

What you’ll learn: 

• What is customer lifetime value (CLV)?
• How to calculate customer lifetime value (CLV)
• Why is customer lifetime value important?
• Factors that influence customer lifetime value (CLV)
• How to increase customer lifetime value
• Customer lifetime value (CLV) examples
• How to predict and manage risks to CLV
• How to track CLV with technology and AI
• Measure your customer lifetime value and drive your business up
• FAQs

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, 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.

Customer lifetime value formula (and how to calculate it)

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

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

To break this down further, here’s a table that details the business metrics you’ll need for the customer lifetime value calculation. Follow these steps to arrive at CLV.

How to Calculate Customer Lifetime Value (CLV)

Metric	Formula	Example
1. Average Order Value (AOV)	Total revenue ÷ Total purchases	$150
2. Purchase Frequency (PF)	Total purchases ÷ Total customers	4
3. Average Customer Value (CV)	AOV × PF	$150 × 4 = $600
4. Churn rate	Customers lost during time period ÷ customers at start of time period	200 ÷ 800 = 0.25
5. Average Customer Lifespan	1 ÷ Churn	1 ÷ 0.25 = 4 years
6. Annual Costs to Serve	Variable Costs (Support + Fees) + One-off Costs (Onboarding)	$200/customer/year
7. Total Costs to Serve	Annual Costs to Serve × Average Customer Lifespan	$200 × 4 = $800
8. Customer Lifetime Value (CLV)	(CV × Average Customer Lifespan) − Total Costs to Serve	($600 × 4) − $800 = $1,600

To ensure your CLV is accurate, you need to keep your inputs consistent. Align the time period (usually a single fiscal year) across every calculation. For example, if purchase frequency is calculated from April to April, your churn rate should be, too.

While you can make a rough estimate of CLV through averages, the real value comes when you can tie each of these metrics to real business data, especially for less concrete metrics like cost to serve and customer lifespan. 

Tools like Agentforce Sales and CRM Analytics can bring together customer, sales and service data to track retention trends, surface insights, segment CLV by customer type, and identify which behaviours correlate with higher lifetime value. 

How CLV and customer acquisition cost (CAC) link together

CLV is most useful when you can compare it to customer acquisition cost (CAC) – how much it costs to win a new customer. 

A common benchmark for CLV and CAC is 3:1, meaning your average lifetime value is three times higher than the cost of acquiring a customer. This shows your growth is sustainable and that winning new customers is a beneficial strategy. 

Why is customer lifetime value important? 

Modern customers expect companies to adapt to their changing needs. To meet that expectation, you need to know not just who your customers are today, but also how their relationship with your business evolves over time. 

Customer lifetime value is how you track that evolution. It reveals the long-term health of each customer relationship and helps you identify your most valuable accounts, uncover upsell and cross-sell opportunities, and spot risks early to prevent churn. All of this lets you maximise revenue while 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 it’s much more cost-effective.

Factors that influence customer lifetime value (CLV)

Every customer experience will influence whether they stay, grow, or churn. As such, CLV isn’t a reflection of any singular touchpoint. It’s shaped by various factors, from customer satisfaction to the cost of customer acquisition. Here are some of the key drivers: 

• Customer satisfaction: As mentioned earlier, product and service quality are some of the strongest indicators of whether a customer will stick around. Poor onboarding or frustrating experiences can quickly weaken customer loyalty. Tracking satisfaction 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. If it’s too expensive to win, onboard, and support a customer, this will impact profitability even if revenue is strong. That’s why the full customer lifetime value formula considers both revenue and costs, so you can prioritise the relationships worth the investment. 

Ultimately, CLV improves when you can deliver frictionless personalised experiences that provide value while keeping costs manageable. 

How to increase customer lifetime value

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

Here are a few tactics to employ:

• Keep communication consistent and proactive: One of the biggest CLV risks 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 standardise 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 don’t rely on them alone. Incorporate other listening channels, such as reference programs, product advisory groups, and check-in calls, so you’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,  make sure to close the loop every time. For high-value accounts, that might mean a personal message from the rep or AE. For others, use automated follow-ups to acknowledge the input and share how it’s being acted on. Either way, the goal is the same: let customers know they’ve been heard, and make it clear their feedback is shaping the experience.

These tactics work particularly well when they’re within a defined system, with clear ownership, consistent processes, and shared visibility across sales and support. This will let you prioritise the right accounts and scale growth without introducing friction. 

Customer lifetime value (CLV) examples

CLV becomes a lot more useful when you move beyond the numbers and apply it to real customer behaviour. Two patterns recur 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 can tell you a lot more than raw spend. Teams can even use CLV to optimise account assignments and provide high-potential customers with more personalised 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.

Here are a few signals to 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.

The key to managing all of these signals is proactivity. Track your leading indicators and set clear thresholds for when an account needs attention. Then, when a customer is showing signs of dropping off, pinpoint exactly who should lead the intervention. 

Leveraging advanced predictive CLV models

Once you’ve calculated CLV, advanced methods can help you forecast value more accurately. These models factor in discounts, variable costs and predictive analytics signals like usage and engagement trends to capture not just revenue potential but the long-term profitability of each account.

A predictive CLV model can be especially useful if your sales cycle is long or renewal behaviour varies by segment. For example, if a customer starts with a $5,000 contract but similar accounts typically grow to $15,000 within two years, you can incorporate that projected growth into your CLV forecast, as long as your data supports it.

In practice, it helps to pair predictive analytics with a few leading indicators like:

• Customer profitability score: (Customer revenue − customer-specific costs) ÷ customer revenue. Shows whether a customer is profitable to retain and grow. 

• User adoption rate: Active users ÷ total eligible/licensed users (per period). Indicates the number of users actively engaging with your product over time. 

• Engagement score: Logins, event attendance, or interactions that signal ongoing interest.

• Product expansion: Growth in existing customer revenue (upsells/add-ons/seats) over time. Captures whether the value is increasing after purchase. 

These metrics will strengthen your CLV forecast by grounding it in what customers are actually doing, rather than just what they’ve spent. 

Some teams also apply a discount rate to account for time value and 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 customer retention. The right model depends on what you sell, the consistency of your customer behaviour, and the historical data available.

How to track CLV with technology and AI

Tracking and calculating customer lifetime value becomes less reactive and more strategic when you connect the right data in the right systems.

A customer relationship management (CRM) tool like Agentforce Sales provides a good foundation. Teams can use it to log activity, as well as 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 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 customise 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 behaviour. They can recommend the right time to reach out, flag gaps in adoption, or suggest relevant cross-sell opportunities without the guesswork.

AI is especially useful when you want to move from reporting CLV to predicting it. 

The right AI agent can pull engagement data, sales activity, and metrics from your CRM and then use the insights to estimate the future value of individual customers based on behaviour, engagement patterns and how likely they are to churn. All of this helps teams prioritise accounts before risks grow into revenue concerns.

Measure your customer lifetime value and drive your business up

Customer lifetime value is a shared lens into customer success for sales, service, marketing, and product. It not only offers insights into future revenue, but 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.

The true value of CLV is when you can move from calculating it to predicting it over time, and for that, you need the right AI solution that surfaces risk and opportunity early. Agentforce Sales can help you operationalise CLV by bringing together sales activity and customer signals to reveal where to focus and how to grow high-value relationships sustainably.

Try it for free today to get started.

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