How to calculate and analyze customer lifetime value

Customer lifetime value—also known as CLV, CLTV, or LTV—is a vital metric for any business. It tells you how much a customer is worth, guides customer retention efforts, and helps you build customer delight.

But simply calculating CLV isn’t enough to create actionable insights. After all, what does it really mean if your customer spends an average of $800 on your business over their lifetime? Without deeper analysis, that number lacks context. And, you might not know how to improve it.

To get the most value from CLV, teams need an effective customer lifetime value analysis to help them understand what factors drive long-term value, where customers drop off, and how to optimize the user journey.

This article shows you how to calculate and analyze CLV to keep customers around for the long haul.

Key insights

• CLV alone doesn’t give the full picture, but layering in behavioral insights does. When you know why customers stay—and spend—you can optimize every touchpoint to create a more enjoyable customer journey.

• Improving CLV isn’t just about customer retention. It’s about maximizing value at every stage so you can build deep customer loyalty

• Different CLV calculation methods serve different purposes. A simple CLV calculation offers quick insights but lacks depth. Whereas predictive CLV is highly accurate but requires advanced analytics. The best method is the one that fits your needs and available resources.

3 steps to conduct an effective CLV analysis and increase profitability

A thorough CLV analysis helps tech and product teams understand customer satisfaction by taking a deeper look into why customers stay, where they drop off, and how their behavior impacts retention and revenue. It also provides insight into whether customer acquisition costs (CAC) are justified. For example, you’d want to optimize CAC if it’s close to—or more than—CLV.

Here’s how to conduct, and get the most from, your CLV analysis:

Step 1: calculate your CLV

Understanding how to calculate customer lifetime value is essential for sustainable business growth. CLV establishes a baseline so you can measure each customer’s value over time, identify opportunities for improvement, and boost long-term customer relationships. 

The way you calculate customer lifetime value depends on the customer data you have available and your business needs. Each approach offers different levels of insight, from quick estimates to highly accurate forecasts.

Simple CLV 

Simple CLV is a straightforward customer lifetime value calculation method teams use to estimate the total revenue a business can expect from a typical customer over their lifetime. This method relies on historical averages and uses basic metrics that are fairly easy for businesses to obtain.

Here’s the formula for calculating a simple CLV:

CLV=(Average Purchase Value × Purchase Frequency) × Customer Lifespan

This simple calculation doesn’t require customer lifetime value software or advanced analytical skills, making it ideal for teams that need a quick calculation or teams that don’t have the resources for more in-depth calculations.

Gross margin CLV

Gross margin CLV builds on the simple CLV calculation by factoring in the cost of goods sold (COGS), giving businesses a clearer picture of profitability, rather than just revenue. 

Say you spend $200 to acquire a customer. Your simple CLV calculation tells you that your customers are worth $500, which justifies your acquisition cost. But, let’s say your gross margin CLV comes to $150. This means you’re spending more to obtain customers than what they’re worth.

Here’s the formula for calculating gross margin CLV:

Gross margin CLV=(Average Revenue per Customer per Period × Gross Margin) × Customer Lifespan per Period

For example, if a customer spends $50 per month and stays for 24 months, and the gross margin is 20%, the calculation would look like this:

(50 × .20) × 24 = $240

Predictive CLV 

Predictive CLV is an advanced approach to calculating customer lifetime value, using historical data, machine learning, and statistical models to forecast an individual customer’s future value.

Some analytics tools, like Google Analytics, Put It Forward, and Microsoft Dynamics 365 offer predictive analysis solutions. Or, you can work with a developer or data scientist to build a custom model.

Unlike simple or gross margin CLV, which both calculate the average of past data, predictive CLV assigns a unique value to each customer by analyzing spending or churn rate patterns. This level of analysis makes predictive CLV an accurate and dynamic approach.

For example, when predicting customer lifetime values in SaaS, teams might include customer behavioral data like

• Usage patterns, such as logins, active sessions, and feature adoption

• Subscription behavior, such as renewal rates, upsells, and downgrades

• Support & engagement, such as help desk tickets, Net Promoter Score (NPS), and survey responses

Together, these factors can give an estimate of each customer's future CLV.

Since this estimate updates as new data comes in, it gives teams forward-looking insights to anticipate churn, improve customer retention strategies, and personalize customer experiences.

Building a predictive CLV model requires machine learning algorithms to analyze patterns in customer behavior. This approach is ideal for businesses with large datasets, complex customer journeys, and teams with the expertise to build and interpret these models.

Here’s a concise table summarizing the differences between the 3 CLV calculation methods:

Step 2: gather behavioral data

Before you can analyze your CLV, you need a complete dataset of how users interact with your product. User interactions give you additional context behind your CLV and help you understand why certain customers stay and spend more compared to others.

Focus on making sure your data collection methods capture broad behavioral trends across all types of customers, including those who churn, those who return, and those who don't turn into customers at all.

To get a well-rounded view, teams need to collect both quantitative and qualitative behavioral data, which can then be used to compare high- and low-CLV segments.

Quantitative data

Quantitative data gives you measurable insights (think session duration and bounce rate) into how users move through your site.

Look to your web analytics tool to gather metrics like

• Pageviews to see which pages attract the most traffic

• Sources to see where users come from (like social media)

• Session duration to see how long users spend on each page

• Click-through rates to see how often users interact with calls to action (CTAs) 

• Conversion rates to see what percentage of users complete key actions

• Exit rate to see where users leave your site most frequently

Contentsquare’s Dashboard highlights essential site metrics so you can get a quick overview of user behavior on your site

Qualitative data

While quantitative data reveals what users do, qualitative uncovers why users behave that way.

By observing real user behavior, teams can uncover friction points, confusion, and intent—insights that aren't always obvious from numbers alone. 

Qualitative behavioral data can come from a number of methods, including 

• Session replays: recordings capture how people move through your site, including moments of hesitation, areas of friction, or interactions that lead to conversions

• Live chat logs and support requests: live chat conversations reveal recurring pain points and usability issues that help you spot potential barriers affecting CLV

Contentsquare’s Session Replay capability lets you watch users move through your site, so you can experience their frustrations and wins in real-time

Step 3: analyze and interpret the data

Now it's time to analyze your behavioral data to uncover patterns that influence customer lifetime value.

By breaking down CLV by different factors—such as demographics, products, and acquisition channels—you can see which customer segments are the most valuable and why. Analyzing how each segment behaves helps answer questions like

• What blockers prevent customers with a low-CLV from spending more? 

• Why aren’t certain groups turning into repeat buyers?

• Are there any journeys we can simplify to improve the user experience?

• What resources do people need to encourage brand loyalty?

• How can we identify and acquire more high-value new customers?

💡Pro tip: create custom user attribute values with Contentsquare to easily filter data to analyze specific segments, like customers with a lifetime value of $10,000 or customers with little to no lifetime value. That way, you can quickly spot customers with varying values and review their behavior for your analysis.  

Analyzing CLV by demographics

Understanding which demographics contribute to a higher CLV can help you understand who to target your marketing and messaging towards, so you can continue to attract high-value customers.

Demographic CLV analysis helps you answer questions like

• Are certain age groups more likely to become repeat buyers?

• Do customers from specific locations have higher retention rates?

• Does income level indicate higher or lower spend?

You can then use Contentsquare’s Journey Analysis capability to see how different demographics move through your site—and where certain people get stuck and drop off. 

💡Pro tip: Contentsquare’s Speed Analysis and Frustration Score can also help find friction points, such as slow page loads or other web performance metrics, that may be leading to high drop-off rates. That way, you can quickly see what to fix to make the journey more enjoyable.  

For example, you might notice that an age bracket with a lower CLV tends to have longer journeys than those with higher CLVs.

Running a survey lets you ask this demographic about their purchasing experience to understand whether they’re getting stuck and what you can improve to get them from point A to point B faster.

Contentsquare’s Surveys capability give you a direct line to customers so you can hear from them, in their own words, what they need from your brand

Once you know the friction points in the customer journey, you can A/B test changes to optimize the experience, better serve different demographics, and make purchasing easier. In the end, you’ll increase the chance of more people becoming repeat buyers with higher lifetime values.

Analyzing CLV by product or product category

Reviewing CLV based on products helps determine which products deliver the most long-term value—whether through repeat purchases, upgrades, or higher retention rates.

For example, you might think your most expensive product contributes the most to CLV, so you allocate the largest portion of your marketing budget to promote that product. However, through CLV analysis, you realize customers who purchase a lower-priced product tend to become repeat buyers and speed more over time. 

Analyzing CLV at the product level lets you

• Uncover high-value products to prioritize in your marketing efforts

• Identify underperforming products that you may need to improve or sunset

💡Pro tip: use Contentsquare’s Surveys capability to ask customers about their preferences and experiences with your products. By understanding what they like or dislike, you can fine-tune your product offerings and ensure each product is aligned with customer expectations.

Analyzing CLV by acquisition channel

By analyzing CLV by acquisition channel—such as social media, paid search, or referrals—you can identify which sources bring in your most valuable customers. That way, you can allocate your marketing resources accordingly.

Through acquisition analysis, you’ll answer questions like

• Which channels have the highest spend and retention rate?

• Which channels result in the highest churn?

• Do promotional campaign customers spend more or less than customers acquired organically?

To determine the cost-effectiveness of each channel as it relates to CLV, you’ll want to conduct a customer acquisition cost (CAC) analysis. You can then compare each channel’s CAC and CLV to ensure you're not overspending on channels that don’t generate enough long-term value.

Contentsquare’s Session Replay capability helps you explore how different acquisition channels interact with your product to help you uncover what behaviors may lead to greater spend. 

For example, by watching back sessions, you might notice referral customers move through your product with ease and tend to adopt features faster, whereas organic customers get stuck, frustrated, and churn. 

Use a churn survey to reach out to those who leave and ask them why they left. Then, use Contentsquare’s Interviews to directly speak with churned customers to get even deeper insight. With these understandings, you can make changes and improvements that make all your customers happy, no matter where they came from or how they found you.

Speak with users and get concrete answers for their actions using Contentsquare’s Interviews capability

Finally, use the insights from your demographic, product, and acquisition analysis to hypothesize product improvements (like better onboarding or feature refinements) to increase customer lifetime value across all segments. Backing up your proposed changes with behavioral data helps you secure stakeholder buy-in.

Using CLV analysis to improve the customer experience

A thorough CLV analysis doesn't just tell you how much a customer is worth, it helps you identify gaps in the customer experience that may be pushing others away. Understanding these insights is key to learning how to increase customer lifetime value across your entire customer base.

Addressing friction points helps you provide a better customer experience that naturally increases retention and loyalty. This strategic approach to increasing customer lifetime value creates a positive cycle that benefits both your customers and your bottom line.

FAQs about customer lifetime value analysis