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How to calculate customer lifetime value

11 min read
Customer lifetime value (CLV) is an essential metric for almost any customer experience (CX) program. It helps you to understand how profitable (or not!) a particular customer or customer segment is over their entire relationship with your brand. Find out how to calculate CLV and use the customer lifetime value formula alongside your other metrics to identify ways to increase revenue.


We all know the old adage — it costs less to retain an existing customer than it does to retain a new one. While there’s nothing wrong with that, it’s littered with nuance — what about the existing customers that cost you more to serve than they contribute to revenue? Which customers or segments are worth investing more in?

CLV is how you answer those questions. It shows how much a customer is worth to you over the lifetime of their relationship with the company and it’s a useful CX metric because it’s directly tied to the bottom line.

Compare that to Net Promoter Score (NPS) or Customer Satisfaction (CSAT) — both often used to measure customer loyalty. While CLV measures a tangible impact on revenue, NPS and CSAT measure a future promise of loyalty.

By calculating CLV, you’ll know how much it’s worth investing in the customer experience in order to see a positive ROI. It’s also useful in building customer loyalty prediction models, particularly in organizations that have a multi-year relationship with their customers, as a drop in CLV can be an early sign of attrition.

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What you’ll need to calculate customer lifetime value

CLV can be calculated at a company level (i.e. the average CLV across all your customers), a customer segment level (the CLV of distinct groups within your customer base) or an individual level (the CLV of each individual customer you deal with).

To start off simply, let’s begin with a company-wide CLV. But before you rush headlong into the formula for CLV, you’ll need a few pieces of data to hand.

  • Average purchase value — the value of all customer purchases over a particular timeframe (a year is usually easiest), divided by the number of purchases in that period
  • Average purchase frequency — divide the number of purchases in that same time period by the number of individual customers who made a transaction over the same period
  • Customer value — the average purchase frequency multiplied by the average purchase value
  • Average customer lifespan — the average length of time a customer continues buying from you

Calculating CLV: The Magic Formula

Okay, now you’ve got the foundations, calculating CLV is easy!

CLV = customer value X average customer lifespan

The resulting CLV is a monetary value (depending on the currency you work in) and shows how much you can reasonably expect the average customer to spend with you over their lifetime.

It’s a great frame of reference for everything from the investments you make into improving the customer experience (i.e. will the investment deliver ROI by generating more revenue over a customer’s lifetime); to optimising your customer acquisition strategy (i.e. comparing the amount you invest to ‘win’ a customer vs the amount you can expect them to spend with you).

Not all customers are the same – calculating CLV by customer segment

Understanding average CLV is a great first step, but in reality, CLV will differ by different customer segments. It’s most likely that one or two segments will have a much higher CLV than others, whether because they spend more per transaction or because they stay with you for longer.

Understanding your CLV by different segments is useful because it helps you to:

  • Identify what’s driving higher CLV (i.e. making those higher value customers worth more to you)
  • Spot opportunities to make less valuable customers more valuable (i.e. identify actions that will make increase CLV with segments who currently spend less over their lifetime)

To calculate CLV by customer segment, first you’ll need a breakdown of your different customer segments. Segmentation is where you break your customers up into distinct groups based on their behaviour or demographics to identify commonalities between individual customers.

We’d suggest using dynamic customer segmentation that builds your segments based on real-life behaviour rather than broad demographics. This way, you have a true picture of what a group of customers does in the real world (e.g. what they spend, how often, and what they spend it on) rather than what they say they’ll do.

Once you have your segments, follow the same formula for customer segmentation above, but this time only inputting the data for each segment. So you’ll need to filter average purchase value, average purchase frequency, and average customer lifespan by that segment.

It’s easiest to do if you have a single system of record for all your customer interactions, such as the XM Directory. The data in the directory feeds directly into your CX program so you can automatically see a real-time breakdown of your key CX metrics, including CLV, by customer segment.

If you don’t have a system of record yet, don’t worry — you can still calculate CLV using the formula above, and pulling data from various systems like your CRM, sales records etc.

Calculating the CLV of individual customers

In the same way as you can calculate CLV by different customer segments, you can calculate it on an individual basis. For many organizations, this kind of calculation is perhaps too granular, but it can be useful in customer service settings, and organizations such as telcos where customers sign up to a long-term commitment.

For these organizations, knowing an individual’s CLV is useful when identifying how far you’re willing to go to prevent customer churn. For example, say a customer calls to cancel their contract — if an agent has a breakdown of that customer’s lifetime value, they can make quick decisions about what they’re willing to do to save them.

For customers with a CLV over a certain threshold, you may be willing to invest more such as offering a discount, or adding in other products and services, whereas for those with a lower CLV, it may make financial sense to accept the churn and move on as it will cost you more to keep them than you can reasonably expect them to spend.

The formula for calculating CLV at an individual level is the same, although slightly easier to calculate – you simply multiply how much that customer spends each year (so no averages for purchase frequency or spend required) multiplied by the number of years you can reasonably expect them to stay with you.

Once again, a single system of record across the organisation is essential here. With the XM Directory agents can see, for each customer, a historic record of all their interactions with the company. So when they get in touch to cancel, they have all the information to hand to take the action that’s right for the business and the customer.

Calculating predictive CLV

So far we’ve talked about calculating CLV in terms of customers’ historical behaviour — i.e. what they spent, and how often, in the past — and extrapolated that to make a prediction about the future.

Advances in analytics technologies today have introduced more accessible predictive models for CLV that factor in each individual customer’s propensity to churn to make a more accurate prediction about future CLV.

If you have the right data for each customer in your database, you can start to get more granular, factoring in churn predictions into your CLV calculations.

The formula for predictive CLV is the same — so customer value multiplied by expected lifespan — however, your expected lifespan is much more accurate as a result of the churn modelling the system is able to do.

That’s not to say that using historical data to calculate CLV is wrong — far from it in fact — but instead that with predictive analytics, you can reduce the margin of error and get a more accurate figure for CLV.

How to use your CLV calculations

Once you know your CLV – whether that’s an average or broken down by segments – there are plenty of ways to use it to not only track how your investments in the customer experience are paying off, but also identify new opportunities to design experiences that deliver back to the bottom line.

Here’s a some of the ways you can use CLV in your organisation:

Optimize your marketing spend — by knowing which customers are most valuable to you, you can prioritise your customer acquisition strategies, making sure you spend budget in the areas that will attract the right customers

Reduce churn and drive loyalty — as we explored earlier, providing CLV data to customer-facing teams, they’re able to make better decisions about which customers to invest in when it comes to preventing churn. Similarly, you can use CLV to identify high value customers you want to nurture and reward way before they get to the point of contacting customer support or threatening to cancel.

Identify costly experience gaps — with CLV data feeding into your customer experience program, you can see which touchpoints in the journey have the biggest impact on the bottom line. It’s a great filter to use as you prioritise which improvements to make, as you’ll know which touchpoints and experiences are negatively impacting how much customers spend with you. With those insights in hand, you can then get to the root cause and take action to close those gaps.

Design new experiences that grow the business — CLV not only helps you identify broken experiences that are negatively impacting the bottom line, but also the breakthrough ones that are having a positive impact. You could see a correlation between customers that have engaged with a particular touchpoint such as buying through a specific channel, or s new product or service proposition and higher CLV. This can be the trigger to dig deeper and identify why so you can scale it across other channels, segments, products and services and drive even greater increases in CLV.

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