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EXPERIENCE MANAGEMENT

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How to calculate customer lifetime value 13 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.

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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

organisations 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

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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.

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).

Learn more about improving customer loyalty

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

+

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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:

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

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)

+

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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 organisations, this

kind of calculation is perhaps too granular, but it can be useful in customer

service settings, and organisations such as telcos where customers sign

up to a long-term commitment.

For these organisations, 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. This formula is suitable for situations where the

figures are likely to remain relatively flat year-on-year.

Customer revenue per year * Duration of the relationship in years – Total

costs of acquiring and serving the customer = CLV

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.

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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.

Here’s a worked example of the customer lifetime value calculation using

the simple formula below:

Customer revenue per year * Duration of the relationship in years – Total

costs of acquiring and serving the customer = CLV

Here’s the example in practice:

The subsequent math:

£500 x 10 = £5,000

£5,000 – £550 = £4,450

Customer A’s revenue per year = £500+

Customer relationship duration = 10 years+

Cost of acquisition = £50+

Cost to serve = £50 per year (£500 over 10 years)+

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CLV for Customer A = £4,450

Calculating traditional CLV But what happens when your customer revenues don’t stay flat year on

year, and you need to factor in changes that happen across the customer

lifetime?

If this is the case, you need a formula that goes into a little more detail.

The traditional customer lifetime value formula fits the bill for many

businesses in this position.

Traditional CLV formula

GML * Retention rate / (1+ Rate of discount – Retention rate) = CLV

This calculation involves a few additional concepts:

GML – gross margin per customer lifespan

This is the profit you’d expect to make over the average customer lifespan

(i.e. the revenue minus your costs)

R – retention rate

The percentage of customers who stay with you over a set time period (as

opposed to those that churn during that time)

D – discount rate

A percentage to account for inflation. This is frequently set at 10%.

Let’s take a look at this customer lifetime value calculation in action…

So:

1 + 0.70 – 0.1 = 0.4

Your company’s GML = £2,200+

Your customer retention rate = 70%+

Discount rate of 10%+

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We divide the retention rate of .70 by 0.4 to get 1.75

We can then multiply that by our GML of £2,200.

CLV = 0.44 x 2200

CLV = £3850

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 are some of the ways you can use CLV in your organisation:

Optimise 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, by 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

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could see a correlation between customers that have engaged with a

particular touchpoint such as buying through a specific channel, or a 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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