Customer Lifetime Value (CLV) – an Essential Business Metric

When we are trying to optimize the experience for our customers, there are many metrics we can track and aim to improve.

The Customer Lifetime Value (CLV) shows us how much money a customer will bring to the business on average over the entire time they remain a paying client.

Whether we decide to refer to the metric as CLTV, LTV, or the most popular CLV, it helps us calculate the overall value a customer has to the business, showing us their worth to the company.

Knowing this metric is instrumental in planning how much we can invest in customer acquisition and retention. If one customer has a high CLV, there’s a higher probability they are fans of our brand and will continue to buy our products and services in the future. On the other hand, a low CLV customer indicates it was a passive one-time purchase and will be much harder to re-engage with our company.

Whatever our CLV is, it’s paramount to keep it higher than our Customer Acquisition Costs (CAC) if we want to remain profitable over time.

CLV looks at customer value not on a purchase-by-purchase basis but for the entire relationship with our company.

If we ensure we understand and track our CLV, this will help us build better strategies for acquiring new customers and retaining old ones.

CLV and Customer Retention

Any commerce business should consider the Customer Lifetime Value as one of its key metrics.

Managing CLV opens the opportunity to access additional revenue. When we analyze CLV, we often do it to justify investing more into customer acquisition. However, this may be the wrong side of the equation. Instead, we should focus on customer retention.

If we have 5% of our customers return for a second purchase, this already gives us at least a 5% increase in sales, and this is if they keep the same average purchase value. However, repeat customers are more likely to place a larger order and come back a 3rd and 4th time. This will result in an even more significant boost to our company’s revenue.

Following this reasoning, let’s assume a 5% customer retention improvement will result in a 10% increase in revenues. Then if we invest resources in improving our retention rate by 10%, we would benefit from a 20% increase in revenue from repeat customers alone.

The probability of selling to existing customers is much higher than that of converting new prospects. Adopting a CLV-focused strategy can help us emphasize customer retention and drive stable revenue growth within our business.

If we follow the Pareto principle, 20% of our customers account for 80% of our revenue. We need to pay enough attention to those customers and improve their loyalty by constantly striving to maximize the perceived value of our products and services.

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Customer Lifetime Value Analysis

To understand what drives CLV, we need to look closer into the customer experience with our business and track feedback at all significant customer journey steps with our brand.

The technique works very well with subscription-based operations where we can track clients over more extended periods. We can better understand and plan attrition and churn by noticing early signs. Some of those can be:

  • Decrease in usage-based charges;
  • Fewer visits to the online solutions we provide;
  • Less interaction with our customer support.

CLV and Cost to Serve

When we discuss CLV and Customer Acquisition Costs (CAC), we also need to consider the Cost to Serve. It’s part of the cost of doing business and represents all the overheads we incur to serve our clients continuously.

Combining CAC and Cost to Serve, we can analyze revenue on a more granular level. We can compare high-CLV customers to low-CLV ones and look into various questions:

  • Do they cost the same to acquire and to serve?
  • Are there any customers for whom the Cost to Serve will exceed the CLV over time?

Remember that CAC is a one-off cost to acquire a customer, but the Cost to Serve is an ongoing cost over the client’s lifecycle, and it can fluctuate over time. For example, if a client contacts our support team more often, this means a higher Cost to Serve.

Understanding those numbers is vital to get an authentic feel of how customer spend and loyalty are adding to our business’s bottom line.

When we combine CLV and CAC, our business can estimate how long it takes to cover the investment we need to acquire a new customer. We must identify and take good care of our most valuable customers. This will ensure higher profit margins, better Customer Lifetime Value, and lower Customer Acquisition Costs.

We have to look at high-value and low-value customers with their CACs in mind. One can even argue that low-value customers are more important for the business as they are way cheaper to acquire than high-value ones (or at least they should be).

The Customer Lifetime Value is a metric we often leave out when we prepare financial models and budgets. We should make it an output of our models that update dynamically and use it as part of our scenarios functionality.

Customer Lifetime Value Calculation

If we rent the same holiday home at the seaside for one week each summer for ten years and pay €300 for it, our CLV for the owners will be €3,000.

However, in modern companies with complex products and services, Customer Lifetime Value becomes immensely harder to calculate. We need to have appropriately integrated data within our company to be able to calculate CLV correctly.

Steps to calculate

  1. Identify the customer experience steps where value is generated;
  2. Define the customer journey and its key steps;
  3. Measure revenue at each essential step;
  4. Add up revenue over the lifetime of the client.

There’s also a more straightforward formula to calculate CLV, which is suitable for companies with relatively constant revenue per year.

Customer Lifetime Value (CLV) Formula

Where:

  • ACR is the Annual Customer Revenue;
  • n is the duration of the lifetime in years;
  • CAC is the Customer Acquisition Costs;
  • CS is the Cost to Serve.

It is essential to understand that this formula relies on historical data to calculate the metric, so it only works well when the business is showing a somewhat constant revenue year over year.

If we take it a step further, we can employ predictive statistical methods to build forward-looking CLV models. If revenue is not flat year on year, we need to factor in the changes over the customer life cycle.

The traditional Customer Lifetime Value applies in most of those cases:

Customer Lifetime Value (CLV) Formula

Where:

  • GML is the gross margin over the average life cycle of a client (revenue – costs);
  • RR is the retention rate – % of customers that stay with the business over some time (as opposed to those who churn);
  • d is the average discount rate to factor in inflation (usually at 10%).

There’s another approach to calculate Customer Lifetime Value. We start by calculating the Average Purchase Price:

Average Purchase Price Formula

The next step is to calculate the Average Purchase Frequency Rate:

Average Purchase Frequency Rate Formula

Then we can utilize the two to calculate the Customer Value:

Customer Value Formula

We also need the Average Customer Lifespan in years, which we can calculate as follows:

Average Customer Lifespan Formula

Once we have those, we can calculate the Customer Lifetime Value:

Customer Lifetime Value (CLV) Formula

Improving Customer Lifetime Value

A positive ongoing relationship with our clients is the way to increase CLV. We need to actively engage with our customer base and work on those relationships.

The business must invest in customer experience. We have to start monitoring client interactions and introduce lasting changes that will improve how customers feel.

Another thing is to start a loyalty program. That way, we can incentivize repeat orders by giving discounts and various other benefits.

We can significantly improve customer relationships if we seek out unhappy customers proactively to discuss their issues and avoid escalations.

CLV helps us to identify specific customers that contribute the most revenue to our business. When we optimize CLV, we usually give more value to our clients, which increases retention and loyalty.

Example

To better illustrate the Customer Lifetime Value concept, let’s look at an example calculation.

We start by taking the sum of our client acquisition campaign costs, at €125 thousand. We signed 85 new clients by this marketing campaign, which averages about €1,471 in acquisition costs per client. Some of those clients we may have gotten by a single social media commercial, while some of them might’ve required much involvement from our sales team. For CLV, we are looking at the entire cohort.

The next step is to estimate the annual revenue we can expect from each customer over the next five years. The 85 customers have all signed on for an entire year, with a monthly charge of €100. We can safely assume a €1,200 customer revenue for the first year. From there on, we can apply an expected growth, which will come from add-ons and upsells. We can derive this percentage by looking at historical data for the company.

We also apply our Gross Profit Margin for the company as a whole. This is what’s left for the business after the Cost to Serve is deducted. Keep in mind that we would deduct the Cost of Goods Sold/Cost of Sales instead if this were a business selling physical goods.

Now that we have the forecast for Gross Profit per customer in the cohort, we will once again look at historical data to calculate customer retention. We calculate the retention rate as the portion of customers with the business at the end of the period from the total number of customers at the beginning of the period. In contrast, we usually exclude newly signed customers during the period. Another way to calculate retention is to subtract your churn rate from one.

We add the retention rate per annum and calculate the cumulative rate, showing us that we would expect to lose about 75% of the 85 customers currently in the cohort by year five.

Using this rate, we can reflect the probability of churning in our profit calculation.

The last aspect to consider is the time value of money. The €456 we expect to make per customer in year five doesn’t cost the same amount today. Therefore, we add a discounting factor. In most general cases, this is around 10%.

We then use the discounting factor to calculate the Net Present Value (NPV) of the annual profits. Accumulating these gives us the average Customer Lifetime Value of the business.

Don’t also forget to subtract the initial Customer Acquisition Cost we incur to onboard each client.

We see that it will take us on average two years to cover the initial investment in a customer, but at the end of their journey with the company, we expect a profit of €850 per client.

Now that we have the CLV for this cohort, we can make more informed decisions on how much to spend on converting new prospects into paying customers.

Conclusion

The Customer Lifetime Value is a reflection of the core health of any customer-focused company. It’s crucial to focus on improving loyalty and customer churn rate to increase the Customer Lifetime Value for the business.

Doing so will improve the overall profitability of the company and will support the growth of the business.

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Dobromir Dikov

FCCA, FMVA

Hi! I am a finance professional with 10+ years of experience in audit, controlling, reporting, financial analysis and modeling. I am excited to delve deep into specifics of various industries, where I can identify the best solutions for clients I work with.

In my spare time, I am into skiing, hiking and running. I am also active on Instagram and YouTube, where I try different ways to express my creative side.

The information and views set out in this publication are those of the author(s) and do not necessarily reflect the official opinion of Magnimetrics. Neither Magnimetrics nor any person acting on their behalf may be held responsible for the use which may be made of the information contained herein. The information in this article is for educational purposes only and should not be treated as professional advice. Magnimetrics and the author of this publication accept no responsibility for any damages or losses sustained in the result of using the information presented in the publication.

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