Unlocking the key to sustainable business growth lies in understanding customer relationships by calculating customer lifetime value (CLV). The different versions of the customer lifetime value formula underscore the notion that it’s not just about how much a customer spends in a single transaction – it’s about their overall engagement value.
These calculations can get intricate due to product variations, costs, purchase patterns, and volumes. However, with the right tools, unraveling CLV can be just a few clicks away.
Across various industries, the leading five companies in the market typically maintain an average customer retention rate of 94%. So mastering CLV becomes more than just a strategy – it becomes crucial for ensuring business success.
In this blog post, we’ll show you three straightforward models for calculating CLV, along with their limitations. We’ve also included some common mistakes to avoid when calculating CLV. Finally, you will find the best practices for aligning customer lifetime value with heightened retention rates.
Customer lifetime value is a fundamental metric in business and marketing. It refers to the predicted net profit a company can expect to earn from a customer throughout their entire relationship with the business.
⚠️ A common error that occurs when calculating CLV is confusing profit and revenue. Please check the “Common mistakes to avoid when calculating CLV” section below for additional typical mistakes that businesses encounter.
In certain references, the overall earnings garnered from a customer are employed for calculating CLV. Hence, when examining CLV figures, it becomes essential to discern whether they signify the entire revenue or the profit generated. These values align only under circumstances of notably high profit margins.
Calculating CLV enables businesses to make more informed decisions about resource allocation, marketing strategies, and customer retention efforts.
CLV and LTV (simply lifetime value) represent the financial essence of a customer’s value to a company over their entire journey. These terms are sometimes used interchangeably; it’s often a matter of company preference.
However, in some instances, there’s a shade of distinction between CLV and LTV. Some companies take a nuanced approach where LTV calculation signifies the average value a customer contributes across the entire customer base, while CLV zooms in on the lifetime value of an individual account.
CLV highlights the significance of building and nurturing customer relationships over time. Businesses that understand and use CLV are better equipped to tailor their strategies to cater to high-value customers, optimize customer acquisition costs, and reduce churn rates.
Here is a brief overview of the benefits of mastering CLV calculation.
Businesses often use customer lifetime value formula variations to tailor their calculations to the unique dynamics of their industry and customer interactions.
Here are three models commonly used to calculate CLV.
The traditional model delves into the intricate dynamics influencing a customer’s contribution to a company’s success. It considers margins, inflation, and retention rate and offers a perspective of how CLV evolves over time.
This is the traditional customer lifetime value formula:
To illustrate, if we take a scenario where the gross margin is 0.29 (equivalent to 29%) and the average total revenue per customer amounts to $1,000, the GML calculation will look like this:
GML = Gross margin × Average total revenue = 0.29 × 1,000 = $290
For instance, picture a month with 300 customers at its end, 50 newly acquired, and 270 customers at the beginning.
You can determine the retention rate (R) using this formula:
R = (Customers at the end – Customers acquired) / Customers at the beginning × 100
= (300 – 50) / 270 × 100
= 0.9 × 100
The discount rate accommodates inflation and future value adjustments. A frequently used rate in software as a service (SaaS) businesses is 10%.
Finally, let’s piece everything together to find the CLV. Just insert the values into the traditional customer lifetime value formula:
CLV = $290 × [0.9 / (1 + 0.1 – 0.9)]
= $290 × (0.9 / 0.2)
= $290 × 4.5
In this example, the expected value of a customer’s contribution over their lifetime is estimated to be $1,305.
Business expansion isn’t a straight path, though. This model assumes that customer behavior will remain consistent over time, neglecting potential shifts in preferences, market trends, or changes in the company’s offerings.
The predictive model forecasts CLV by analyzing the purchasing behaviors of both new and existing customers. This model stands out in its ability to identify the most valuable customers. It also helps recognize products or services that contribute to higher profits.
Here is the predictive customer lifetime value formula:
Imagine a cutting-edge company providing innovative cloud-based tools to enhance business productivity.
Over a span of 10 months, this SaaS company documented a cumulative total of 500 transactions.
T = 500 / 10 = 50
Delving into a specific time frame, let’s see the month of March. Within this time window, the company accomplished a remarkable feat by generating a revenue of $20,000 through the execution of 40 orders.
AOV = $20,000 / 40 = $500
To calculate the gross margin percentage for each quarter, the company applies the following formula:
Gross margin = [(Total revenue – Cost of goods sold) / Total revenue] x 100
In March, the total revenue was $20,000.
Cost of goods sold = $12,000
Gross margin (%) = [($20,000 – $12,000) / $20,000] x 100 = 40%
Let’s determine the AGM over the course of 10 months. The cumulative gross margin aggregates to 40%, equivalent to 0.4.
The ALT value emerges through the reciprocal of the churn rate. To compute the churn rate, let’s delve into the following scenario.
At the beginning of January, the SaaS company initiated with a clientele of 600. By the close of October, the customer count had diminished to 450. The churn rate computation unfolds as follows:
Churn rate = (Customers at the beginning – Customers at the end) / Customers at the beginning
=(600 – 450) / 600 = 150 / 600
= 0.25, equivalent to 25%
ALT = 1 / Churn rate
= 1 / 0.25 = 4 months
Let us then compute the predictive CLV using the below values:
Predicted CLV = (50 x $500 x 0.4 x 4) / 450
= $40,000 / 450
To wrap up, the company envisions an anticipated CLV of around $88.88.
This model relies heavily on precise data and assumptions, leaving room for errors if predictions miss the mark. It might not effectively address sudden changes in customer behavior.
Our CLV calculator simplifies CLV calculation by factoring in average revenue per account (ARPA), monthly recurring revenue (MRR), and churn rate.
Here’s a step-by-step example of how to calculate CLV using the simplified customer lifetime value formula.
ARPU (Average revenue per user) = MRR / Number of customers
= $20,000 / 500
CLV = ARPU x GM / Churn
= $40 x 0.35 / 0.02 = $700
The CLV in this scenario amounts to $700.
While our CLV calculator provides valuable insights, remember that it relies on the accuracy of the input data. Variability in these numbers can impact the precision of the calculated CLV.
We have listed below some of the mistakes you should watch out for before starting your CLV calculations.
Mistaking revenue for profits when calculating CLV can significantly inflate customer value, leading to the misconception that you can invest more to acquire customers than is financially viable.
Imagine acquiring a new customer for $10, and throughout their customer lifespan, they make three $5 purchases. It might be tempting to calculate CLV like this:
CLV = 3 purchases x $5 = $15
CAC (Customer acquisition cost) = $10
Hence… CLV > CAC, right?
Actually, CLV should always reflect profit, not revenue. Suppose your profit margin is 50%. In reality, the CLV is only $7.50, meaning you’re actually losing money on each new customer you gain, so CLV < CAC.
⚠️ The confusion arises because not all costs are integrated into CAC. CAC accounts solely for expenses related to acquiring new users, not other costs impacting the profitability of your products or services.
While understanding overall CLV is valuable, segmentation adds insight. By grouping customers based on demographics or behavior, you can calculate separate CLVs for each segment.
For instance, you initially think your CLV is $50, so you’d pay up to $50 for new users. Yet, after segmenting, you find organic users have a CLV of $70 while ad-acquired users have a CLV of $20.
There are two primary approaches when calculating future profits: using NPV (net present value) or relying on nominal predictions.
The NPV method is used only in the traditional model of calculating CLV. It considers the declining value of money over time, incorporating a discount rate. While this approach provides a more accurate projection, it’s also more complex.
Many organizations calculate CLV using nominal, non-discounted figures. These predictions tend to lean toward the optimistic side and become increasingly inflated the further into the future they extend.
Let’s consider a software company planning to assess the long-term value of its customers.
Discover the most important practices for elevating customer lifetime value.
CLV is about more than just numbers; it reflects the relationships you’ve nurtured and the value you’ve provided. It’s about turning customers into advocates, brand enthusiasts, and long-term partners. You’re setting the stage for a thriving future as you use CLV to fine-tune your marketing strategies, tailor your offerings, and enhance customer experiences.
Take the next step in optimizing your strategies by harnessing the insights provided by our CLV calculator. This tool equips you with valuable data on CLV to help you make more informed decisions for your business.
Touchpoint, our advanced customer service platform, effectively enhances businesses’ capabilities to provide support and boost customer retention and CLV. By embracing an omnichannel approach powered by artificial intelligence, Touchpoint simplifies customer service for a seamless user experience.
Iustina is a curious copywriter with an analytical mind and a demonstrated history in the advertising industry. She likes mixing words with data and is passionate about human behavior. When she's not at her work desk, you'll probably find her enjoying refreshing tea flavors, spilling stories on paper, or binge-watching true-crime documentaries.
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