Calculating Customer Lifetime Value (LTV) for Your eCommerce Brand

by Bryan Teo

Calculating Customer Lifetime Value (LTV) for Your eCommerce Brand

What is Customer Lifetime Value (LTV)?

Customer lifetime value (LTV) is the total value of a customer to your business over the entire duration of your relationship. Rather than focusing on individual transactions, LTV encompasses all past and potential transactions within the customer's relationship span with the business. This helps calculate the specific revenue from that customer.

Mastering the calculation of customer lifetime value and applying the LTV formula is essential for eCommerce businesses seeking to maximize profitability and drive growth.

Calculating LTV for subscription brands

Firstly, what are subscription brands?

A subscription brand or business model is a recurring revenue model in which customers pay a weekly, monthly, or yearly fee in exchange for your products or services. Customers can renew their subscriptions after a certain period of time. This allows you to leverage your customer relationships to create a steady stream of income.

Before you rush into the formula for LTV, these are a few key pieces of data you need to understand and have:

  • Revenue Per Customer/Purchase value: This involves assessing the total income generated from a customer over the entire relationship period, including all transactions. For example, if a customer spends $10 every month for a year, their annual revenue contribution would be $120. This figure is crucial for understanding the direct financial impact of each customer on your business.
  • Purchase frequency: This is the number of times a customer makes a purchase from your brand over the course of your relationship. This is typically broken down into specific time periods, specifically annually.
  • Costs: These include all expenses directly or indirectly related to providing value to the customer, and largely fall under cost of acquisition and cost of retention. This can encompass marketing expenses, cost of goods sold, customer service costs, and any other operational costs associated with maintaining the customer relationship. Accurately tracking these costs helps businesses understand the profitability of each customer segment.
  • Customer lifespan: This is the projected time span (this is specific to subscription brands) over which a customer engages with a company and buys its products or uses its services. For instance, if a customer typically remains active with your brand for three years, this duration is factored into the LTV calculation. The longer the customer lifetime duration, the higher the potential LTV.

Historic Customer Lifetime Value

This is a retrospective measure that looks back at past transactions to determine a customer's value and makes it useful for understanding the customer’s contributions. From there, the numbers can be extrapolated to make a prediction about the future.

For example, if a customer bought a $100 gift basket annually for the past 5 years, their historic customer lifetime value is $500.

Another example with a projected lifespan, a customer has a gift basket subscription that sends out a gift basket annually. For that business, they calculate that their average subscription period is 6.4 years. As such, the customer lifetime value is thus $640.

This straightforward measure helps understand what an existing customer has contributed to your brand and build profiles of ideal customers. However, it is less useful for predicting future revenue when considered alone, as it relies solely on historical data without accounting for future potential.

Once you have the data above, calculating customer LTV is easy. Simply multiply customer value by the average expected customer lifespan.

The resulting lifetime value is a monetary figure (based on your operating currency) that indicates the expected total expenditure of an average customer throughout their relationship with your business.

This metric serves as a valuable reference point for various business decisions. This includes evaluating investments in customer experience to determine if they will generate a return on investment (ROI) by increasing their lifetime value. It also helps in optimizing your customer acquisition strategy by comparing the costs of acquiring a new customer with their anticipated lifetime spending.

Predictive Customer Lifetime Value

Predictive lifetime value uses algorithms to forecast future behaviors and revenues based on historical data, catering to businesses focused on long-term strategic planning.

Predictive customer lifetime value uses historical data to forecast the future value of a customer. This approach considers various factors such as customer acquisition costs, average purchase frequency, and business overheads to provide a realistic prediction of future revenue. It leverages algorithms and machine learning to analyze past behaviors and make educated guesses about future behaviors.

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

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

Predictive customer lifetime value calculations are used by subscription brands that want to get accurate foresight into their customers. This gives businesses the ability to make changes right now to help improve these forecasted numbers. And that acts as a clear indicator of whether their business model has been successful.

The formula for predictive LTV is the same—(purchase value * purchase frequency) multiplied by expected average customer lifespan. However, your expected lifespan is much more accurate as a result of the churn modeling the system is able to do.

That’s not to say that using historical data to calculate LTV is wrong but with predictive analytics, you can reduce the margin of error and get a more accurate figure for LTV.

Here’s a worked example of the customer lifetime value calculation using the simple formula below:

Calculating LTV for non-subscription brands

Cumulative Customer Lifetime Value

Cumulative LTV provides a purely factual perspective by summing actual transactions, making it versatile (and applicable) for both subscription and non-subscription models.

Cumulative LTV focuses purely on transactions that have already occurred, without making projections. For example, if a customer spent $100 two months ago, $80 last month, and $120 today, their cumulative LTV is $300.

LTV at various levels for subscription brands

It is important to understand the flexibility of customer lifetime value calculations. LTV can be calculated for a large macro-level audience, to a single granular individual. This all depends on what your goals are when using these calculations.

By segment

In practice, LTV varies across different customer segments. That’s just how it works. Typically, 1 or 2 segments will have a significantly higher LTV than others. This variation can be attributed to factors such as higher spending per transaction or longer customer retention periods, among other reasons. Vice versa, 1 or 2 segments could also have significantly lower LTV.

Analyzing LTV by different segments is beneficial because it enables you to:

  • Identify the factors driving higher LTV (i.e. what makes these high-value customers more profitable)
  • Recognize opportunities to increase the LTV of less valuable segments (i.e. pinpoint actions that can boost the LTV of segments with currently lower spending over their lifetime)

To determine LTV by customer segment, you would first need to segment your customer base. Segmentation involves categorizing your customers into distinct groups based on their behavior or demographics, allowing you to identify commonalities among individual customers.

At this level, the data we mentioned above you use here has to be an average so as to represent the entire segment.

  • Average revenue per customer/Average purchase value
  • Average purchase frequency
  • Costs
  • Average customer lifespan

By individual customer

In the same way that you can calculate LTV by different customer segments, you can calculate it on an individual basis. For many businesses, this kind of calculation may perhaps be too granular. However, it is not without its use cases.

It can be useful in customer service, client calls, and other settings. For some businesses, having knowledge of that individual customer’s LTV could be useful when identifying how far you’re willing to go to prevent customer churn. For example, suppose you’re in a client success call with a client who raised the idea of churning—if your team has a breakdown of that customer’s lifetime value, you can make quick decisions about what you’re willing to do to save them.

For customers with an LTV over a certain bar, you may be willing to invest more such as offering a discount, or adding in other products and services. Conversely for those with a lower lifetime value, 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 LTV 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.

LTV at various levels for non-subscription brands

For non-subscription brands, lifetime value (LTV) calculations are also highly adaptable.

At Fairing, our LTV analytics tool assists businesses in gaining a deeper understanding of their marketing expenditures. By analyzing these numbers, businesses can clearly identify which channels have been effective and whether their investments have paid off. This allows you to refine your marketing strategies.

To provide a comprehensive understanding of customer lifetime value (LTV), we examine it at three distinct levels: overall cohort, by first acquisition, and by response.

Overall Cohort

This level offers a broad view of your entire marketing mix and their respective customer lifetime values. By analyzing the LTV of the overall cohort, you gain insights into the performance of each marketing channel. This bird's-eye perspective allows for easy comparisons and a comprehensive analysis of how different channels contribute to your revenue.

First Acquired

At this level, we focus on the LTV of customers from their first purchase. Since businesses are constantly seeking to acquire new customers, this perspective helps in understanding the effectiveness of various acquisition channels.

By filtering data to this level, you can evaluate the success rate of different channels in attracting new customers and assess whether the investment in these channels is justified. Comparing these figures against your customer acquisition costs provides a clearer picture of their efficiency.

Additionally, incorporating "How Did You Hear About Us" (HDYHAU) questions in post-purchase surveys can enrich this analysis.

By Response

For a more detailed analysis, we examine LTV by individual channels, monitoring their monthly performance. This granular approach enables you to track the customer lifetime value and average order value specific to each channel.

By staying vigilant about the performance of your marketing channels, you can remain agile and responsive to changes, ensuring your marketing strategy is always aligned with business goals. This detailed insight helps in fine-tuning your marketing efforts and maximizing the returns from each channel.

How to use your customer lifetime value calculations

Once you know your LTV—whether of your overall cohort, 1st acquired, or by response—there are plenty of ways to use it. Not only can you track how your investments in 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 LTV in your organization:

Optimize your marketing spend

By knowing which customers are most valuable to you, you can prioritize your customer acquisition strategies, making sure you spend budget in the areas that will attract the right customers

Reduce churn and drive customer loyalty

As we explored earlier, by providing LTV 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 LTV to identify high-value customers you want to nurture and reward before they get to the point of contacting customer support or threatening to cancel.

Identify costly experience gaps

With LTV data feeding into your customer experience program, you can see which touch points in the customer journey have the biggest impact on the bottom line. It’s a great filter to use as you prioritize which improvements to make, as you’ll know which touch points 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

LTV not only helps identify problematic experiences but also highlights successful ones that positively impact revenue. For instance, you might notice a correlation between customers interacting with a particular touch point, like purchasing through a specific channel or trying a new product, and having a higher LTV. This can prompt further investigation and scaling of successful strategies across other channels, segments, products, and services, driving even greater increases in LTV.

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 LTV.

Interested to learn more about Fairing or LTV? Book a demo here today.

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