The last thing a software marketer wants is for their hard-earned customers to leave before they’ve got an opportunity to earn their loyalty. You can maintain long-lasting relationships with your clients and increase client retention by measuring customer lifetime value (CLV). Indeed, CLV is ranked by 25% of marketers as one of their top five marketing metrics because it enables them to evaluate and improve the effectiveness of their outreach activities.

CLV can help you identify your most valuable customers so you can engage them with offers and incentives that are engaging to them. This is especially useful for software marketers who are focused on customer acquisition and lifecycle.

CLV provides a dependable method for identifying high-value clients and directing retention campaigns. This article explains client lifetime value and how using it can improve customer service.

What is Customer Lifetime Value (CLV)?

Customer lifetime value, or CLV, is the total amount of money a customer brings into your firm for the duration of that customer’s relationship. To put it simply, it’s a way to calculate how much a software buyer has spent—or is projected to spend—on your goods and services over the course of their relationship with you.

A buyer is more valuable to your company if their CLV is higher since they are more likely to be devoted and produce more income.

Customer lifetime value helps you understand the growth and revenue value of each customer over time. This metric is important to any business because it can help your business:

• Boost customer loyalty
• Reduce churn
• Improve strategic decision-making

For example, you can use customer lifetime value to find the customer segments that are most valuable to your company.

Here are some other reasons why understanding your CLV is essential.

1. Increasing CLV can increase revenue over time.

The longer the lifecycle or the more value a customer brings during that lifecycle, the more revenue a business earns. Therefore, tracking and improving CLV results in more revenue.

CLV helps you find the specific customers that contribute the most revenue to your business. You can use this information to segment your audience by the value those customers bring.

Once you find those customers, you can encourage repeat purchases and find specific cross-selling and upselling opportunities for different segments of your audience. Or you can tailor your products or marketing to your highest spenders to keep them coming back for more.

2. It can help you identify issues so you can boost customer loyalty and retention.

If CLV is a priority in your business, you can use it to identify impactful trends in your customer data. This insight can help you stay ahead of the competition with action items to address those changes. CLV helps you understand customer behavior, preferences, and spending patterns. With this analysis, you can improve your data-driven decision-making. This leads to more personalized marketing strategies for growth.

For example, say your CLV is low. You can work to optimize your customer support strategy or loyalty program to better meet the needs of your customers. Or you can optimize a new product to attract higher-value customers.

3. It helps you target your ideal customers.

Customer lifetime value tracking makes it easier to segment your customers. You can segment based on profitability, customer needs, preferences, or behavior. When you know the lifetime value of a customer, you also know how much money they spend with your business over some time — whether it’s $50, $500, or $5000.

Armed with that knowledge, you can develop a customer acquisition strategy that targets customers who will spend the most at your business. You can personalize marketing to attract and retain them, and effectively allocate resources to get the most value from your efforts.

4. Increasing CLV can help reduce customer acquisition costs.

Acquiring new customers can be costly, and it’s less expensive to retain a customer than it is to acquire a new one. Customer lifetime value can help reduce costs with a focus on retaining existing customers. If you can keep a customer happy long-term, then you can improve their value to the business.

Using CLV metrics can improve customer loyalty and word-of-mouth referrals — it can also reduce marketing and sales expenses.

5. CLV can simplify financial planning.

The financial health of a business is often a big concern for CEOs and business owners. Customer lifetime value helps you get a clear picture of your customer’s relationship with your business and products. It can offer insights into future revenue streams and changes in customer behavior.

This knowledge can help you make more accurate predictions about future cash flows. So, CLV helps you reliably forecast revenue and plan the financial future of your business.

6. CLV trends can show you how to improve your products and services.

Understanding CLV can give you a better understanding of the value customers get from specific products or services. With insights from your CLV, you’ll have a clear direction for further analysis. This may guide you to look at customer feedback and behavior, update pain points, or change your approach to product development.

Read Also: Website Speed Optimization and Its Impact on Analytics

Lifetime value data can help you find where to make key improvements that align with customer needs and boost satisfaction. This not only strengthens customer loyalty but also differentiates your company from competitors.

Understanding CLV allows you to make informed decisions based on how long a customer typically buys from you and what they spend over the lifetime of that relationship. This metric can help inform your strategy on acquisition, customer retention, customer support, and even the quality of your products and services.

Calculating customer lifetime value for different customer segments helps in a number of ways, mainly regarding business decision making. Knowing your CLV can tell you, among other things:

• How much you can spend to acquire new customers (CAC) and still have a profitable relationship
• The exact amount you can expect an average customer to spend over time
• What kinds of products high-value customers want
• Which products have the highest profitability
• Which customer relationships are driving the bulk of your sales
• Who your most profitable types of clients are
• Details about the customer journey and churn rates

Using your CLV as a base, you can work to better understand your most loyal customers. What do they like? Why do they continue to purchase from you?

Together, these types of decisions can significantly boost your business’s profitability.

As with any metric you track in business, knowing the number is not enough. You have to use your CLV to shape your overall business strategy.

• If your customer lifetime value is on the rise, that could mean you should continue to invest in product development or your customer success teams. 
• If your CLV is declining, that might tell you your latest marketing strategy could use a reboot. 

One of the main benefits of understanding CLV is that it can help you significantly reduce your customer acquisition costs over time.

Customer Lifetime Value Example

Using data from a Kissmetrics report, we can take Starbucks as an example for determining CLTV. Its report measures the weekly purchasing habits of five customers and then averages their total values together.

By following the steps listed above, we can use this information to calculate the average lifetime value of a Starbucks customer.

1. Calculate the average purchase value.

First, we need to measure the average purchase value. According to Kissmetrics, the average Starbucks customer spends about $5.90 each visit. We can calculate this by averaging the money spent by a customer in each visit during the week.

For example, if I went to Starbucks three times and spent nine dollars total, my average purchase value would be three dollars. Once we calculate the average purchase value for one customer, we can repeat the process for the other five.

After that, add each average together, and divide that value by the number of customers surveyed (five) to get the average purchase value.

2. Calculate the average purchase frequency rate.

The next step to calculating CLTV is to measure the average purchase frequency rate. In the case of Starbucks, we need to know how many visits the average customer makes to one of its locations within a week.

The average observed across the five customers in the report was found to be 4.2 visits. This makes our average purchase frequency rate 4.2.

3. Calculate the average customer’s value.

Now that we know what the average customer spends and how many times they visit in a week, we can determine their customer value.

To do this, we have to look at all five customers individually and then multiply their average purchase value by their average purchase frequency rate. This lets us know how much revenue the customer is worth to Starbucks within a week.

Once we repeat this calculation for all five customers, we average their values to get the average customer’s value of $24.30.

4. Calculate the average customer’s lifetime span.

While it’s not explicitly stated how Kissmetrics measured Starbucks’ average customer lifetime span, it does list this value as 20 years. If we were to calculate Starbucks’ average customer lifespan, we would have to look at the number of years each customer frequented Starbucks. Then we could average the values together to get 20 years.

If you don’t have 20 years to wait and verify that, one way to estimate customer lifespan is to divide 1 by your churn rate percentage.

5. Calculate your customer’s lifetime value.

Once we have determined the average customer value and the average customer lifespan, we can use this data to calculate CLTV. In this case, we first need to multiply the average customer value by 52. Since we measured customers on their weekly habits, we need to multiply their customer value by 52 to reflect an annual average.

After that, multiply this number by the customer lifespan value (20) to get CLTV. For Starbucks customers, that value turns out to be $25,272 (52 x 24.30 x 20= 25,272).

What is the Concept of Customer Lifetime Value CLV and Its Importance in Customer Analytics?

As the cost of acquiring new customer is higher than retaining an existing customer, which means that existing customers are great wealth to a business. With customer lifetime value in action, a business can decide how much money to spend on acquiring new customers and how much on retaining existing customers.

Not just that, with in depth analysis of Customer Lifetime Value, a business can divide its customers into segments and then decide upon strategies, expenditures and action plan for each group separately.

CLV can also be used to predict or catch early signs of attrition, for example for TeleCom Company, fewer and fewer subscriptions over the months or years shows signs of attrition. All businesses irrespective of size, and capital need strategies to retain its customers and acquire new customers in such a manner to maximize profit. High CLV is an indicator of product-market fit, brand loyalty and recurring revenue from existing customers. Hence, it becomes important for every business to analyze its customer base and profitability.

Many models are present in the literature for calculation of CLV, each one with dealing with different condition of factors, having different target outcome. On the basis of approach, they can be summarised in following three categories:

Deterministic Models

Customers in deterministic models are given scores that are based on the characteristics of their previous purchases. These criteria include the purchase frequency, recency, purchase amount, and so on. On the basis of these scores, it is projected what the consumer behaviour is going to be in the next purchase period, assuming that customer behaviour is going to remain same, and thus, CLV is derived. RFM models, retention model, migration model are some of the most common models of this category.

Probability Models

In probability models, the behaviour of customers is analyzed in terms of the stochastic processes that are operating in the background. These processes are defined by the observable and latent aspects of client purchase behaviours. The fact that these qualities differ from person to person makes the concept more applicable in the real world. These models are used when calculating CLV on an aggregate level such customer cohort or customer base, rather than at the individual customer level. 

Pareto/NBD, EP/NBD, Gamma-Gamma are the most popular models of this category. For predicting future transactions, purchase frequency and Churn, we used Pareto/NBD, or EP/NBD or BG/NBD Model but for predicting monetary values such as Average order Value, we use Gamma-Gamma
model(see here).

Econometric Models

In the third category, known as the Econometrics Models, the behaviour of customers is observed based on variables such as customer acquisition, retention, and growth (cross-selling or margin), and then these factors are combined (all or few of them) to estimate customer lifetime value (CLV).

These models operate on a basis that is fairly similar to that of probability models. Some of the most popular models include model for Customer Acquisition, Customer Retention, Customer Margin, Customer Expansion, etc. We will into econometric models later in more detail.

Persistence Models are an improved version of the Econometric Models that are included in the underlying concept. In a manner that is analogous to that of the Econometric Models, they model behaviour on the basis of the purchase components such as acquisition, retention, and so on. Within the framework of the Persistence Models, these constituents are regarded as dynamic systems and subjected to time series analysis. The method examines how the changes in one variable influence the others and takes those relationships into account.

On the basis of desired outcome, we can also classify the models as follows:

Models for calculation of CLV

In the first category are the models that have been developed specifically for the goal of calculating the Customer Lifetime Value (CLV), or that utilize the results of CLV calculations to develop a strategy for the most effective use of available resources in order to maximize CLV. These are a compilation of applicable models that are used in the process of formulating CLV-based plans and choices.

Some of the popular models of this category are basic structural model, customer migration model, optimal resource allocation model, customer relationship model, we will look into more detail for some of these models.

Models of customer base analysis

The second category takes into account the previous purchase patterns of an organization’s entire customer base in order to forecast the probabilities of customer behaviour during the subsequent purchase period. This can be done in terms of the likelihood of a purchase being made or the predictive value of a purchase being made.

When assessing the chance of a customer making a purchase in the subsequent time period, these models take into consideration the stochastic behaviour of consumer purchases. This implies that they take into account the reliability of each and every client. The CLV is calculated using the results of these models as the foundation for the computation or as the underlying theorem for calculating CLV. Some of the most common models for this category are Pareto/NBD, EP/NBD, etc.

Normative models of CLV

Normative Models focus primarily on the problems that have an effect on the CLV and, as a result, help maximize the CLV. These include the research into the effects of a variety of variables on CLV and the elucidation of the guiding principles that may optimize these aspects to provide the greatest possible CLV. When calculating CLV, these models account for some of the most fundamental assumptions and beliefs, such as the notion that customers with longer lifetimes generate higher profits.

The concerns with CLV are studied using normative models, which, in contrast to empirical models, these do not allow for the interference of noise. The majority of these models have overlooked competition, mostly because there was a lack of data related to the influence that competitors had.

There are several models for the many components that make up CLV, such as acquisition, retention, profit margin, purchase frequency, etc., and occasionally these aspects are merged into a single model. However, there are distinct models for each of these components. Some of the most popular models of this category are Customer Equity Model, Dynamic Pricing Model, etc.