Customer Lifetime Value (CLV) is defined as the entire value of a customer to a brand for the entire relationship with that customer.

Table of Contents

• What is Customer Lifetime Value (CLV)?
• Why should you care about Customer Lifetime Value? The 4 Key Benefits
• How to Calculate Customer Lifetime Value: Formula and Models
• Customer Lifetime Value (CLV) Benchmarks
• How to Improve Customer Lifetime Value? 7 Tips to Optimize CLV

What is Customer Lifetime Value (CLV)?

DefinitionCustomer Lifetime Value (CLV) is defined as the net profitability associated with a customer’s lifecycle with the company. Simply put, CLV is a projection for what each customer is worth to a business.

It is important to note that CLV is a prediction – and the accuracy of this prediction is dependent on 2 primary factors:

1.Their current behavior and purchase patterns
2. Their projected behavior and patterns

The projection, in turn, depends on your company’s technology stack and predictive analytics capabilities. The stronger your analytical powerhouse, the more accurate is your CLV.

Unless you want to drive blind-folded, customer lifetime value is a critical metric for any organization’s capacity to remain or become profitable. This is because CLV enables businesses to plan their customer acquisition and retention strategy in such a way that the cost of acquiring/retaining a customer is less than the lifetime monetary value of the customer. It shows a comparison between the customer’s revenue value and the predicted customer lifespan and helps understand a reasonable cost per acquisition.

CLV, however, is different from customer profitability (CP). CLV helps calculate the expected spends and individualized value of each customer; whereas CP is the analysis of just the past spends and does not attempt to predict future spends.

Knowing the CLV is imperative for the business to help strategize the business in a way that retains existing customers and acquire new ones. It is a primary metric to gauge what is working and what is not.

For example, Panera Bread operates over 2,000 physical stores across the United States. It is important to know the sort of customers they are drawing, repeat customers as well as the customers they are losing to larger chains like Starbucks. CLV helps them understand the value of each customer taking into consideration overall factors like average spending, brand retention rate, etc.

In the above example, calculating the CLV would help Panera to make timely tweaks to service levels, new product launches, gamified programs and overall increasing the satisfaction at the point of experience. This means that there is a marked difference in the way that each department is expected to operate.

This is how different departments in an organization are impacted by calculating CLV:

• Marketing: Are the current marketing spends and channels justified and relevant to bring in new customers as well as offer enough for existing customers? Does it help to advertise at a higher cost per acquisition if the lifetime cost supersedes the ROI on the current spend?
• Sales: Is there a mechanism to tailor-make sales programs to be inbound or outbound centric depending on the lifetime value of each customer? How much more sales input and support would be required to increase the value of each customer?
• Product/Service: Is the product/service relevant to existing customers and is the purchasing decision-making process reducing with the current product/service line? Is there a need to make tweaks to exceed customer expectations and in turn increase the value of the customer?
• Customer Service: Do there need to be time and resources spent to manage customer satisfaction and customer experience? Will an increase in efforts for customer service increase the lifetime value of each customer?

This means, that while CLV is just one number, calculated by a few people in the company, its implications trickle down as guidelines for the whole organization to function as one. Much like how it takes a whole company to deliver on customer experience (CX) goals, the CLV is a reminder to every department – to not spend more than what can be reaped!

For example, did you know that Facebook turned profitable in 2009? This means, from 2004 to 2008, for 4 years, the company focused on user acquisition because they knew that once they have enough users advertising on their platform, the net CLV for each customer will be much higher in the long term. Without this predictive understanding, Facebook would not have inclined towards investing in customer/user acquisition at a loss for 4 years. This strategy was later followed by WhatsApp, Twitter, Amazon and is the mainstay of well-funded startups.

Why should you care about Customer Lifetime Value (CLV)? The 4 Key Benefits

Customer lifetime value was a term coined in 1988 and from the early 1990s, it has formed the ethos and DNA of many mature organizations. For example, CROCS used CLV to understand the customers with the highest churn and the customers that weren’t price-sensitive, to promote to them differently. This lead to a 10x revenue gain for high churn customers and a 2x revenue gain for loyal customers. Win-win and how!

Whilst there are different metrics you should care about CLV and historically there have been multiple examples, these 4 key benefits apply to businesses no matter the organization type and size.

1.Tailor-made customer programs

If marketing and sales campaigns are developed invariant of the market demographics, customer base and without consideration for microeconomic financial milestones, the campaign would invariably end up being a dud. However, if the CLV is calculated and then the customer base is segmented for various marketing and sales programs, there is a higher chance of success of converting customers into sticky customers and brand advocates.

For example, Abercrombie & Fitch has tailor-made programs for the heavy spenders as well as casual shoppers. This helps the brand to maintain a loyal base of shoppers as well as increase spends from casual shoppers that aid towards a higher dollar spend.

2. Appropriate spends & higher ROI

CLV helps to identify the type of customers, the avenues where they convert as well as what appeals to them. This means that there could be localized campaigns, demographic-based campaigns, sales vs marketing campaigns and even in marketing campaigns, online vs offline marketing. It could also be a mix of the above but it helps derive the best avenues to spend and the programs that derive the highest ROI for the brand.

For example, Starbucks provides a gamified platform for repeat customers and has full-blown programs to entice new customers with the promise of a better “experience” including the coffee roasts, quality merchandise, healthy bites and free Wi-Fi.

3. Increased customer retention value

An important metric that CLV helps identify is the sticky and most loyal customers. It makes sense to identify and nurture such customer relationships because a brand can then co-create with them as well as they act as brand advocates.

For example, Amazon Prime is a curated platform to make life easy for customers as well as provide entertainment experiences in the form of leisure items. The ethos of the brand is to create a loyal customer base and build on top of that so that these customers are advocates for the brand because of heightened experiences.

4. Reduced customer churn

Where CLV helps the most is to retain existing customers and reduce the spending to bring on new customers. By identifying gaps in service/product delivery and optimal channels of customer liaison, CLV is a powerful tool to reduce customer churn.

For example, Kimberley-Clark along with Nielsen identified that around $1000 is spent on diapers and baby wipes in the first two-and-a-half years of the baby’s life. This helped them to identify multiple touchpoints to deliver these products and attractive promotions with ease of access so that parents didn’t buy competitor products.

All in all, CLV is a powerful tool that brands need to use to be customer-vigilant and profitable in the ever-changing market ecosystem.

Learn More: Top 10 Digital Customer Experience (CX) Software Platforms For 2020Opens a new window

How to Calculate Customer Lifetime Value: Formula and Models

The CLV calculation formula is varied for each organization because each of them tracks data differently and organizational goals may be different. However, there are multiple methods to calculate customer lifetime value. They broadly are, historic CLV, predictive and lifespan CLV, cohort analysis, and individualized CLV.

1. Historic CLV calculation formula

Historic CLV can be termed as the sum of profits from a customer’s past purchases. This is based on the existing customer data for a given period of time. This is a simple method to calculate CLV and is one of the quickest methods given the nature of the input data. The formula to calculate historic CLV then is:

Historic customer lifetime value = (Transaction 1 + transaction 2 + transaction 3 + ….N) * AGM

Where N is the last transaction of the customer and the AGM is the average gross margin for the brand.

This calculation method helps predict the worth of a customer to the brand to date taking into consideration all line-item spends to bring in and retain this customer. This, however, is a rudimentary method of calculating CLV and may not paint the entire picture of a customer’s worth to a brand since the input variables are limited.

2. Predictive CLV calculation formula

The predictive CLV calculation formula is a detailed formula to calculate the predicted spend and value of a customer to a brand since it takes into consideration a lot more data points.

In real life though, it is a little tricky to calculate the predictive CLV because of the fluctuation of multiple factors like spending capability, market value, pricing, discounts, etc. Hence the predictive formula calculation can be broken down into two distinct sections: simple and complex.

The simple predictive CLV calculation formula is where the average of all the below aspects are factored in:

Simple predictive CLV or GML = ((Monthly transactions * Value of each order) Gross margin) * Lifespan of the customer

The above equation can be used to calculate the predictive CLV. But to factor in complex factors such as retention rate, discounts, etc. you can use the above value as the gross margin contribution per customer value (GML) and then calculate as:

Complex predictive CLV = GML * (R/(1+D-R))

Where GML is gross margin contribution per customer value

R is the monthly retention rate

D is the monthly discount rate

Learn More: What Is Customer Analytics? Definition, Process, Key Trends And ExamplesOpens a new window

3. Lifespan CLV calculation formula

The third widely used CLV calculation formula is the lifespan CLV calculation. In this method, there are 5 distinct steps and is the formula that a behemoth like Starbucks uses to calculate CLV.

• Average purchase value: The first step is to calculate the purchase value of each customer. If a customer visits a store 5 times and spent $15 in total, my average spend is $3. By repeating this exercise, you arrive at an average spend of all customers. Averaging that number gives you the average purchase value. Let’s, for example, say that the number is $3.65
• Average frequency purchase rate: The next step is to calculate the frequency of the customer’s visit to a location in a week. If we were to consider this weekly and calculate the value, the average of 100 customer’s visit to a location for example then is 4.2 times
• Average customer value: Now that the average purchase value and average frequency rate for each customer are known, by multiplying the two you can arrive at customer value. Averaging out all the customer scores then is the average customer value for the week. This, for example, could be $18.45.
• Average customer lifespan: The average customer lifespan is individualized to each organization and can be rather arbitrary but for the sake of this example, let’s consider it to be 5 years.
• Calculation: To then calculate the lifespan CLV, multiply the average customer lifespan into the number of weeks (since we calculated the average customer value per week) and the average customer value. This number then equates to (52 * 5 * 18.45) = $4,797 as the lifespan CLV of a customer.

This method is similar to the predictive CLV in the sense that they both help calculate the predictive value of a customer.

4. Cohort CLV analysis

The cohort CLV calculation method is the bunching or grouping of people with similar demographics data or any organizational pre-defined data to analyze people as a collective or a group. This calculation method can be utilized to broaden the scope of the CLV and draw parallels between unique and distinctive groups of people.

This, for example, could be either on similar dates of subscriptions or creating a loyalty program or even types of subscription plans, etc. This model would work well for the likes of Netflix due to its operating model. Another industry where cohort CLV analysis would work is a frequent flier program due to the dynamics of frequent flier creation date, tier model on the basis of the number of flights flown, distance flow, the class flew, etc.

To keep cohort CLV analysis conclusive, organizations must track market dynamics and fluctuations in external factors to the business model.

5. Individualized CLV calculation

The individualized CLV calculation is used by organizations to monitor broader perspective trends in the market dynamics. For example, if Best Buy were to monitor marketing spends on online purchases on Cyber Monday versus in-store purchases. This could also be used to monitor ROI for macro-economic activities like digital marketing spend vs cold calling spend, etc.

The above different methods of calculating CLV are used by organizations to track and monitor different aspects of their business to understand operating costs, profitability, ROI of different activities, etc. In most cases, these models cannot be used in isolation but a mix of these different models may be required to gather conclusive data and insights given the time available, customer-centricity of the organization, nature, and countries of operations, federal mandates, etc.

The Customer Lifetime Value (CLV) Models

As seen above, there are multiple methods to calculate CLV. Two widely used methods that are relevant to most businesses are the predictive CLV and the lifespan CLV because they are non-contractual and there is always scope for a customer to make a purchase, including a higher dollar value purchase.

However, mature organizations work off of Probabilistic models are the most effective methods of CLV modeling. There could be different probabilistic models but the contours of the probabilistic frameworks remain the same.

What then, are the 3 latent parameters in the probabilistic modeling framework?

There are 3 variables that are relatively unknown and that can completely fluctuate in the CLV probabilistic modeling method. To make sense of these and in general reign them in is a better metric to taming the CLV calculation beast. They are:

• Lifetime: The lifetime of the customer is an invariable metric. This could differ from customer to customer and also can change very quickly depending on market relevance, spending capability, brand loyalty, etc.
• Purchase rate: The purchase rate is another metric that can very easily fluctuate on the basis of need vs want. There is no fixed way of knowing the total number of purchases a customer will make and the frequency at which they will be made.
• Monetary value: The last parameter that is also extremely arbitrary is the monetary value associated with each purchase and a consolidation of all purchases.

1. Pareto/NBD Model & Gamma Gamma Extension Model

There are 2 commonly used models to vector CLV modeling given that there are 3 invariant variables that are discussed above.

Pareto/NBD Model to calculate CLV

The Pareto/NBD model is one of the most widely used models to calculate CLV due to it being based on the past orders to draw an inference into future purchases with latent parameters. This model uses a customer’s order history as the primary input to calculate and the recency and frequency of the past orders as the secondary inputs.

The premise of this model is based on the Pareto distribution of probable customer churn and the NBD model to determine the number of times that a customer will make a purchase. These are both predictive models. The more information that is available on the customers, the more effective do these models end up being.

2. Gamma Gamma Extension to Pareto/NBD model to calculate CLV

The Gamma Gamma extension model is used in conjunction with the Pareto/NBD model because it takes into consideration the third unknown variable, the monetary value of each purchase. The premise of this model is that the monetary value of a customer is independent of the number of purchases made.

Both of the above are advanced statistical models that organizations employ to create a predictive CLV calculation model and they are required to be run in advanced parameter sets and complex software to achieve.

Customer Lifetime Value (CLV) Benchmarks

In today’s competitive world, organizations are constantly looking to benchmark against their peers to increase profitability, broaden the customer base and in general increase the stickiness of customers.

So, are there benchmarks then that they aspire against? The short answer is no. Industry benchmarks for CLV are tough to create because of proprietary customer information that brands would not disclose outside of the management hierarchy. The CLV for different brands operating in the same space may also be different due to the nature of the business, brand objectives, pricing models, customer centricity, operating models, target demographics, data collected, etc.

But however, does this mean that there are no CLV benchmarks at all?

There are some benchmarks that a brand must aspire towards:

1. One of the most important internal benchmark is that the customer lifetime value (CLV) has to be at least 3x of the customer acquisition cost (CAC). This would mean that there is very high business relevance.

2. Another metric for the CLV benchmark is that organizations must conduct timely score evaluations and then work to improve those scores. This could be quarterly, monthly, annual or any other metric that they decide.

For example, the average time that a consumer holds a Netflix account and actively pays for it is 25 months of subscription. Netflix constantly endeavors to increase that amount, because the longer a consumer is on their platform, the more is the recurring revenue generation. This means improving customer experience by offering advanced streaming features while ensuring that the pricing is competitive/ is considered worth its value. These parameters, and more, propelled by CLV measurement, essentially helps Netflix increase the average time spent per user account on their media channel and thereby increasing the overall customer lifetime value.

How to Improve Customer Lifetime Value? Tips to Optimize CLV

1. Customer service and gamification programs

A lot of mature brands do not focus as much as they really should on customer service programs. Solving problems before they reach social media or snowball into larger issues is imperative. Creating a customer service model across your organization from the nascent stage is a key tip to increasing customer satisfaction and increasing the number of happy customers. Happy customers translate to a higher CLV.

Customers are more likely to offer feedback and stay in your ecosystem if they are getting something more than what they are paying for. Set up loyalty and gamification programs that allow customers to be deeply entrenched in your offerings and gain out of it. These also help in brand loyalty due to the top of mind recall.

2. Omni-channel model

As a brand, it is important to diversify into an omnichannel model. This is both for marketing and sales as well as product and service delivery. Find a consolidated model to reach and interact with a brand as well as increase the ease of them reaching you. This reduces customer churn and keeps the customer loyal to the brand.

3. Point of sale satisfaction

A cardinal error that brands make is to demarcate between delivery of goods and services and leaving customer service for when issues come up. Creating a model of satisfaction at every touchpoint with your brand will keep customers happy and content and less eager to evaluate competitors.

4. Nurture and develop brand advocates

The best marketing is in references. And it is absolutely free. Identify and take care of your best customers with custom programs, higher brand visibility, etc. They will act as advocates for your brand in a manner that increases your brand value without you having to spend on marketing and sales efforts. Goodwill begets goodwill!

5. Co-create with your brand’s top customers

Your top customers know real-world problems, wants and needs that your product or service caters to. Co-create with your brand’s top customers to increase the relevance of your offering. Giving customers what they want reduces customer churn and also brings in new customers to the brand.

6. Mobile-first buying experience

In the world of mobile-first where purchases begin and end online, customers are easily put off by shoddy mobile purchasing experiences. Make sure that you create a seamless mobile purchasing experience for your brand so that customers see a unified brand value and are happy to interact and buy from you from the comfort of their smartphones

Learn More: What Is Customer Data? Definition, Types, Collection, Validation and Analysis