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What is customer lifetime value and how to increase it?

customer lifetime value

Customer lifetime value (CLV) is the metric that measures how much a company is likely to gain from its relationship with a customer for as long as that relationship continues. CLV is one of the main KPIs in marketing, one that is important because it can return the total value produced by an (average) customer for a company over the lifetime of their relationship. Among the metrics that marketers typically use, CLV is one that can’t be missed. To confirm its strategic importance, we only need to compare the cost of obtaining new customers and the expense of continuing to sell to existing customers.

The CAC (the cost incurred by a company to win a customer) can amount to up to 7 times more than the cost to retain a customer. In addition, the probability of successfully winning a new customer ranges between 5 and 20%, while the probability of selling to an existing customer is between 60 and 70%. Therefore, the benefit of investing in actions that increase the lifetime value of a customer and more generally in loyalty programs, seems clear.

It’s not only about saving time and financial resources: including CLV among the most regularly used metrics is always a good idea. Thanks to customer lifetime value, you can assess business results and performance with a certain degree of accuracy and make more informed marketing and sales decisions accordingly. Analysis of CLV by customer segment can offer insights into what is working and what isn’t, and even into what may need to be eliminated. In a more proactive sense: increasing the CLV of existing customers contributes to revenue and sales growth.

There are many tools that companies can use to improve CLV over time. In this post, we will provide: an easy to understand definition of the customer lifecycle value, some tips on how to calculate it, and we’ll suggest some tactics for increasing it.

 

 

Customer lifetime value: a definition and an example to explain it better

Customer lifetime value expresses the economic benefit that a company expects to accrue as long as a person remains its customer. This is a basic definition, but it can get complicated, even very complicated, if we go beyond the basic calculations (which consider only revenue) to also take into account gross margin and operating expenses (in this case, more complex equations become necessary). For simplicity’s sake, we will refer generically to revenue in this post.

We have said that customer lifetime value is a measure of the revenue that a customer has generated throughout his or her relationship with a company. Now, this customer can be considered as a unique value or as part of a broader segment. In the latter case, we’re talking about a “typical” or “average customer.” Either way, whether it is a real person (with his or her distinctive, unrepeatable profile) or an abstraction (useful for describing structured situations or over extended periods of time), comparing CLV and customer acquisition cost is always a quick way to estimate a customer’s profitability and the company’s long-term growth potential.

For a company, an inversely proportional relationship between customer acquisition cost (CAC) and customer lifetime value (CLV)—where the latter number is higher—can be said to be truly profitable. The less it costs to acquire a single customer, the higher the overall value the customer represents and the greater the profit the company can realize.

An easy example of customer lifetime value

Let’s try to put this in context with an example. Imagine that you are in charge of marketing and sales for a small grocery store chain and you want to measure performance over the past five years. Based on the data in the ERP system, we track: 

To calculate our customer lifetime value, we will have to multiply these three numbers (35 x 52 x 5) and get a total value of €9,100. In summary: to calculate customer lifetime value, we multiplied the average revenue or profit per visit by the number of visits per year, and then multiplied the result of this first operation by the average number of years of the customer relationship. The formula to be applied is as follows:

CLV = Average transaction value x Number of transactions x Time period of the relationship

Three very good reasons to calculate customer lifetime value

Companies that decide to calculate their customer lifetime value, like the small grocery chain in our example, do so for three very good reasons:

  1. Because you cannot improve what you don’t know. By measuring customer lifetime value you can break it down into its components (prices, sales, advertising) and target specific strategies in a more granular and articulated way, with the goal of building customer loyalty, progressively reducing costs, and ultimately increasing profits.
  2. Because measuring customer lifetime value allows you to identify the “best” customers (the ones who are most loyal or spend the most). With CLV you can get a better idea of how much you will earn from your customers, and based on this knowledge you can decide whether to increase or decrease your planned spending on retention or acquisition.
  3. Because customer lifetime value gives you access to more accurate forecasts. Keeping customer lifetime value in mind helps you make forward-looking decisions about inventory, staffing, production capacity, implement targeted actions, reduce costs, and maximize profitability by attracting the “right” types of customers.

 

The four main benefits of customer lifetime value

Customer lifetime value helps answer some questions that are critical in determining future strategic decisions:

The benefits of integrating the customer lifetime value within a marketing plan are a direct result of this ability to answer complex issues in a simple and straightforward manner. Specifically, there are four main benefits: 

Focus on customer experience: 3 ways to improve customer lifetime value

Focusing on customer satisfaction is by far the best way to increase the lifetime value of a customer. There are many different strategies that companies can adopt to achieve and maintain this goal. We have grouped them into three macro groups.

1) Design personalized initiatives.

Personalized marketing initiatives allow you to focus on specific customer segments to which you can target tailored content. 

2) Invest in the customer experience

The website, the storefront, and the call center are all touch points in the marketing funnel that help define the customer experience. If customers experience a smooth, stress-free shopping experience at every step of the way and on each touch point, they are likely to feel more engaged and decide to return again and again.

3) Adopt the most appropriate technologies

Customer lifetime value can be complicated to calculate, understand, and use if the reality you want to assess is particularly complex and if you don’t have the appropriate data storage and monitoring systems. An enterprise resource planning (ERP) system or a customer relationship management (CRM) system with automated dashboards that track many KPIs at the same time can make the information easily available and immediately actionable. Technology today can automate processes and monitor and centralize much of business data, from basic tools such as email, spreadsheets, and contact databases to more advanced software platforms and suites that centrally manage huge amounts of information and enable a wide range of different functions.

Equipping your organization with the right technologies is the only way to harness the potential of information-intensive metrics such as customer lifetime value. The point is not to limit measurement but to choose the most effective tools to enhance what emerges from the metrics. From this perspective, developing a personalized communications system that invests meaning in every possible touchpoint is the most effective way to increase customer lifetime value.

Doxee’s interactive experience products enable precisely the enhancement of all touch points: they can extract the hidden value in customer data to create highly personalized and interactive content.

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