When measuring your company’s success, you don’t just want to know how many sales you’ve already made. You also want to be able to project how much money you will make in the future. Establishing a customer lifetime value, or a CLV, is the metric that helps you do that.
What is Customer Lifetime Value?
CLV refers to how much a business can expect to earn from an average customer for the entire course of their interaction. This is a useful metric because it helps companies develop a deeper understanding of how customers interact with their business. This empowers them to make smarter decisions with regard to marketing and sales.
Of course, this isn’t a straightforward calculation to make. To get the most accurate picture possible, you have to consider different products, their respective costs, realistic purchase frequencies, and purchase volumes. Therefore, it makes sense to examine the total average revenue generated as well as the total average profit.
You may even decide to get more detailed with the data, breaking down CLV by quartile or segmenting to examine different demographics. Why? Because then you can get a clearer idea of what is working effectively with your high-value customers. You can then develop a strategy to replicate that approach with other demographics.
How CLV is defined varies across industries and businesses depending on the data that’s most relevant to them. At its most complex, CLV could even take operational costs into account. A simpler method is to focus on revenue, though.
Why is CLV important?
CLV doesn’t just tell a business how much a customer has spent so far; it predicts how much they are likely to spend in the future. This allows businesses to assess their trajectory and plan accordingly more accurately. It’s an important metric, especially for small businesses.
Companies have to invest to attract and acquire new customers. By comparing CLV with the cost of customer acquisition, a company can estimate its future growth and how profitable each new customer really is for the business.
Once a business knows its CLV, it can take steps to improve it. This might involve targeted marketing, for example. When CLV increases, this suggests that these strategies have been effective. It’s important for a business to know what is working and what isn’t, especially when it comes to costly marketing campaigns.
Business benefits of knowing CLV
The business world is more data-driven than ever, and CLV is an invaluable metric. Although the calculation can be complex, companies benefit from understanding their customer lifetime value in the following ways:
Knowledge regarding the various components of CLV allows businesses to adopt an informed approach to pricing and advertising. It can help them to develop strategies that increase customer retention.
Companies spend a lot to acquire and retain customers. They need to know if their expenses are justified by the CLV and which customers are most valuable. Otherwise, they could be wasting money on their efforts. An appreciation of CLV will ensure they operate in a cost-effective way.
No business can predict the future, but CLV supports companies to forecast more accurately. This is important because they can then plan long-term when it comes to staffing, production, and other essential costs. They look forward with more confidence when they know their CLV because they understand the demand for their products or services.
If a company segments the CLV data, it can identify the most critical customers to retain and apply extra attention to this audience. By developing a clearer understanding of which strategies customers respond well to, attrition can be avoided.
CLV allows you to track repeat visitors and strategize how to increase visits. It also provides important insights that can help you to generate higher-value sales. The overall effect? Increased profitability, which is surely any company’s number one priority.
How to calculate CLV
The first step is to determine the value of an average order. You should already be tracking this data. Even if you’ve only been in business for a relatively short time, you can use as little as one month of results to get a rough idea.
Then, you should calculate how many transactions are performed on average per period. This will depend greatly on the nature of your product or service.
A fast-food restaurant might attract weekly visits, whereas an appliance store might only expect to sell to the same customer once every few years. Therefore, the frequency of visits is crucial to CLV. The more accurate this figure is, the better.
Next, you have to understand your customer retention: how long the average customer stays with your brand, using your products or services. Expected customer loyalty differs across industries.
For example, a customer may stick with the same car brand for their entire life. However, thanks to the ever-changing world of fashion or because they’ve aged out of target demographics, they might switch between different clothing companies throughout their life.
Once you’ve extracted this information from your data, you have all you need to calculate CLV. You achieve this using the following formula:
CLV = Average Transaction Size x Number of Transactions x Retention Period
Based on this formula, all you have to do to increase your CLV is increase transaction size, number of transactions, or your customer retention period.
You have to consider all of them together, though. For example, increasing prices (transaction size) might sound like an easy way to increase CLV, but it could have a negative impact on the number of transactions a customer makes or even your ability to retain their loyalty. So consider how these elements interact before making any drastic decisions.
Challenges of CLV
Unless you have adequate tracking systems in operation, you may struggle to obtain the data required to make this calculation. This is why it’s important to consider your data sources and how they are managed as part of your business plan.
You should also be careful not to assume that a high CLV means your business is operating at its full potential. For example, when you segment the data, you may find that there is still room for improvement amongst certain demographics or during particular periods.
