- What is customer lifetime value (CLV)?
- Why you should calculate customer lifetime value (CLV)
- What it takes to calculate customer lifetime value (CLV)
- How to calculate customer lifetime value CLV
- Historical customer lifetime value (CLV)
- Predictive customer lifetime value (CLV)
- Traditional customer lifetime value (CLV)
- How to apply customer lifetime value (CLV)
- 6 steps to improve customer lifetime value (CLV)
- Creating value for lifetime
Marketers and managers devote time, effort, and significant resources to track their customers’ journeys from start to finish, improve content, and provide the best customer experience.
However, many marketers focus on fleeting results, such as a single sale or upsell. Businesses that want to ensure long-term success need to see the bigger picture. It means they include measuring customer lifetime value (CLV) in their strategy.
Customer lifetime value is a primary metric for understanding your customers. It’s a prediction of the value your relationship with a customer can bring to your business. This approach allows organizations to demonstrate the future value they can generate from their marketing initiatives.
As noted in this comprehensive marketing guide: “Rather than thinking about how you can acquire a lot of customers and how cheaply you can do so, CLV helps you think about how to optimize your acquisition spending for maximum value rather than minimum cost.”
Focusing on CLV helps you design an efficient strategy with concise budget planning. However, some customers bring your business more value than the others. That’s why it’s crucial to know which ones you should focus on first and invest in.
Since you can’t be sure how long this relation will last, you can estimate it and state CLV as a periodic value. Depending on your business, you can set it for different time frames, but commonly it’s fixed for a 12 or 24-month period.
One of the key reasons for measuring CLV is customer retention. Marketing Metrics reveals that the probability of selling to a new prospective customer is 5%–20%, while the probability of selling to an existing customer is 60%–70%.
It means that selling more to repeat customers will bring significantly more profits. That also highlights the impact of promoting customer loyalty. Regular customers tend to spend more on your products, helping you grow and promote your business. According to a Criteo survey, 81% of marketers claim that monitoring CLV boosts sales.
But your customers aren’t equal when it comes to revenue per customer, cost per acquisition, and other metrics. By measuring CLV you can better evaluate how much you should invest in retaining your customers. It also enables your organization to define marketing goals, plan spending to lower acquisition costs and keep retention high.
Moreover, you’re able to allocate more resources to encourage customers to spend more over their lifetime with your brand.
CLV gives you a closer look at your business’s health by taking a longer time frame into account. This gives a greater precision to determine whether your current acquisition and retention strategy is designed for scoring quick wins or supports steady and sustained growth.
Finally, customer lifetime value provides you with relevant information on your users and clients. It lets you answer some key questions, such as:
- How much should I spend to acquire a customer?
- How much should I invest to retain or win back my customers?
- How much time should my sales and marketing team spend on customer acquisition?
- Are my offers well-suited for my best customers?
First, you need a good understanding of your customers’ journey. That’s what you get from analytics – data on customer behavior and experience across multiple touchpoints, such as your website, mobile app, and digital products.
Collecting consumer data over the whole journey often involves personal data, information that can identify individuals. This kind of data falls within the scope of different privacy regulations and requires proper handling.
Read more about the legal meaning of personal data in: What is PII, non-PII, and personal data?
If you want to gather and process such data in a compliant manner, you need privacy-friendly analytics. That is, methods for measuring and analyzing data that both respect individual privacy rights and deliver relevant insights.
One piece of software that applies such methods is our own product, Piwik PRO Analytics Suite. We designed it to maximize the collection of data. But personal data is only collected when proper consent is in place. Otherwise, only anonymous data is collected.
If you obtain consent from your site visitors and product users, you get more granular information that you can use for various purposes whether it’s analytics, conversion tracking or remarketing.
Even if consumers decline to give consent, you can turn on anonymous tracking. It means that Piwik PRO registers an anonymous visitor and session. You won’t be able to identify the user, but you still obtain valuable information about the session details.
Anonymous data will allow you to estimate CLV in some cases. The addition of personal data, however, will make your CLV calculations much more reliable.
For example, adding a customer data platform (CDP) to your current analytics stack will give you the personal data connections you need to precisely calculate CLV. It allows you to merge data from multiple online and offline sources, such as from a CRM or a transactional system.
Read more about customer data platforms in: Customer data platform: what is it and how does it work?
If you want to calculate CLV you have a few formulas to choose from. Your choice depends on the resources you have. But just pick one and stick to it. We’ll present you with the simplest and most traditional one. To measure CLV, you need to include the following:
- Customer lifespan
- Retention rate
- Customer churn rate
- Average profit margins (per customer)
Also, you can differentiate between historical and predictive CLV. We’ll talk about this in detail in the next sections.
Historical CLV is the sum of all the gross profit from a customer’s past purchases. To calculate it, you need to add up all the gross profit values up to the last transaction (N) a customer made. Measuring CLV based on the net profit to get the true profit a given customer generates.This involves costs of service, return, marketing, acquisition, and so on.
The downside is that you might have to do some complicated math at the individual level to have the most up-to-date data. Still, gross margin CLV gives you a thorough understanding of your customers’ profitability to date.
Where: AGM = Average Gross Margin
In principle, this method is handy if customers share the same preferences and interact in the same way with your brand over roughly the same time.
Keep in mind that calculating historical CLV means putting all customers, old and new, into one basket. That might be tricky, because they can vary when it comes to behavior and preferences. Differences between clients can affect CLV.
The aim of predictive CLV is to model the transactional behaviors of your customers to forecast what actions they will take in the future. It’s a great indicator of CLV, better than historical CLV.
The predictive model uses algorithms that try to generate a precise CLV while predicting a customer’s total value. It works based on a history of past transactions and the customer’s actions.
Again, you can choose from different CLV formulas. We’ll focus on a simple one for clarity’s sake.
Here’s the formula:
T = Average monthly transactions
AOV = Average order value
ALT = Average customer lifespan (in months)
AGM = Average gross margin
An analytics platform with a CDP can quickly integrate with your CRM to give you easy calculations for AOV and ALT.
However, keep in mind that this approach is a prediction, so it won’t always be 100% accurate. To improve accuracy, you should adjust CLV calculations to the specific industry you operate in. Precision in your CLV gives you a tool for developing sound marketing strategies.
Sometimes a more traditional but in-depth CLV formula might work better. That’s the case when your yearly sales aren’t flat. Then, it’s important to consider the discount rate, average gross margin per customer lifespan and retention rate.
Here’s what your final formula will look like:
GML = gross margin per customer lifespan
R = monthly retention rate
D = monthly discount rate
This formula looks at possible changes in customer revenue throughout a period of time. Then, each year is corrected by a discount rate to account for inflation.
Once you calculate your CLV, you can use this information to chisel your strategies. Here are a few cases where you can apply it.
- Segment your customers effectively: With the application of CLV models, you can improve both profiling and segmentation. That allows you to customize offers and target customers based on their potential value. It also enhances forecasting and lead conversion rates. Additionally, you can increase the effectiveness of your segments with data gathered exported from different sources into technologies like customer data platforms (CDP).
- Optimizing acquisition: Experts from ALTA stress that predictive CLV lets you increase acquisition as it helps professionals “make sure to acquire subscribers who will represent the biggest lifetime value in the future.”
- Lift retention: CLV is your compass for navigating steps to keep your customers loyal to your brand. It aids you in setting priorities, such as which customers to win back, and in devising a unique strategy to do just that.
- Improve forecasting: Calculating CLV provides you with a tool to predict the future need for your products or service. With this information at hand, you can manage your investment in terms of workforce, inventory, or other resources. Detailed forecasting is vital for reducing productivity losses and allocating resources more efficiently.
- Recognize best customers: Data on your customers, like frequent purchases and transactions, allow you to spot those who spend the most. This information tells you which products to promote more heavily. You can invite customers to special events and offer deals tailored for high-value clients. Finally, you can take better care of your biggest customers by providing them with an individual assistant or adviser.
Let’s think about what you need to improve your CLV. We’ll walk you through some steps you can follow to improve your relationship with customers, providing them with a better experience to retain them.
Because you power your CLV with data, it’s crucial to use high-quality data. In that case, your focus should be on first-party data. Since this kind of data comes directly from your customers, it’s more accurate and gives you details about consumers’ interactions on your website or app, like form submissions, product views, and in-site search queries. You’ll need such information to improve content personalization and recommendations, activate audiences, and refine up- and cross-sell initiatives. A carefully personalized user experience significantly boosts revenue and CLV.
It’s crucial to get the first experience customers have with using your products and services right. A smooth onboarding process encourages customers to use your product and come back to it often. That translates into higher lifetime value in the long run. Making the process quick and providing e.g. video tutorials might come in handy.
It’s essential to take care of your customers both before and after they make a purchase. The building blocks of this care are enabling them to feel comfortable using your product, staying proactive, and replying to them on time. Your support team can also assist customers with personalized training and practical self-help guides. According to Microsoft research, for 90% of Americans, customer service is a factor in deciding whether to do business with a company.
Another way to improve CLV is to increase average order value. You can achieve this taking advantage of up- and cross-sell methods. Next time when your customer is making a purchase you could offer them a complementary product to those they’re just about to buy.
Good communication with your customers is about knowing their needs, active listening and building relationships. It also involves collecting feedback from your customers, giving them explicit answers to their technical questions and adapting communication channels to match their references.
The world is changing rapidly and so are customers’ expectations and desires. If you want customers to stick with your business, make your offer relevant to your audience. Ensure good communication with customers, provide them with products that address their needs. Finally, it’s important to have a competitive advantage that you can present to them.
CLV is one of the most critical metrics for businesses that adopt a data-driven approach. However, it should be one part of your strategy, not the sole focus.
Improving your CLV takes more than better customer service and staying relevant. It requires changing your perspective on consumers and seeing them “more as value-creating partners than as value-extraction targets,” as Michael Schrage notes. This researcher explains that customers become more valuable when they give you good ideas, cooperate with you, share their data, try out your new products, and promote your company.
But consumers give you even more information as they request new features, share experiences of using competitors’ products and get in touch with your customer support.
All that underlines the importance of data in measuring and improving CLV. With the use of the right analytics software, you’ll be able to collect that data and transform it into actionable insights.