Customer lifetime value (or CLV) is a widely recognized marketing concept, yet very few businesses know how to correctly measure it. Customer lifetime value is defined as the prediction of the net profits that can be attributed to a particular customer during his or her lifetime with a business.
More simply stated, CLV is the monetary value of a customer relationship.
In today’s rapidly changing digital world, big data provides a company the unprecedented ability to better measure CLV, but only by understanding your big data can you gain valuable new customer insight, develop more effective marketing strategies and ultimately set yourself apart from your competitors.
Why You Should Even Care
We know what you’re thinking: collecting raw data and calculating Customer lifetime value is daunting and I don’t even know where to start! Yep – we get that.
Collecting detailed data on each individual customer, defining different segments, and identifying key parameters, variables and inputs certainly isn’t easy. Of course, rarely is the “easy” worth doing at all, especially in competitive landscapes where doing what everyone else does won’t get you a second click (or even thought for that matter).
What many companies fail to appreciate is that accurately calculating Customer lifetime value can be one of the – if not the – most powerful metrics for accessing the true value of your customers. Ultimately, what every company wants is the ability to identify each customer’s personal behaviors, understand what their value would be to the company and leverage that understanding into focused strategic initiatives to capture that consumer’s lifetime value, boost revenue and growth.
Thankfully, Customer lifetime value does just that.
The Math Behind the Metric
The benefits of having a strong model to accurately calculate CLV are many. For the most part, companies do not track and estimate Customer Acquisition Cost (CAC) – the cost of acquiring a new customer – which is one of the key inputs that feeds into calculating CLV. To be sustainable and profitable, a business must seek to ensure that their CLV is higher than their CAC and continue to re-assess this ratio as it fluctuates.
While knowing CAC is useful for many reasons, it is more useful when leveraged to accurately measure the CLV of a particular customer. After all, acquisition cost doesn’t match expected ROI if that new user isn’t into your brand for the long-haul (or at least just a longer than today haul).
In essence, by incorporating CAC into the CLV calculation, a company can determine whether a specific consumer is going to be profitable or not. The company can then spend their targeted marketing dollars toward those valuable customers by launching specific promotions and campaigns. Not only does this better target valuable customers, but it also allows a business to more accurately evaluate the effectiveness of launching a specific campaign. In other words, CLV calculations aren’t just about the long-term. They help to prove the value of a campaign before it is launched, ensuring you get the ROI you need to invest in the spend.
Quality Over Quantity Breeds ROI
Most businesses either assume that all customers are equally important, or, even if they do realize that customer loyalty drives the value of an individual customer, few companies treat loyal customers any differently than those with low CLVs. As a result, they end up launching unfocused, expensive marketing initiatives that yield very little lift in revenue.
We know customers respond differently to marketing campaigns and promotion techniques, and big data can allows us to segment those differences more accurately. Segmenting customers effectively allows a business to identify its “loyal,” “average,” and “non-” customer base. According to Pareto’s 80:20 principle – the rule states that 80% of your sales come from 20% of your clients – these loyal customers are responsible for the majority of profits. By using CLV to truly understand your customers, you will be better able to retain your “loyal” customer, turn “average” customers into “loyal” ones, and identify true “non-” customers, allocating company efforts to the customers that matter.
Segmenting customers effectively allows a business to identify its “loyal,” “average,” and “non-” customer base.
Customer lifetime value can be leveraged to implement initiatives that extend the lifespan of a customer through brand loyalty, increase the frequency of visits and purchases, and develop directed marketing campaigns to find more customers with similar behaviors. It’s about instituting a catch and keep strategy rather than wasting time of catch and release.
The importance of calculating Customer lifetime value is becoming more and more clear, especially as web profitability metrics shift priorities from pageviews to engagement. The next hurdle once a company commits to calculating CLV is defining the parameters, variables and inputs used for the framework. Each company must define these parameters according to their own business need. Taking the time and finding the right advisors to identify and define these parameters in advance of developing a framework will make it a less daunting experience, and at Umbel, we focus on helping our clients identify and engage those users with the highest CLVs.
Ultimately, a robust CLV calculating framework can allow a business to significantly improve its strategies and initiatives to increase revenue and boost customer retention.