Legend has it Alfredo Pareto noticed something unusual in his garden, 20% of the pea pods were responsible for 80% of the peas. A Eureka moment which sparked the Pareto’s research into income distribution. Conventional wisdom dictates that the 20/80 rule also applies in business, but looking at actual data reveals a slightly different picture. This is where customer lifetime value, or CLV, will help your business.


Example of a Pareto Distribution using an open-source dataset.

Recent research ( Kim et al. 2017) shows that consumer packages goods(CPG) categories have a Pareto ratio of .73, meaning that 73% of the sales came from the top 20% of the customers. In non-CPG-industries, the Pareto Ratio varies from 0.67 for non-subscription firms to 0.59 for a subscription business( McCarthy et al. 2019). So a small fraction of customers seems to be responsible for a large part of the revenues, an observation which holds for different industries and even different business models. A fundamental question for any company then becomes what value does an individual customer represent? Or more simply who is part of the 20% and who is part of the rest? Understanding which customers are responsible for your (future) revenue is a solid foundation for growth strategies and go to market decisions.

To assess the individual value of a customer requires insight into three different factors: average order value, purchase frequency, and the lifespan of a customer. The Customer Lifetime Value (CLV) metric was developed and combines each of these values into a single number. CLV estimates how much value (revenue or profit margin) any given customer will bring to your business over the course of the total time they interact with your company—past, present, and future. Most companies understand the past and present value reported by their finance department. A prediction on the future value of customers, however, is often lacking. Insight into CLV is important and even essential to growth as the following case illustrates.

The case of Customer Lifetime Value at Slack*

Slack, the collaboration software company, is an extreme example where it really pays to compute the CLV of individual customers. Slack customers have an average payback period of 3 years. Slack spend 233 million in Sales & Marketing to acquire 30.000 new customers in 2019, thus having an average cost of acquisition of $7700. Slack may, therefore, incur substantial losses as it builds its subscriber base and to make things worse Slack loses money on 75% of newly acquired customers. A bleak scenario on the surface, however, the remaining 25% more than makeup for these losses. It’s so extreme that 1% of the customers are “whales” who are worth $4,9 million (in post-acquisition value). The other 99% have a combined post-acquisition value of $56K. Knowing what customers are worth is a critical piece of information for the survival of Slack ( For a comprehensive analysis on Slack please look at the great work by Dan McCarthy at Theta Equity, link below). The CLV lens forces Slack to become a highly customer-centric company, as everyone understands the importance of a few but vital customers. Understanding these dynamics help Slack to think about the following questions. What are the common characteristics of this 1 % of customers? Where can we find them? How can we help our sales team to acquire them? How much can we spend on acquisition costs? And to what lengths should we go to keep these customers satisfied? Without the CLV lens, these questions may be answered differently with suboptimal outcomes.

How to apply CLV

CLV provides a quantitative view for marketers and decision-makers, which shed light on the unit economics of the business. Computing CLV per customer is not rocket science; it’s marketing science**. It can be surprisingly straight forward to assess the worth of your customer base by combining standard models and your own financial data. Over the last two decades, validated models** ( for subscription and non-subscription business models) have become publicly available. These models allow for robust and accurate predictions on the customer lifespan, average order value, and purchase frequency. Using the data printed on any invoice, you can model CLV since all you need is a customer id, date, and price over an extended period of time (For one client we simply extracted all invoice data from their financial system and could start modeling right away.) CLV provides a framework for action so that data-driven decisions can become a reality. So if you want to know what customers are worth, you can review the additional sources below or give us a call.


The data from Slack comes from the excellent analysis of Theta Equity you can find here: https://www.thetaequity.com/slack-ipo

** A curated selection of marketing papers is available here: https://paperpile.com/shared/Z0Vi5Q

*** CLV Models implemented in R: https://cran.r-project.org/web/packages/BTYDplus/


Kim, Baek Jung, Vishal Singh, and Russell S. Winer (2017), “The Pareto Rule for Frequently Purchased Packaged Goods: An Empirical Generalization,” Marketing Letters, 28, 4, 491–507, doi:10.1007/s11002-017-9442-5.

McCarthy, Daniel M. and Russell S. Winer (2019), “The Pareto Rule in Marketing Revisited: Is It 80/20 or 70/20?,” Marketing Letters, 30, 2, 139–50, doi:10.1007/s11002-019-09490-y.