In 2021, initial public offerings (or IPOs) hit an all-time record with 1,000 companies entering the scene, more the doubling the previous year. From investors to managers to board members, business leaders need to understand the true value of companies, but many of the traditional valuation methods are outdated and incomplete. With increasing access to new data, astute forecasters are deploying new methodologies. Among these is customer-based corporate valuation, a field “equal parts marketing and Wall Street” with a central focus on how customer behavior drives success.  

Dan McCarthy joins to discuss customer-based corporate valuation, including which customer data points are most important to monitor and how investors and managers stand to benefit from this approach.  

Dan is an Assistant Professor of Marketing at Goizueta Business School. His research centers on customer lifetime value, limited data problems, data privacy, and the marketing-finance interface. He is regularly featured as a key expert, with recent coverage in the Harvard Business Review, Wall Street Journal, Fortune, the Economist, and CNBC. 

Corporate Valuation as a New Approach to Forecasting 

Until now, evaluating firms has been a question of forecasting future revenues off of past revenues. Customer-based corporate valuation (CBCV) entails looking at data regarding the flow of customer acquisitions over time. 

 

The model consists of four interlocking submodels governing how each customer of a firm will behave. They are: 

Using this data, predictive models for customer behavior produce forecasts – of revenues, as well as marketing expenses and ultimately cash flows. It’s Wall Street meets marketing. 

 

A Perfect Mix of Finance, Statistics, and Marketing 

McCarthy is the founder of two predictive customer analytics companies, one of which was acquired by Nike in 2018. Along with Peter Fader at Wharton, he has spent countless hours studying and working to refine CBCV over the last several years. His journey into this method combines many of his passions, including finance, statistics, and marketing.   

 

He shares that predictive and analytical tools, such as his CBCV, can be adapted for a multitude of uses for a variety of audiences, including investors, managers, CEOs, and marketing departments. Those business leaders with access to heavy data can receive detailed predictive information that can be leveraged for future decision making. It isn’t as simple as, “this is how we will perform”, but rather, McCarthy’s tool allows companies to understand the pieces that make them thrive. They can then use this information, such as which customers are more valuable or which marketing tactic is working best, to progress the company and drive growth. This approach, he says, is more of a value management task rather than a value measurement task.  

 

Even amateur investors with statistical aptitude can apply this approach. McCarthy shares the following Excel spreadsheet to make predictions for subscription-based firms.  

 

Corporate Valuation with Movie Pass and Wayfair 

McCarthy applies the CBCV methodology across industries. He recently evaluated the new three-tier system that Movie Pass implemented earlier this year. MoviePass skyrocketed in popularity in 2018 after it lowered its monthly subscription to $10, garnering 3 million subscribers. But it wasn't sustainable. After burning through hundreds of millions of dollars, MoviePass shut down in 2019 and its parent companies filed for bankruptcy in 2020. Now, Movie Pass just re-launched on Labor Day with a three-tiered payment system. So, what does McCarthy predict? In short, he shares, they likely won’t “lose money as quickly”. He predicts that most customers will head for the cheapest option available as they have already experienced this company’s market adoption. It's the proverbial free lunch. The national average price of movie tickets is approximately $10. So if the cost of the subscription is $10, then as long as someone buys one movie, the company's going to lose money.  

 

McCarthy’s work with ecommerce furniture site Wayfair also underscores the importance of understanding consumer behaviors to accurately interpret company financials. Though its revenue has grown very quickly, he says, there remains a very low margin for profit. A deeper look into consumer behaviors shows that Wayfair has high customer acquisition costs, yet these customers are rarely repeat purchasers. Though the company had been struggling, the COVID-19 pandemic forced buyers into their homes exclusively, and heads turned to ecommerce platforms such as Wayfair. This change helped the company sustain itself for a short period, but it is now realizing the harsh reality of its flawed marketing tactics once again.  

 

Standardization of Data and Definitions 

One of the biggest challenges that CBCV faces is the disclosure of fully accurate and thorough data, or rather, the lack thereof. Companies are often reluctant to disclose data that may make them look bad, limiting the data sets predictive models can work with. Standardization of industry terminology is also a challenge, because different firms utilize different definitions, and this can lead to the misinterpretation of data. As investors apply pressure, standards are slowly forming. While he recognizes the challenges, McCarthy is hopeful we can converge on “industry specific, informal standards”. 

 

Up Next for Dan McCarthy 

McCarthy is enthusiastic about what’s in store as he continues to refine predictive data models. He looks forward to applying artificial intelligence and other deep learning models to this work. He is grateful to Kyeongbin Kim, PhD candidate in marketing at Goizueta, for her transformational research in this space.  

 

To learn more about Goizueta Business School and how principled leaders are positively influencing business and society, visit www.goizueta.emory.edu.