Short biography

Daniel McCarthy is an Assistant Professor of Marketing at Emory University’s Goizueta School of Business. He is specialized in applying leading statistical methodology to help marketing departments solve contemporary problems. 

McCarthy’s research focuses on customer lifetime value, solving issues around missing and aggregated data, and the interface for marketing and finance. All these research interests are brought together by the “customer-based corporate valuation,” a methodology that allows companies to calculate their value based on predictive customer behavior.

McCarthy’s work was featured in major publications like Harvard Business Review, Wall Street Journal, FT, Fortune, Barron’s, Inc Magazine, The Economist, CNBC, and CFO Magazine. He won multiple awards such as Don Lehmann, Gary Lilien Practice Prize, MSI Alden G. Clayton, ASA, ISMS.

In 2015, he co-founded Zodiac, a predictive analytics company that Nike acquired in 2018. Subsequently, McCarthy co-founded Theta Equity Partners, a company that specialized in Customer-Based Corporate Valuation®.

customer value optimization

Daniel McCarthy’s contribution to the Customer Value Optimization world

Valuing Subscription-Based Businesses Using Publicly Disclosed Customer Data

By Daniel M. McCarthy, Peter S. Fader, Bruce G.S. Hardie

In this paper, McCarthy, Fader, and Hardie explore the increasing interest in customer acquisition, customer retention, and customer lifetime value among subscription-based businesses. 

The authors show how “customer-based corporate valuation” can be used by companies that want to link their business’s value with their customers’ value. 

The paper suggests a framework that can be used by marketing and accounting departments and show how customer-based corporate valuation work for companies as DISH Network and Sirius XM Holdings.

Customer-Based Corporate Valuation for Publicly Traded Non-contractual Firms

By Daniel M. McCarthy, Peter S. Fader

In this paper, Daniel McCarthy and Peter Fader explore the growing interest in financial valuation based on customer lifetime value for noncontractual companies.

The authors show that noncontractual businesses have more challenges in applying this method compared to subscription-based companies. This happens because transactional patterns like purchase frequency or monetary value are more irregular among noncontractual companies.

The authors apply their methodology “to publicly disclosed data from e-commerce retailers and Wayfair, provide valuation point estimates and valuation intervals for the firms, and compare the unit economics of newly acquired customers.

Scalable Data Fusion with Selection Correction: An Application to Customer Base Analysis

By Daniel Minh McCarthy, Elliot Shin Oblander 

In this paper, Daniel Minh McCarthy and Elliot Shin Oblander apply their method “to estimate a model of customer acquisition and churn” for subscription-based companies. They use public data from Spotify to apply their method and show its efficiency.

We propose an aggregate-disaggregate data fusion method that corrects for selection bias and is both computationally scalable and statistically efficient.

Exploring the Equivalence of Two Common Mixture Models for Duration Data

By Peter S. Fader, Bruce G. S. Hardie, Daniel McCarthy &Ramnath Vaidyanathan

In this paper, the authors explore the beta-geometric (BG) distribution and the Pareto distribution of the second kind (P(II)).

After exploring the models empirically and analytically, they conclude that despite some key differences, “the two models are strikingly similar in terms of their fit and predictive performance as well as their parameter estimates.

Power-weighted densities for time series data

By Daniel McCarthy, Shane T. Jensen

In this paper, Daniel McCarthy and Shane T. Jensen explore the predictive accuracy of time series models. They suggest a new, fast and effective power-weighted density (PWD) approach “to allow for nonstationarity in the parameters of a chosen time series model.” 

“In a financial application to thirty industry portfolios, our PWD method has a significantly favorable predictive performance and draws a number of substantive conclusions about the evolution of the coefficients and the importance of market factors over time.