When determining a fair valuation for a company—especially in anticipation of an initial public offering (IPO)—investors often rely heavily on “top down” approaches focusing primarily on traditional financial measures to do so. But what if this approach doesn’t paint the full picture?
Daniel McCarthy, assistant professor of marketing at Emory’s Goizueta Business School, is building the case that augmenting traditional data sources with customer behavior data gives investors a more accurate company valuation.
For the past several years, McCarthy and Peter Fader, professor of marketing at the Wharton School of the University of Pennsylvania, have worked to refine a customer-driven investment methodology they created. “Customer-based corporate valuation (CBCV) simply brings more focus to how individual customer behavior drives the top line,” they explained in “How to Value a Company by Analyzing Its Customers,” an article published in the Harvard Business Review (HBR) earlier this year. “This approach is driving a meaningful shift away from the common but dangerous mindset of ‘growth at all costs,’ towards revenue durability and unit economics—and bringing a much higher degree of precision, accountability, and diagnostic value to the new loyalty economy.”
Fader, McCarthy’s PhD advisor while he was at Wharton, had done some of the seminal work on forecasting customer shopping/purchasing behaviors. This helped build baseline expertise for how one could go about the customer-level modeling. McCarthy recognized that this behavioral modeling could be put to good use in a financial setting, if done the right way.
“There was this untapped source of intellectual property that’s been accumulating within marketing over the last 30 years,” McCarthy said. While other academics have done some conceptual work in the area, none, McCarthy noted, had done so in a way that was consistent with how financial professionals go about performing corporate valuation. McCarthy and Fader merged these well-validated customer-level models with standard corporate valuation methods, then put their resulting valuation tool head-to-head with alternative approaches. They found that their CBCV model subsequently outperformed.
A full article on this subject is attached, within it, you will find key CBCV highlights such as:
- Using unit economics to more accurately predict revenue forecasts
- Gaining access to the right data
- The CBCV model is also good for managers and for customers
- Working to have publicly traded companies adopt CBCV
McCarthy’s work on the CBCV methodology has earned him a number of awards, including the MSI Alden G. Clayton, American Statistical Association, INFORMS, and the Shankar-Spiegel dissertation awards.
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Daniel McCarthy is an Assistant Professor of Marketing at Emory University's Goizueta School of Business where his research specialty is the application of leading-edge statistical methodology to contemporary empirical marketing problems. If you are looking to contact Daniel – simply click on his icon now to arrange an interview today.
Daniel McCarthy Assistant Professor of Marketing
Marketing expert focused upon methodologies and frameworks for predicting customer behavior to better understand firm-level outcomes