Eliminating The Noise In Partnership Valuation

BY ADAM GROSSMAN

In their recent book Noise: A Flaw in Human Judgment, authors Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein focus on “how chance variability of judgments” impacts decision making in multiple contexts. We can apply lessons learned from Noise directly to partnership valuations.

 While all of the authors are famous in their fields, Kahneman is arguably the most famous living psychologist in the world today. He is most famous for the research he completed with psychologist Amos Tversky published in the book Thinking Fast and Slow for which Kahneman won a Nobel Prize in Economics.

Michael Lewis featured how then Houston Rockets general manager (and current Philadelphia 76ers President) Daryl Morey applied lessons learn from Kahneman and Tversky in The Undoing Project: A Friendship That Changed Our Minds. In particular, Lewis focuses on Morey’s draft process in relation to National Basketball Association (NBA) prospects in which he tried to mitigate judgement variability.

The primary reason Morey and his team used this approach is that there was too much noise when it came to the evaluating players. For example, members of his staff would have different responses when interviewing prospects ages 18-22 a few times (or only one time) from both a positive and negative perspective. Morey recognized both his team and he could be too likely swayed at times by interviews in ways that did not predict future success.

Morey’s experience is echoes Kahneman’s subsequent research on noise. He looked at professionals in organization including, “appraisers in credit-rating agencies, physicians in emergency rooms, underwriters of loans and insurance, and others.” He found that, “the problem is that humans are unreliable decision makers; their judgments are strongly influenced by irrelevant factors, such as their current mood, the time since their last meal, and the weather.”

While this seems like a complex problem, Kahneman’s solution is actually relatively simple. He and others have found that even “simple statistical algorithms” are more accurate than experts because they are “noise-free.” More specifically, “superior consistency allows even simple and imperfect algorithms to achieve greater accuracy than human professionals.”

Kahneman’s thesis is one reason why we developed the Corporate Asset Valuation Model (CAV). While it is not a “simple” algorithm, CAV does standardize the process for partnership valuation across all channels. By using a uniform approach to all partnership asset types, CAV enables our clients to have greater accuracy in partnership valuation. 

This is critical particularly as venues across the sports landscape continue to open to fans. More specifically, a common question we receive is how can partnership buyers, sellers, and agencies compare in-venue, hospitality, and experiential assets with social, digital, or traditional media assets. These different assets would seem to require a different approach and could introduce significant noise into a valuation process.

The CAV standardizes the process while creating company-specific valuations. More specifically, we look at each company’s specific return on investment (ROI) and return on objectives (ROO) goals. We then break each partnership asset down to evaluate how successfully it helps companies achieve those goals. Focusing on what an asset does versus what an asset is enables us to achieve consistency across multi-channel partnerships.

To clarify, there is clearly a significant place for human judgment in creating effective partnerships in multiple contexts. In particular, human expertise should help to develop and streamline partnership strategy from both a buyer and seller perspective. In addition, leveraging human creativity to develop brand narratives and activate partnerships in ways that maximize value is critical.

It is measuring the effectiveness of partnerships where technology, analytics, and data can play an increasingly important role. The CAV provides the partnership valuation framework to reduce noise and provide more accurate valuations.