Optimizing the Playing Field Where the Great Deleveraging Meets Freetopia

Two economic developments are currently having a profound effect on the playing field of consumer demand.  One is the Great Deleveraging: the painful scaling back of the household debt burden that reached a historical peak, at 133% of household income, in late 2007.  The Great Deleveraging means that household dollars that several years ago would have been earmarked for new discretionary spending are instead being diverted to pay down the hangover of old discretionary spending.  As fewer dollars chase the same supply of products we would expect some combination of lower prices and/or a reduction in the quantity of products supplied – a reversal of the SKU proliferation that has been a dominant feature of our consumer experience for the past several decades.

At the same time, though, a second major event appears to be unfolding:  the emergence of the economics of “free,” or “freeconomics” as provocatively described by Chris Anderson of Wired magazine in his recently published book “Free: The Future of a Radical Price.”  “Free” in Anderson’s formulation is the notion that the near-zero cost of doing business online turns upside down the conventional notion of economics as the science of parsimonious choices under conditions of scarcity.  The “economics of abundance” in Anderson’s phraseology may filter through the prism of our traditional understanding of markets as being good news for cash-strapped consumers (more stuff for which I don’t have to pay money) and bad news for suppliers of goods and services (“free” doesn’t sound like a price that will shore up my profit margins). Read the rest of this entry »

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Quantitative Intuition II: The Bayesian Brain’s Achilles Heel

In a previous posting (“Quantitative Intuition: It’s Not Counterintuitive”) I described some of the advancements that have been made in bringing together the disparate worlds of quantitative methods and human intuition, ending on the rather happy note that advanced scientific micromarketing models today are capable of introducing qualitative human judgment and experience into quantitative models, such that the models are able to “learn” from humans about important factors such as competitive threats, nuanced negotiation strategies and even meteorological vagaries – factors that traditionally have been difficult to crunch into the binary 1s and 0s of machine language.  The human brain works in a hierarchical manner, embedding propositions within propositions to think a potentially infinite number of thoughts.   In the example I used in the last posting, a sales rep who reads about a national wholesaler coming to town to open a discount distribution center can nearly instantaneously form a series of mental propositions to evaluate the importance of that news and the probability of potential outcomes that may (or may not) require decisive competitive action from the sales rep’s firm. Read the rest of this entry »

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