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).

But is that right?  I would argue differently: the playing field where Freetopia meets the Great Deleveraging presents unique opportunities for enterprises that are able to use scientific methods to figure out the detailed contours of this new environment.  Household dollars are hard to come by.  But there are other things of value that factor into Freetopian economics: things like time, attention and reputation.  The key challenge for organizations is to figure out what these things are, who cares about them, where they fit into the picture and how to quantify them for optimal outcome.

I distill the following principal arguments from Anderson’s work: (a) the cost of doing business online is nearly zero; (b) transactions in Freetopia are not classical binary exchanges between a single buyer and a single seller, but rather involve a mix of parties where the exchange of cash is only a part of the value equation; and (c) some of the parties to the transaction are willing to offer some things for free in exchange for other things that confer some other value notion.  These complex multiparty transactions involve exchanges of product, service, cash, convenience, labor, information, gifts, reputation and awareness. In other words, Freetopia is not synonymous with free lunch (though, enjoyably, we discover in Anderson’s book the origins of this phrase as a value proposition used by San Francisco saloons in the late 1800s: anyone paying for a beer got a “free” lunch to go with it).

What this prompts us to do is to think in new ways about how our customers’ demand curves fit into that complex web of interests.  What are the components of the total cost borne by the customer in any given transaction, and what are the terms of value?  How valuable to the customer is a reduction in the cost of search?  What would induce the customer to pay more for A while getting B and C for nothing, or perhaps bartering a service (such as writing a review or filling out a questionnaire) that would benefit some other party to the transaction who would then subsidize part of the cash price of A to make it more appealing to the customer?  These are the types of opportunities that emerge on this new playing field.

The added complexity posed by these non-traditional transaction webs suggests that going by gut instinct alone will not suffice for organizations trying to figure out how to optimally supply their customers’ demand curves.  Nor, however, will the methods embedded in earlier generations of revenue optimization solutions be up to the task.  As Freetopia moves more into the mainstream of our economic lives the scientific methods that help us uncover the most important insights will need to do more than apply conventional optimization algorithms to historical daily prices.  At Sentrana our focus is on achieving mastery at the micromarket level – disentangling all the variables that connote what matters to a given customer at a given node in a given transaction opportunity.  As we look into the kind of future that Freetopia presages, we see an increased urgency for nuanced clarity and a growing role for scientific micromarketing – not as a one-off management tool but something at the strategic core of making the most from the opportunities this daunting – but potentially lucrative new world – will provide.

Looking at the human mind this way, as a machine constantly evaluating possible outcomes based on prior knowledge and assigning probability weights to those outcomes, gave rise to the notion of the “Bayesian brain,” a term popularized in 1983 by Geoffrey Hinton of the University of Toronto and Terry Sejnowski of Johns Hopkins University.  This notion has subsequently received a good amount of validation by neuroscientists as they continue to make advances in understanding how the brain really works.  Neuroscientists Alexandre Pouget and David Knill of the University of Rochester in 2004 referred to a “growing body of evidence that human perceptual computations are ‘Bayes optimal’ (“The Bayesian brain: the role of uncertainty in neural coding and computation,” Trends in Neurosciences, vol 27 issue 12, December 2004, pp 712-719).  That’s a fancy way of saying that we make estimations based on likelihood – for example we routinely make estimations about things like the distance from us of an object, or the speed at which it is traveling, based on our prior knowledge of the shape, clarity and movement patterns of such objects and the likelihood that the present reality fits into that a priori knowledge.

So far, so good.  But there is an Achilles heel to our hierarchical mental gymnastics.  Briefly, we may be great at the kind of proposition-within-proposition reasoning that our sales rep exhibited in getting to the essence of the competitive threat posed by the wholesaler’s move to town.  But we humans are generally pretty lame when it comes to computation.  Our intuitive reasoning skills fail at the task of instantaneously calculating that 34 x 57 X 71 = 137,598.  One way that we get around this failing is through heuristics – basically, shortcuts that we use in conditions of uncertainty to help us get from information to evaluation and decision.  Take the example above of evaluating the distance a particular object might be from us.  Now, there is a physics formula we could apply to measuring distance, height, momentum (if moving) and so forth to give us the precise answer as to how far away that bicyclist in the yellow jersey is from us.  But our brains lack the ability to do spontaneous physics equations.  Even if we precisely knew one or more of the variables it would be hard to do an on-the-spot computation.  So we need something else – a proxy, a heuristic.  That something might be clarity.  Can we see the bicyclist clearly?  Can we make out the details of his black helmet with red stripes, and the ‘Elf Aquitaine’ logo on the yellow jersey?  That can give us enough information to call upon our “cyclist in a yellow jersey” neuronal connections and infer a likelihood that he is, say, 50 yards away.

The problem with heuristics is that they are subject to error: not occasional lapses in judgment but systematic, predictable biases.  For example if we use clarity as a heuristic we may overestimate the distance of that cyclist from us if there is poor visibility.  Understanding the role of heuristic errors in human judgment and decision making was one of the main contributions of Amos Tversky and Daniel Kahneman to our understanding of behavioral factors in human decision-making (“Judgment Under Uncertainty: Heuristics and Biases,” Science New Series vol 185 no. 4157 Sep 1974 pp 1124-1131).  Tversky and Kahneman documented specific heuristic errors such as representativeness (drawing broad or sweeping conclusions from a limited data set), availability (assigning the likelihood of an event based on the easiest example that comes to mind, whether or not appropriate, and anchoring (relying heavily on one piece of information when making a decision even if it is irrelevant).

Since heuristic errors are part and parcel of human judgment and decision-making under uncertainty, we have to take this reality into account when we attempt to integrate quantitative modeling methods and qualitative human judgment.  What are the best mathematical tools and frameworks to integrate these two domains?  One area in which we at Sentrana are spending considerable time is that of Bayesian hierarchical modeling. The Bayesian approach is particularly useful in marketing situations such as modeling differences in the needs and wants of customers using both generalized and conditional assumptions involving multiple variables. Bayesian frameworks provide a natural way to pool disparate sources of information. A Bayesian model requires the formulation of prior distributions and the estimation of a likelihood function, which can add complexity to the model-building process.  However we expect future insights and innovations in this area, alongside the development of more robust computation capabilities to bring more firepower to bear on this difficult but potentially valuable quantitative approach.