The September 2006 issue of the Harvard Business Review has a brief piece by Columbia sociology professor Duncan Watts and Steve Hasker of McKinsey about marketing in environments where social influence is important. Watts and Hasker argue that when a consumer’s interest in a given product is driven by how popular the product seems to be with others in the consumer’s social network, predicting whether a product will be a success or not becomes very difficult. To cope effectively with this uncertainty, marketers should spend less time and money trying to predict big-budget blockbusters, and instead develop “portfolios” of products, and the ability to rapidly shift marketing resources to emerging successes based on customer feedback.

The implication for marketing executives is that they should de-emphasize designing, making, and selling would-be hits and focus instead on creating portfolios of products that can be marketed using real-time measurement of and rapid response to consumer feedback.

The aurhors recommend five measures for more effective marketing campaigns which take social network effects into account:

1. Increase the number of bets, and decrease their size
2. Focus on detection, measurement, and feedback
3. Follow through with flexible marketing budgets
4. Exploit naturally emerging social influence
5. Build flexibility into supply chains and contracts

Their results are based on academic work published earlier this year by Watts as well as Matthew Salganik and Peter Dodds: Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market

Read more: Marketing in an Unpredictable World, by Duncan J. Watts and Steve Hasker

UPDATE: I’ve heard from some readers that the Harvard Business Review link didn’t work for them. Here is an alternate link to the paper, hosted by Columbia: Marketing in an Unpredictable World

UPDATE 2: For discussion of a very similar “portfolio” approach to dealing with complex or uncertain environments, this time in the context of business strategy, see my earlier post Strategy in an Unknowable Universe

Those of you who have read the introduction to this site know that the term micromotives comes from Thomas Schelling’s book Micromotives and Macrobehavior. This second term, macrobehavior, denotes the oftentimes surprising and complex group behavior that can emerge from even relatively simple patterns of individual behavior. Columbia sociology professor Duncan Watts is leading practitioner of this approach to the study of social science, and has popularized the dynamics of social networks with his books Six Degrees: The Science of a Connected Age and Small Worlds: The Dynamics of Networks between Order and Randomness. He leads the Collective Dynamics Group at Columbia.

From their overview:

The Collective Dynamics Group is a dedicated research effort, led by Professor Duncan Watts, the unifying theme of which is the application of modern mathematical and computational techniques to problems relevant to the social sciences. Examples of current projects include the structure and evolution of social networks, the dynamics of disease epidemics and cultural fads, the role of social information in financial markets, and the use of the Internet as a tool for social science research. The group, which meets weekly, consists of graduate students and post-doctoral researchers from mathematics, sociology, and economics.

Several of the group’s research projects should have important ramifications for those in the business world.

Interpersonal Influence, Contagion, and Collective Decision Making:

People constantly influence each other in all facets of life. Social contagion is the spreading of ideas, rumors, and behavior through a population via interpersonal influences. Collective decisions are generated by a social contagion process which is (often greatly) augmented by the machinery of mass media. Consequently, understanding interpersonal influence is crucial to understanding the behavior of both individuals and groups. Our projects on influence are divided between online experiments and conceptual mathematical models. We have developed a generalized model of contagion that reconciles and extends previously disparate models of contagion from the social and biological sciences. We are interested in standard biological contagion alone since the collective behavior of people is almost always important in how diseases spread. For example, motivated by observations of the SARS outbreak in 2002, we are exploring the effect on a contagion’s spread due to people moving between subpopulations with some frequency. We are also currently developing an online experiment which will explore interpersonal influence in `cultural markets’ (markets for cultural products, such a books, music, celebrity, etc) and how individual behaviors aggregate to produce collective outcomes.

Social Search, Collective Problem Solving, and Organizational Robustness:

The ability to solve problems collectively is central to the long term stability of any group of people, from a small business marketing a new product to nations confronting global economic crises. Real world collective problem solving is inherently a decentralized, distributed activity. When faced with a novel, ambiguous problem defined at the group level, individuals must determine how to coordinate their actions with others by exchanging ideas, knowledge, and questions. A key aspect of this coordination is search. How do invididuals find others who can at least partially answer or rephrase poorly specified problems? We approach this issue of what we call social search by building conceptual models and online experiments. For example, we have constructed a simple, sociologically plausible model of social networks that shows them to be searchable under general conditions. This is the so-called Small World hypothesis, the notion that two random individuals can find a way to connect to each other through a small number of intermediary contacts. For the past few years, we have been running a global small world experiment, where people send email to friends and acquaintances trying to find a sequence of contacts leading to `target’ individuals. In related work, we model modern organizations as reinforced hierarchical networks of individuals searching for information bearers among their peers. Being effective at collective problem solving leads to a tradeoff between specialized efficiency and flexible robustness.

I haven’t had the opportunity yet to read through any of their findings in detail yet, but I’m looking forwad to it.