CATERING TO FRINGE POPULATION LEADS TO SUCCESSFUL PRODUCTS IN HIGH TECH, AUTO, DRUGS, SAYS O.R. PAPER (June 19, 2001)

The paper, Design for Defensibility: How Catering to a "Fringe" Population Can Lead to Sustainable New Product Development, is by operations researchers Avi Giloni, Sy Syms School of Business, Yeshiva University, and Sridhar Seshadri and Christopher Tucci of the Leonard N. Stern School of Business, New York University.

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Those Who Hate It the Least
"Under certain demand conditions, pursuing technology that caters to a "fringe" population can actually be an optimal strategy for both established companies and challengers," say the authors.

These conditions exist where market research indicates general agreement about the desirability of a product but wide disagreement about how to improve it.

Identifying a cluster of unhappy customers who might be satisfied with a modified product is the key.

"In a sense, the challenge is: Design me a product so that the people who hate it, hate it the least," says Prof. Seshadri.

An example, say the authors, is the Palm Pilot, which was developed to serve people who wanted a better portable electronic organizer but didn’t want to be burdened with the size and weight of laptops. The resulting Palm Pilot was, in a sense, inferior because it lacked a keyboard and forced users to learn a new form of electronic handwriting script. But its benefits of utility and light weight led to a new market that the Palm company has dominated ever since.

"In pharmaceuticals, Zantac is an example of such a product," says Prof. Tucci. "When more powerful drugs to manage stomach ulcers were first introduced, market research showed a fringe group, the elderly, were more concerned about the medication’s side effects, which included central nervous system effects such as disorientation. GlaxoSmithKline designed their ulcer drug with this group in mind and Zantac became a "second mover" drug that achieved popularity and significant sales."

Design for Defensibility
For existing companies, allocating R&D resources to appeal to the fringe expands their base and also develops the most defensible type of new product — one that avoids head-to-head competition because it is difficult for rivals to develop a similar product rapidly. For newcomers, catering to the fringe can be the most defensible product development strategy under certain demand conditions.

The authors propose that "design for defensibility," or introducing products that anticipate competitors’ reactions, is a good strategy, particularly for firms investing in new technologies.

The authors approached the problem using math modeling techniques, such as optimization and integer programming, that are common to the discipline of operations research.

At the heart of their work is a model of the design decision that represents the differing preferences of customers, multiple product attributes, and constraints on manufacture.

For the operations researcher, a novel feature is solving an optimization problem so that the utility of the lowest 35% of the population is in fact represented as the highest. Similar problems were studied in detail by Prof. Giloni when doing research under the guidance of Prof. Manfred Padberg of New York University.

"As researchers, every day we’re dismissing or minimizing the effect of statistical outliers," explains Prof. Seshadri. "The insight we had was saying, ‘What if we were really interested in identifying outliers? What kind of methodology would we need?’"

Using the authors’ model, a car manufacturer, for example, might scout out preferences for acar from amongst different customer clusters. The preferences would be expressed as the utility derived from a representative model and additional benefit obtained by increasing or decreasing car attributes such as size, gas consumption, acceleration, handling, safety, and color. Then the team would identify constraints that restrict arbitrary choices of these attributes.

Finally, the operations research team would solve a mathematical problem with several objective functions to create multiple designs. These designs would then be compared. The design with the greatest potential for maintaining or gaining maximum market share would be recommended.

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