Word-of-mouth and dynamical inhomogeneous markets: Efficiency measure and optimal sampling policies for the pre-launch stage

TL;DR

This paper models the diffusion of innovative products in inhomogeneous markets, analyzing how spatial factors and sampling policies affect pre-launch success and revealing unexpected failures of word-of-mouth at high densities.

Contribution

It introduces a spatially explicit micro-level model for innovation diffusion, highlighting inhomogeneity effects and optimal sampling strategies in the pre-launch phase.

Findings

Word-of-mouth can fail in high-density markets.

Optimal sampling policies depend on market spatial structure.

Pre-launch campaign length correlates with firm expenditure.

Abstract

An important assumption lying behind innovation diffusion models and word-of-mouth processes is that of homogeneous mixing: at any time, the individuals making up the market are uniformly distributed in space. When the geographical parameters of the market, such as its area extension, become important, the movement of individuals must be explicitly taken into account. The authors introduce a model for a "micro-level" process for the diffusion of an innovative product, based on a word-of-mouth mechanism, and they explicitly consider the inhomogeneity of markets and the spatial extent of the geographical region where the process takes place. This results in an unexpected behaviour of macro (aggregate) level measurable quantities. The authors study the particular case of the pre-launch stage, where a product is first presented to the market through free sample distribution. The first triers of the samples then inform the other potential customers via word-of-mouth; additional advertising is absent. The authors find an unexpected general failure of the word-of-mouth mechanism for high market densities and they obtain quantitative results for the optimal sampling policy. By introducing a threshold to discriminate between individuals who will purchase and those who will not purchase according to their individual goodwill, they calculate the length of the pre-launch campaign and the final goodwill as a function of the firm's expenditure. These results are applied to a set of major US urban areas.