From Density to Destiny: Using Spatial Dimension of Sales Data for Early Prediction of New Product Success

• Published in: Marketing science (Providence, R.I.) R.I.), 2004-07, Vol.23 (3), p.419-428
• Main Authors: Garber, Tal, Goldenberg, Jacob, Libai, Barak, Muller, Eitan
• Format: Article
• Language: English
• Subjects: Business; Cluster analysis; complexity; Corn; Diffusion processes; Distribution; Economic analysis; Entropy; Geodemographics; Innovation adoption; Innovation diffusion; Innovations; Logistic regression; Market segmentation; Marketing; Mathematical models; Methods; new products; Product development; Product differentiation; Product innovation; Product introduction; Sales; Sales forecasting; Space; spatial analysis; Spatial analysis (Statistics); Spatial dimensions; Spatial distribution; Studies; Success; Takeoff; Test marketing; Time series; Word of mouth advertising
• ISSN: 0732-2399; 1526-548X
• DOI: 10.1287/mksc.1040.0051

One of the main problems associated with early-period assessment of new product success is the lack of sufficient sales data to enable reliable predictions. We show that managers can use spatial dimension of sales data to obtain a predictive assessment of the success of a new product shortly after launch time. Based on diffusion theory, we expect that for many innovative products, word of mouth and imitation play a significant role in the success of an innovation. Because word-of-mouth spread is often associated with some level of geographical proximity between the parties involved, one can expect "clusters" of adopters to begin to form. Alternatively, if the market reaction is widespread reluctance to adopt the new product, then the word-of-mouth effect is expected to be significantly smaller, leading to a more uniform pattern of sales (assuming that there are no external reasons for clustering). Hence, the less uniform a product's distribution, the higher its likelihood of generating a "contagion process" and therefore of being a success. This is also true if the underlying baseline distribution is nonuniform, as long as it is an empirical distribution known to the firm. We use a spatial divergence approach based on cross-entropy divergence measures to determine the "distance" between two distribution functions. Using both simulated and real-life data, we find that this approach has been capable of predicting success in the beginning of the adoption process, correctly predicting 14 of 16 actual product introductions in two product categories. We also discuss the limitations of our approach, among them the possible confusion between natural formation of geodemographic clusters and word-of-mouth-based clusters.