"From Density to Destiny: Using Spatial Dimension of Sales Data for Early Prediction of New Product Success"
Volume: 23 | Issue: 3 | Pages: 419-428
Publication type: Journal article
Research Archive Topic: Marketing
Research on new product launches has focused on improving the initial go/no-go decision to reduce the probability of post-launch failure. However, early-period assessments are hampered by a lack of data to enable further prediction—the few periods of aggregate sales data available to marketers are not enough on which to base reliable predictions.
In this report, authors Garber, Goldenberg, Libai, and Muller show that managers can use spatial distribution of sales data to obtain a predictive assessment of the success of a new product shortly after launch time. Using diffusion theory, they outline the following scenario:
A firm's external marketing efforts, such as advertising and public relations, persuade early potential adopters to try an innovation. Following that, the internal influence from these early adopters?that is, word-of-mouth and imitation?will determine whether or not the new product succeeds.
Because word-of-mouth is often associated with some level of geographical proximity between the parties involved, "clusters" of adopters will begin to form for successful innovations?even in the early periods after launch. However, if the word-of- mouth and imitation effects are significantly smaller, there will be a more uniform geographical distribution of adopters?the result of a firm's external efforts?rather than the "clusters" of adopters seen when word-of-mouth and imitation effects are strong. Eventually, such a product will be declared a failure. In other words, the product whose sales distribution is "clustered" early on will have a higher likelihood of beginning a "contagion process" and therefore being a success.
Using cross-entropy analysis for simulated and real-life data, the authors find that their model provides a robust, early-period tool for predicting new product success. This study suggests one way in which spatial growth data can be used to better understand and predict new product success over time.
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