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Forecasting success via early adoptions analysis: A data-driven study

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  • Giulio Rossetti
  • Letizia Milli
  • Fosca Giannotti
  • Dino Pedreschi

Abstract

Innovations are continuously launched over markets, such as new products over the retail market or new artists over the music scene. Some innovations become a success; others don’t. Forecasting which innovations will succeed at the beginning of their lifecycle is hard. In this paper, we provide a data-driven, large-scale account of the existence of a special niche among early adopters, individuals that consistently tend to adopt successful innovations before they reach success: we will call them Hit-Savvy. Hit-Savvy can be discovered in very different markets and retain over time their ability to anticipate the success of innovations. As our second contribution, we devise a predictive analytical process, exploiting Hit-Savvy as signals, which achieves high accuracy in the early-stage prediction of successful innovations, far beyond the reach of state-of-the-art time series forecasting models. Indeed, our findings and predictive model can be fruitfully used to support marketing strategies and product placement.

Suggested Citation

  • Giulio Rossetti & Letizia Milli & Fosca Giannotti & Dino Pedreschi, 2017. "Forecasting success via early adoptions analysis: A data-driven study," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-21, December.
  • Handle: RePEc:plo:pone00:0189096
    DOI: 10.1371/journal.pone.0189096
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    References listed on IDEAS

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    1. Stefano DellaVigna & Devin Pope, 2018. "Predicting Experimental Results: Who Knows What?," Journal of Political Economy, University of Chicago Press, vol. 126(6), pages 2410-2456.
    2. George H. Haines, Jr., 1964. "A Theory of Market Behavior After Innovation," Management Science, INFORMS, vol. 10(4), pages 634-658, July.
    3. Huh, Young Eun & Kim, Sang-Hoon, 2008. "Do early adopters upgrade early? Role of post-adoption behavior in the purchase of next-generation products," Journal of Business Research, Elsevier, vol. 61(1), pages 40-46, January.
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