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 donâ 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.

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

Giannotti, Fosca;
2017

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 donâ 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.
Settore INF/01 - Informatica
Forecasting; Models; Theoretical; Diffusion of Innovation; Biochemistry; Genetics and Molecular Biology (all); Agricultural and Biological Sciences (all)
Horizon 2020
871042
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/114492
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