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

PLoS One. 2017 Dec 7;12(12):e0189096. doi: 10.1371/journal.pone.0189096. eCollection 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 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.

MeSH terms

  • Diffusion of Innovation*
  • Forecasting
  • Models, Theoretical

Grants and funding

This work was funded by the European Community’s H2020 Program under the funding scheme “FETPROACT-1-2014: Global Systems Science (GSS)", grant agreement #641191 “CIMPLEX Bringing CItizens, Models and Data together in Participatory, Interactive SociaL EX- ploratories", https://www.cimplex-project.eu and under the founding scheme "INFRAIA-1-2014-2015: Research Infrastructures" grant agreement #654024 “SoBigData: Social Mining & Big Data Ecosystem", http://www.sobigdata.eu. There was no additional external funding received for this study.