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

نویسندگان

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

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.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Forecasting Ozone Density in Tehran Air Using a Smart Data-Driven Approach

Introduction: As a metropolitan area in Iran, Tehran is exposed to damage from air pollution due to its large population and pollutants from various sources. Accordingly, research on damage induced by air pollution in this city seems necessary. The main purpose of this study was to forecast ozone in the city of Tehran. Considering the hazards of ozone (O3) gas on human health and the environmen...

متن کامل

User Satisfaction as the Foundation of the Success Following an ERP Adoption: An Empirical Study from Latin America

Enterprise Resource Planning (ERP) adoptions keep consolidating as a critical IT initiative in developing regions. Although Latin America has exhibited lately the largest growth in terms of ERP adoption rate worldwide, there is a gap in the literature focused in examining the success and underlying causes of such adoptions there. This study develops and tests a theoretical model proposing facto...

متن کامل

The Bass diffusion model on networks with correlations and inhomogeneous advertising

The Bass model, which is an effective forecasting tool for innovation diffusion based on large collections of empirical data, assumes an homogeneous diffusion process. We introduce a network structure into this model and we investigate numerically the dynamics in the case of networks with link density P (k) = c/k , where k = 1, . . . , N . The resulting curve of the total adoptions in time is q...

متن کامل

Enhancing Learning from Imbalanced Classes via Data Preprocessing: A Data-Driven Application in Metabolomics Data Mining

This paper presents a data mining application in metabolomics. It aims at building an enhanced machine learning classifier that can be used for diagnosing cachexia syndrome and identifying its involved biomarkers. To achieve this goal, a data-driven analysis is carried out using a public dataset consisting of 1H-NMR metabolite profile. This dataset suffers from the problem of imbalanced classes...

متن کامل

Jacob Goldenberg , Sangman Han , Donald R . Lehmann , & Jae Weon Hong The Role of Hubs in the Adoption Process

The authors explore the role of hubs (people with an exceptionally large number of social ties) in diffusion and adoption. Using data on a large network with multiple adoptions, they identify two types of hubs—innovative and follower hubs. Contrary to recent arguments, hubs tend to adopt earlier in the diffusion process, even though they are not necessarily innovative. Although innovative hubs ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره 12  شماره 

صفحات  -

تاریخ انتشار 2017