Product Pre-Launch Prediction From Resilient Distributed e-WOM Data

Abstract

Pre-launch success prediction of a product is a challenge in today's electronic world. Based on this prediction, industries can avoid huge losses by deciding on whether to launch or not to launch a product into the market. We have implemented a Multithreaded Hash join Resilient Distributed Dataset (MHRDD) with a prediction classifier for pre-launch prediction. MHRDD helps to remove the redundancy in the input dataset and improves the performance of the prediction model. Large volume of e-Word of Mouth (e-WOM) data like product reviews, comments and ratings available on internet about products can be used for pre-launch product prediction. In MHRDD, to identify features a distance similarity score is used. In order to remove duplicates, a hash key and join operations are used to create a hash table of significant features. With in-memory computations and hashing on the join operations, this model reduces redundancy of data. This model is scalable and can handle large datasets with good prediction accuracy. This paper presents a novel big data processing method that predicts product success before its launch in the market. Proposed method helps to identify features that are significant for the product to be successful. Based on the pre-launch prediction, companies can reduce cost, effort and time with improved product success.

Artificial intelligence and the conjectural sciences

“…Narayanan and Salganik observe that the term 'prediction' is often applied to machine-driven conjecture about both the past and the future: limits to ML prediction of future events are often around 'measuring input/output states accurately and collecting sufficiently many training examples [both of which] are highly dependent on the nature of the system'. 65 These authors list several other potential limits to the 'predictive' power of automated analyses, both prospective and retrospective. One such limit, these authors suggest, stems from their claim that 'since [statistical] noise tends to accumulate in the forward direction, inverse prediction problems tend to be easier than forward problems'.…”

Potenciación de la resiliencia en empresas y cadenas de suministro a través de la inteligencia artificial: una revisión de la literatura reciente

“…Dentro de las técnicas actuales y mixtas, Herrera-Enríquez et al, (2021) presentan un caso de aplicación de FAHP para priorizar los factores de resiliencia para una ciudad volcánica en Ecuador, donde han logrado mejorar la capacidad resiliente de la ciudad. Por su parte, Narayanan et al, (2020) proponen un modelo para analizar el boca-en-boca electrónico, e-WOM, de las revisiones y comentarios de sitios web para fortalecer el lanzamiento de nuevos productos. Este modelo utiliza diferentes métodos de aprendizaje automático para clasificar si el lanzamiento será exitoso o no.…”