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.

Keywords

• Big data analytics
• product pre-launch prediction
• resilient distributed dataset
• redundancy elimination