A Statistical Evaluation of Customer Feedback on Social Media and Its Role in Product Development

Abstract

In today's digital economy, social media is an essential mode by which customers are able to generate feedback with high potential for influencing and informing product innovation lifecycle planning. This study performs a stringent statistical examination of customer feedback gleaned from popular social media platforms—Twitter, Facebook, and Instagram—to quantify its impact on the product innovation and improvement lifecycle. Combining statistical modeling techniques like sentiment analysis, regression modeling, and clustering algorithms, this research examines trends in customer feedback across a data set of over 50,000 feedback records from technology and FMCG industry sectors from 2015 to 2019. Using quantitative techniques, the study explores whether there is a relationship between the type of feedback, speed, direction, and success rate of product improvement. Results indicate a statistically significant correlation (p < 0.01) between real-time feedback patterns and feature release during product iteration cycles. Also, more advanced text mining techniques in conjunction with time series regression extracted predictive indicators of social sentiment variance-based product adoption. The results demonstrate the value of employing mathematically-processed social sentiment to reduce time-to-market and improve customer alignment at development phases. This paper spans the fields of statistics, mathematics, marketing, and computer science and provides a solid interdisciplinary model for data-driven product development.