Evaluating online customer data helpfulness to set targets: a QFD perspective

Abstract

Retrieving knowledge and useful information from customers is crucial to develop customer-focused products and maintain the market share. With the rapid growth of the Internet, the ability of users to create and publish content has generated a wealth of product information from customers’ point of view. Given the abundance of large scale, publicly available data social media can enable novel social ways of providing and receiving feedback from new products and concepts. In order to avoid information overload, identifying and analyzing helpful reviews has become a critical challenge. Identifying helpful online reviews and learning how to extract valuable data from product design perspective has become a crucial task due to the existing information overload –identifying what is relevant to analyze is a key task for companies. Existing studies have focused on identifying variables that affect the perceived helpfulness of an online comment. To the best author’s knowledge, actual studies about helpfulness do not consider the Quality Function Deployment perspective on evaluating to what extend the customer data from social media is helpful to set objective targets. The thesis aims to evaluate social media data helpfulness from the designer’s perspective taking as basis QFD. Evaluating this, the work hypothesis is that the helpfulness definition has to move beyond, taking into consideration what is needed to build The House of Quality, a key tool in product design. To do so, an exploratory analysis of real public data from Twitter, Facebook and iMore forum is taken as basis. The purpose of undertaking exploratory research is primarily to investigate and to identify if the proposed variables for defining review’s helpfulness currently existing in the literature review can help designers in target setting within a QFD perspective The presented thesis shows that to go further within target setting is needed to have the QFD perspective: not all current exposed variables do not help to explain online reviews helpfulness.