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

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.Outgoin

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

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.Outgoin

Automated feature extraction from social media for systematic lead user identification

Manufacturers strive to rapidly develop novel products and offer solutions that meet the emerging customer needs. The Lead User Method, emerging from studies on sources of innovation by the scientific community, offers a validated approach to identify users with innovation ideas to support rapid and successful new product development process. The approach has been more recently applied on online communities, where collection and analysis of rich user data are performed by expert practitioners. In this paper, feature extraction techniques are outlined, that enable automated classification and identification of lead users that are present in online communities. The authors describe two case studies to construct a classification model that is then used to identify online lead users for confectionery products, and to evaluate the outlined feature extraction techniques. The presented research points to opportunities in automated identification within the lead user approach that further reduce the resource and time costs.peerreview_statement: The publishing and review policy for this title is described in its Aims & Scope. aims_and_scope_url: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=ctas20status: publishe