Hear your customer!
Problem being addressed
Online product reviews have become a viable and valuable source for collecting user requirements and preference for product development, but designers lack efficient tools to collect and analyze this data.
A novel approach for capturing changes of user expectation on product affordances based on the online reviews for two generations of products. It allows to automatically identify and structure product affordances from review text, classify preferences of product attributes in several categories, and shows changes of user expectation based on the online reviews posted for two successive generations of products.
Advantages of this solution
Designers can use the proposed approach to evaluate their product improvement strategies for previous products and develop newproduct improvement strategies for future products. Compared with traditional user requirement identification methods such as focus group exercises, interviews the large amount of readily accessible online review data enables designers to identify customer needs in a timely and efficient manner as these online reviews get updated in real time, designers can constantly acquire new feedback at all times.
Solution originally applied in these industries
Possible New Application of the Work
While online reviews provide great source of information for the product designers, marketeers can also benefit from them to adjust their marketing strategy and promotions, since the reviews contain not only the product features but also feedback regarding the price, availability, overall image etc.
Using natural language processing of different postings on social media can help identify significant trends, trace immediate reaction to political campaigns or better understand the popular moods.
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