Pressure on product development has risen hugely over recent years. Global competition is steadily increasing, intervals between innovations are getting shorter and shorter and companies are more and more in a hurry to put products on the market without delay. At the same time, customers place increasingly high demands on product performance.
Consequently, R&D departments need more feedback from customers - feedback which has to be more detailed and available sooner than usual. Traditional feedback channels, e.g., service departments, are not able to fulfil this need. In many instances, the information which they provide is only available when the third-generation product has been put on the market. Besides, the quality which is required to expose mismatches between technical specifications and consumer requirements is frequently lacking.
The Internet may provide a solution. Product comparison websites and social media, including Twitter and Facebook, are making consumer feedback about products increasingly simple. It is a hopeless task for companies, though, to scan and analyse all feedback manually and convert it into useful information, as it is often only available as plain text. That is why there is a huge demand in the business community for smart and swift tools which can convert large quantities of texts into applicable knowledge.
Unstructured texts with emotion
In cooperation with five manufacturers of end products, the University of Groningen (COPE) and Eindhoven University of Technology have conducted a five-year research into consumer feedback and the ways in which this feedback can be analysed and translated into information. This research was co-financed by the Ministry of Economic Affairs in the framework of the IOP-ICPR research programme.
One of the first conclusions achieved was that much of the feedback not only refers to the product, but also includes service provision. Just think of companies which promise customers a money back guarantee and don't live up to that promise - at least, in the view of the customer who expresses his grievances online. Gradually, therefore, the scope of the research project has been widened beyond feedback about products.
Much feedback appears to consist of brief, unstructured texts without any punctuation, often drenched with emotion, and with many specific terms. Consequently, existing text analysis tools experience great difficulty in unravelling and ranking these texts.