Many enterprises devote a significant portion of their budget to new product development (NPD) and marketing to make their products distinctive from those of competitors, and to know better the needs and expectations of consumers. Hence, knowledge and suggestions on customer demand and consumption experience has become an important information and asset for enterprises. Inferring user search goals are very important in improving the efficiency. For this, feedbacks are obtained from the customer. The submitted feedbacks are clustered as feedback sessions. Pseudo-documents are generated to better understand the clustered feedbacks. K-means clustering algorithm is used to cluster the feedbacks. These feedbacks are very useful in development of new product. Ranking model is used to provide ranks to the products based on the customer feedbacks. Hence knowledge and feedback from customers has become important information. Product design is integrated with the knowledge of customers. Users may also pose their questions about the products which are added when it is suitable. Hence customer behaviour can be analysed from their posed questions and response. Finally, evaluation criterion is described to evaluate the performance of new product.

Uncertainty is prevalent in a wide range of real-world issues. The fuzzy sets, vague sets or intuitionistic fuzzy sets are widely used in recent years for decision making and various analysis where uncertainty is predominant. An extension of fuzzy sets is Hesitant Fuzzy Sets, which deals with ambiguous situations that arise when determining an element's membership degree in a set. Researchers have defined various ideas, extensions, aggregation operators, and measurements to deal with reluctant information as a result of this new approach. Machine leaning algorithms are also exploiting hesitant fuzzy sets for better decision making.