Owing to continuous advances in information technology, access to information via the Internet and the steady decline of cost related to data creation, big amounts of customer data now reside in many companies. This data is said to hold a large amount of valuable knowledge that could be used to design customer-focused products, a key factor for maintaining market-share. Information overload hinders the search for knowledge and, therefore, it is a challenge for companies to identify what is relevant to analyse. Different approaches based on data mining tools of web-based customer data have been shown to be useful for gaining customer insight. However, this information is not properly factored into the target setting process. Many improvements in modelling the relation between product performance and customer satisfaction during the target setting have been presented. However, these still rely on customer information obtained from traditional gathering techniques such as questionnaires, which do not provide enough valuable and deep customer information; therefore, designers are forced to make assumptions. While some studies highlight the potential of customer data as an aid to designing future product generations, they do not provide enough details on how such information should be processed to generate valuable information for the designers. 

By taking advantage of the generated customer data, this work aims to increase the reliability of the design decisions on product specifications by reducing the existing gap between the customer and the designer world. To do so, customer information from different sources such as surveys and usage data have been combined to model customer satisfaction as a function of design requirements. In this process, customer needs are defined at a detailed level to be able to link customer satisfaction with a clear interface to the design requirements. By means of usage data, customer-product interaction in the customer environment is investigated, and differences between designer assumptions and customer picture are calculated towards the target fulfilment indicator. Results show that the work presented helps designers to set targets towards a higher customer focus, since customer needs and way of using the product become visible in the process. This allows the design team not only to identify differences among customers but also the possibility to detect changes in customer needs. The target fulfilment indicator acts as a feedback channel for continuous product improvement, allowing designers to validate their decisions. Since the voice of the customer drives the process, the presented approaches guide the design team towards the most relevant customer data, thus streamlining the design process in a situation where the amount of information rapidly increases. 

To develop products through a customer-centric strategy, early stages of product development such as target setting play an important role. In the target setting stage Customer Needs (CN) are gathered and translated into Design Requirements (DR) in order to subsequently set product targets that fit cost constraints and at the same time result in high Customer Satisfaction (CS). Continuous advances in information technology create new opportunities for companies to gather information about the customer, for example, for marketing purposes, or to assess customer reactions after the launch of new products. In addition, products are becoming complex systems that are successively equipped with more software and sensors offering opportunities for collecting data on how they are used. Knowing how customers use the product enhances a company’s ability to segment customers and customize products.

Despite customer information availability from different sources (sensors, social media, etc.), surveys and focus groups are considered today as the main data source to derive the set of CN statements during target setting. Further, the team’s interpretation of CNs, which are often described in abstract language, must be translated into DRs, which are described in a more technical language. Hence, the translation process of CNs into DRs is said to be subjective. To set product targets, CS sensitivity to changes in DR levels is also considered. Surveys and benchmarking data containing customer perceptions on competitors’ performance are often the main customer data input into the process. While insightful information may be obtained, surveys are costly and time consuming and only encompass a small part of the market population.

The research presented in this doctoral thesis explores how customer information obtained from sensors (e.g. product usage data) and text data (e.g. from websites, open-survey questionnaires) can be factored in the target setting process before concept generation to enhance customer focus without compromising product development time. The aim is to increase designers’ awareness of target population and in turn increase the quality of the design decisions on product targets. For this purpose, a customer-focused data-driven target setting methodology is proposed. The presented methodology changes the actual target setting methodology by means of indicators and autonomous activities on those parts of the process where marketing or design decisions are needed. The proposed methodology gives the incentive for a more integrated product development where marketing and designers need to work closely. This further allows a sustainable customer information gathering strategy that strives for missing customer information that is required for setting product targets. The indicators act as feedback channels for continuous product improvement. The use of such indicators and autonomous activities highlights the potential of a more efficient, less subjective and higher-quality target setting process.