Konferensartikel

In Kansei Engineering (KE); customers are asked for their emotional response to a range of products. A representative sample of customers is sought using focus groups; face to face on-site interviews and postal/e-mail contact. The choice of products is best made using a designed experiment of design factors. Customers are asked to rate the products on a number of carefully chosen semantic scales. KE typically produces 3 dimensional data with customers; products and emotional response via semantic scales as the dimensions; see for example (van-Lottum et al; 2006). KE questions are effectively in the form of conjoint analysis (see; for example Malhotra; 1996) in which customers are asked for their response to the factors in the context of the product; rather than singly. For example; a product may incorporate the 3 factors of colour; size and shape and the customer is asked to respond to the whole product rather than to a single factor of colour. Photographs or graphical representations are often used if it is inconvenient to demonstrate the product in real life. Usually this does not cause problems (Pearce et al; 2007). Additional questions may ask about buying behaviour including recency; frequency and value of purchases.

Analysing the customer responses to the products can yield insight into the importance of the design factors and the relationships between emotional response and design factors. These relationships are the key to the importance of KE to the design process both in guiding design and in providing a broad portfolio of products. The design factors are derived from various sources; including designers and merchandising staff. Data mining and customer segmentation based on recency; frequency and value of purchases can also indicate which design factors are important. KE is expensive to do properly; so it is particularly important to prepare the groundwork carefully. This paper investigates what information can be obtained from data mining sales data as a pre-cursor to Kansei Engineering.

Kansei Engineering; data-mining; analysis of means