Article

Application of multidimensional scaling for marketing-mix modification: A case study on mobile phone category

Authors:
  • Gyeonggi Institute of Science & Technology Promotion
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Abstract

This paper introduces an application of multidimensional scaling for marketing-mix modification of products at the maturity stage of product life cycle. The application is based upon the concept that marketers would benefit from mining the consumer behaviors to assist their marketing decisions. Using the preference map of multidimensional scaling method and influence-satisfaction matrix, the proposed methodology promotes marketers to analyze their current level of consumer satisfaction in the market and modify the marketing-mix based upon the current consumer preference and satisfaction.The proposed methodology adopts multidimensional preference (MDPREF) algorithm, which is designed to do multidimensional scaling of preference or evaluation data. While most applications with the multidimensional scaling have been to understand the position of a set of brands or products, the proposed methodology with MDPREF algorithm is to understand the similarities and dissimilarities among a set of marketing elements of a brand in consumer’s perception. A case example for the mobile phone category is provided.

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... So the new product will replace the old product by product functions, and the new product will push customers to adapt them by price strategy (Fishman & Rob, 2002). Some research analyzed the position of brands/products for mobile phone market by the preference map of multidimensional scaling method and influence-satisfaction matrix (Cha, Kim, & Lee, 2009). In order to satisfy customers' heterogeneous needs and provide mass customization manufactures, some research made a lot of effort on the recommendation system of classification-based for personalization purchase of mobile phone (Lee & Kwon, 2008;Zhang & Jiao, 2007). ...
... Therefore, the phenomenon pushes enterprises need to understand customers' preferences in the diverse mobile phone market. A market position model of product/brand considered that the aspects (marketing mix) should include product, price, distribution, advertising and sales promotion (Cha et al., 2009). A selection model of value-added services for system operators suggest that the aspect of service function included entertainment, transaction, information, and communication (Kuo & Chen, 2006). ...
... These studies give us an important idea of customer preferences of product image. Product aspect should include tangible characteristics and intangible characteristics, and advertising aspect should include advertising contents, advertising media, and advertising copy/campaign (Cha et al., 2009). A research of product design for mobile phone considered product form features should include body (length, width, thickness, volume and type), function button (type, style), number button (shape, arrangement) and panel (detail treatment) and analyze the customer' needs of product designs for different styles users (plain, sports, female, simplicity and business) (Shieh & Yang, 2008). ...
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... Today, due to dynamically changing customer desires as well as rapid technology evolution, mapping customer needs with optimal configuration of product features have become much more challenging than before ( Wang 2013). For instance, owing to market saturation in smart phones, Nokia and Motorola lost huge market shares and were merged by Microsoft and Google, respectively (Cha, Kim, and Lee 2009;Oküdan, Chiu, and Kim 2013). In contrast, Apple and Samsung have reported their brilliant revenues resulting from hot-selling smart phones, smart pads and other consumer electronic products. ...
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... MDS was used to investigate the relationships in the corpora. Borg and Groenen (1997) argue that MDS is a statistical method developed to detect in a visual way the complex patterns in high-dimensional datasets. By arranging points in space according to similarities between different objects, MDS displays as two or three-dimensional geometrical pictures the structure of distance-type data (Cha et al., 2009). ...
... Borg and Groenen (1997) argue that MDS is a statistical method developed to detect in a visual way the complex patterns in high-dimensional datasets. By arranging points in space according to similarities between different objects, MDS displays as two or three-dimensional geometrical pictures the structure of distance-type data (Cha et al., 2009). Thus, similar sub-corpora are placed near each other on the map, while dissimilar sub-corpora are placed far away from each other. ...
... In order to achieve successful new product development in a crowded and fragmented market, product positioning and product recommendation are two critical techniques to help firms better understand the underlying relationships between product features, competing alternatives and diverse consumers (Lilien and Rangaswamy 2003; Petiot and Grognet 2006; Cha, Kim, and Lee 2009). Specifically, product positioning is not what you do to a product but it is what you do to the prospect of customers. ...
... Optimal product positioning corresponds to determining which attributes and associated levels should be configured in product varieties to satisfy customer requirements (Kwong, Luo, and Tang 2011). In practice, product positioning is implemented through a series of steps: (1) visualising competitive alternatives and product features in a low-dimensional space, (2) constructing a predictive model to forecast how potential buyers will react to marketing stimulus and (3) determining the optimal position of new product (s) and identifying the niche segment(s) to run a firm's business (Cha, Kim, and Lee 2009). Specifically, a perceptual map is a powerful tool to visualise the underlying relationships between the competing products (brands) and associated features (criteria) in a lower dimensional space. ...
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