A UNIFIED FRAMEWORK OF TARGETED MARKETING USING CUSTOMER PREFERENCES

One of the fundamental tasks of targeted marketing is to elicit associations between customers and products. Based on the results from information retrieval and utility theory, this article proposes a unified framework of targeted marketing. The customer judgments of products are formally described by preference relations and the connections of customers and products are quantitatively measured by market value functions. Two marketing strategies, known as the customer‐oriented and product‐oriented marketing strategies, are investigated. Four marketing models are introduced and examined. They represent, respectively, the relationships between a group of customers and a group of products, between a group of customers and a single product, between a single customer and a group of products, and between a single customer and a single product. Linear and bilinear market value functions are suggested and studied. The required parameters of a market value function can be estimated by exploring three types of information, namely, customer profiles, product profiles, and transaction data. Experiments on a real‐world data set are performed to demonstrate the effectiveness of the proposed framework.     

Feature-based recommendations for one-to-one marketing

Most recommendation systems face challenges from products that change with time, such as popular or seasonal products, since traditional market basket analysis or collaborative filtering analysis are unable to recommend new products to customers due to the fact that the products are not yet purchased by customers. Although the recommendation systems can find customer groups that have similar interests as target customers, brand new products often lack ratings and comments. Similarly, products that are less often purchased, such as furniture and home appliances, have fewer records of ratings; therefore, the chances of being recommended are often lower. This research attempts to analyze customers' purchasing behaviors based on product features from transaction records and product feature databases. Customers' preferences toward particular features of products are analyzed and then rules of customer interest profiles are thus drawn in order to recommend customers products that have potential attraction with customers. The advantage of this research is its ability of recommending to customers brand new products or rarely purchased products as long as they fit customer interest profiles; a deduction which traditional market basket analysis and collaborative filtering methods are unable to do. This research uses a two-stage clustering technique to find customers that have similar interests as target customers and recommend products to fit customers' potential requirements. Customers' interest profiles can explain recommendation results and the interests on particular features of products can be referenced for product development, while a one-to-one marketing strategy can improve profitability for companies.     

Energizing business transactions in virtual worlds: an empirical study of consumers’ purchasing behaviors

Virtual marketplaces for products and services have become major profit sources in virtual worlds (VWs). The large quantity and growth of virtual product transactions and their platform providers’ profits have made it critical to understand consumer purchasing behavior in VWs. However, as open-ended VWs such as Second Life have environments that differ from those of other online communities, the underlying mechanisms of consumers’ e-commerce behavior may not explain their VW behavior. Therefore, this study examines consumers’ VW behavior by considering three categories of factors influencing their purchasing behavior: the platform context (i.e., technical characteristics such as interactivity and vividness and social characteristics such as involvement), product context (i.e., product value), and virtual experience (i.e., flow and satisfaction). This study examines how these factors affect consumers when they purchase virtual products. Its results highlight the importance of flow experience in consumers’ VW behavior. Interactivity, vividness, and involvement are found to affect consumers’ virtual experience—flow, and involvement exhibits a significantly stronger influence on flow. Flow and involvement are found to affect product value, and flow exerts a stronger influence than involvement on product value. Flow and product value directly impact consumers’ willingness to purchase, whereas satisfaction with the virtual world experience, which is significantly affected by flow, is not associated with willingness to purchase. The results further indicate that product value is more influential on willingness to purchase than is flow. After describing the study’s contributions to both research and practice, I conclude the paper by presenting avenues for future research.     

Eliciting Customer Preferences for Products From Navigation Behavior on the Web: A Multicriteria Decision Approach With Implicit Feedback

The goal of raising customer loyalty in electronic commerce requires an emphasis on one-to-one marketing and personalized services. To this end, it is essential to understand individual customer preferences for products. In this paper, we present a method for identifying customer preferences and recommending the most appropriate product. The identification and recommendation of such products are all based on the use of customer's real-time web usage behavior, including activities such as viewing, basket placement, and purchasing of products. Therefore, in this approach, we do not force a customer to explicitly express his or her preference information for particular products but rather capture his or her preferences from data that result from such activities. Information on the web usage behavior for the products determines the ordinal relationships among the products, which express that certain product is preferred to other products across the multiple aspects. The ordinal relationships among the products and the multiple aspects of products lead to the consideration of a multiple-criteria decision-making approach. Thus, the problem eventually results in the identification of weights attached to the multiple criteria in the multidimensional preference space constructed by the ordinal relationships among the products. The derived weights are then used for the prioritization of products that are not included in the navigation behavior due to factors such as time pressure, cognitive burden, and the like.     

马尔柯夫链在陶瓷产品市场预测与决策中的应用

对销售市场来说,影响陶瓷产品销售的因素是多方面的,有自身的因素,同时还有外界的随机因素,这些因素会造成陶瓷商品销售量很大的波动幅度,用简单的回归方法难以预测,而马尔柯夫链不受上述因素的影响。本文利用马尔柯夫链理论对陶瓷产品销售市场的未来趋势进行建模,分析陶瓷产品在市场上的状态分布,得到了比较稳定的预测值,从而为提高陶瓷企业的市场竞争力、预测能力及其产品的市场占有率提供了理论依据。     

Discovering patterns of online purchasing behaviour and a new‐product‐launch strategy

Spurred by rapid development of computers and Internet technology, online shopping is gradually overtaking in‐store shopping, because of advantages such as convenience, more choice of products or services etc. Online stores must devote a great deal of time and resources to locating and attracting new customers. Growing a customer base requires first understanding customers and then providing the products or services they need, thus encouraging customers to purchase more. This paper develops a system to analyse customers’ purchasing behaviour and track shifts in their interests. Customers’ purchasing behaviour is measured using proposed standard product loyalty status and standard brand loyalty status. Using these metrics, together with the preference map established for each customer, a marketing specialist can easily locate potential customers to target when a company launches a new product. The new‐product‐launch strategy proposed in this paper can be used to create a list of potential customers for a product being launched under a variety of conditions. A prototype system has been built to test the feasibility of the proposed new‐product‐launch strategy. The result shows almost 40% of potential customers respond to the recommendation positively.     

Product development with data mining techniques: A case on design of digital camera

Many enterprises have been devoting a significant portion of their budget to product development in order to distinguish their products from those of their competitors and to make them better fit the needs and wants of customers. Hence, businesses should develop product designing that could satisfy the customers’ requirements since this will increase the enterprise’s competitiveness and it is an essential criterion to earning higher loyalties and profits. This paper investigates the following research issues in the development of new digital camera products: (1) What exactly are the customers’ “needs” and “wants” for digital camera products? (2) What features is more importance than others? (3) Can product design and planning for product lines/product collection be integrated with the knowledge of customers? (4) How can the rules help us to make a strategy during we design new digital camera? To investigate these research issues, the Apriori and C5.0 algorithms are methodologies of association rules and decision trees for data mining, which is implemented to mine customer’s needs. Knowledge extracted from data mining results is illustrated as knowledge patterns and rules on a product map in order to propose possible suggestions and solutions for product design and marketing.     

The Effects of Customer Value on Loyalty and Profits in a Dynamic Competitive Market

Providing high value products and services to customers normally leads to customer loyalty and profits. In practice, the relationship of customer value, loyalty, and profits can be observed in the market. However, the dynamic interactive relationships which lead to customer loyalty and profits still remain ambiguous. In this paper, the Agent-Based Computational Economics (ACE) model is introduced to explore the formation of customer loyalty in the Taiwanese imported lumber market. Using agents with reinforced learning algorithms in trading simulations, the effects of customer value on loyalty and profit are incorporated and examined in this interactive dynamic model. As results, the positive correlations among variables of customer value, loyalty, and profits are observed. A controlled experiment shows that changing customer value leads to changes in customer loyalty and profits, but price is not the determinant factor for improving customer loyalty. The R ² values of customer loyalty and profits elucidate that they are increasing as the time lapsed elongate. Providing high value of products and services is a better strategy for suppliers to attract potential loyal customers.      

行业应用产品创新的开发管理探讨

创新能力是企业竞争优势的重要来源。在面向政府和企业客户市场时,电信运营商需要通过不断地进行产品的创新形成差异化的市场优势。如何有效地利用时间、激情、技术以及人才保持创新?本文提出了对于行业应用产品创新的理解,分析了新产品开发上市流程以及新产品开发所面临的挑战,并对于行业应用产品开发新的流程和方法进行了重点探讨。     

Selection of the optimum promotion mix by integrating a fuzzy linguistic decision model with genetic algorithms

Integrated marketing communication (IMC) is an important process by which a company can influence a target market, improve the position of that company’s product/service in the target market, and effectively build up its brand image. Sales promotion is an important communication channel designed to influence the customer’s purchasing behavior in the target market. There are many promotion tools available. Variations in business objectives and budgetary limits make it impossible for a company to employ all these promotion tools to convey sales messages to the customers. The selection of the best mix of promotion tools involves subjective information processing, instead of a numerically expressed objective decision-making process. In this research, we integrate a fuzzy linguistic decision model with a genetic algorithm (GA) to extract the optimum promotion mix of a variety of tools under satisfying expected marketing performance and budget limitations. The proposed methodology shows satisfactory results in an empirical study in terms of estimating the degree of satisfaction for achieving the business objectives, determining the optimum promotion mix, and minimizing expenditure on sales promotion activities.