Dynamic micro-targeting: fitness-based approach to predicting individual preferences

It is crucial to segment customers intelligently in order to offer more targeted and personalized products and services. Traditionally, customer segmentation is achieved using statistics-based methods that compute a set of statistics from the customer data and group customers into segments by applying clustering algorithms. Recent research proposed a direct grouping-based approach that combines customers into segments by optimally combining transactional data of several customers and building a data mining model of customer behavior for each group. This paper proposes a new micro-targeting method that builds predictive models of customer behavior not on the segments of customers but rather on the customer-product groups. This micro-targeting method is more general than the previously considered direct grouping method. We empirically show that it outperforms the direct grouping and statistics-based segmentation methods across multiple experimental conditions and that it generates predominately small-sized segments, thus providing additional support for the micro-targeting approach to personalization.

Product architecting for personalization

Personalization is an emerging manufacturing paradigm whereby customers can tailor products to their individual needs while maintaining high production efficiency. This paradigm necessitates “personalized product architecting” for determination of customizable/personalizable product modules and cost-effective manufacturing methods. This paper presents an initial effort in developing a method for identifying appropriate product architectures and manufacturing resolutions to achieve personalization considering functional utility and manufacturing cost. Ergonomic experiments and conjoint analysis are implemented to build functions relating manufacturability, price, and utility. Using these functions, a case study based on shoe products is conducted and the common integer programming welfare problem is expanded to a mixed-integer programming optimization problem for determination of a product family incorporating both personalized and customized offerings.     

An approach to competitive product line design using conjoint data

In today’s highly competitive environment, where market oriented firms aim to maximize profits through customer satisfaction, there is an increasing need to design a product line, rather than a single product. The main goal of designing a profit maximizing product line is to target the ‘right product’ to the ‘right customer’. Although conjoint analysis has turned out to be one of the most widely used techniques for product line design, it falls to explicitly consider retaliatory reactions from competitors. In this paper, we propose a new conjoint-based approach to competitive new product line design, employing the Nash equilibrium concept. The optimal product line design problem for each firm is formulated as a nonlinear integer programming problem. In the absence of a closed-form solution, to compute the Nash equilibrium and to determine the optimal product line, we propose a two-phase procedure: a sequential iterative procedure in the first phase, and backward induction in the second. To solve the optimization problem in each of the iterations of the sequential procedure, we used the branch-and-bound method. The proposed approach is illustrated under several scenarios of competition using previously published conjoint data.     

A hybrid recommendation technique based on product category attributes

Recommender systems are powerful tools that allow companies to present personalized offers to their customers and defined as a system which recommends an appropriate product or service after learning the customers’ preferences and desires. Extracting users’ preferences through their buying behavior and history of purchased products is the most important element of such systems. Due to users’ unlimited and unpredictable desires, identifying their preferences is very complicated process. In most researches, less attention has been paid to user’s preferences varieties in different product categories. This may decrease quality of recommended items. In this paper, we introduced a technique of recommendation in the context of online retail store which extracts user preferences in each product category separately and provides more personalized recommendations through employing product taxonomy, attributes of product categories, web usage mining and combination of two well-known filtering methods: collaborative and content-based filtering. Experimental results show that proposed technique improves quality, as compared to similar approaches.     

遗传算法在退货逆向物流网络设计中的应用

退货产生的逆向物流是很多企业长期面临的难点,并成为学术界和企业界广泛关注的热点.逆向物流网络设计是逆向物流管理的首要任务,也是一类NP完全问题.遗传算法作为组合优化设计的一种算法,可有效地提高解决这类NP完全问题的效率.     

Conjoint analysis for IPTV service

Given the upcoming introduction of IPTV service in Korea, it is necessary to develop business models and marketing strategies to improve customer satisfaction and succeed in market competition. We use conjoint analysis to estimate customer preferences and the relative importance of service factors. Based on results from total customers’ and clustered customers’ service preferences, we propose marketing strategies for service providers.     

A heuristic genetic algorithm for product portfolio planning

Product portfolio planning has been recognized as a critical decision facing all companies across industries. It aims at the selection of a near-optimal mix of products and attribute levels to offer in the target market. It constitutes a combinatorial optimization problem that is deemed to be NP-hard in nature. Conventional enumeration-based optimization techniques become inhibitive given that the number of possible combinations may be enormous. Genetic algorithms have been proven to excel in solving combinatorial optimization problems. This paper develops a heuristic genetic algorithm for solving the product portfolio planning problem more effectively. A generic encoding scheme is introduced to synchronize product portfolio generation and selection coherently. The fitness function is established based on a shared surplus measure leveraging both the customer and engineering concerns. An unbalanced index is proposed to model the elitism of product portfolio solutions.     

Mining customer requirements from online reviews: A product improvement perspective

Big data commerce has become an e-commerce trend. Learning how to extract valuable and real time insights from big data to drive smarter and more profitable business decisions is a main task of big data commerce. Using online reviews as an example, manufacturers have come to value how to select helpful online reviews and what can be learned from online reviews for new product development. In this research, we first proposed an automatic filtering model to predict the helpfulness of online reviews from the perspective of the product designer. The KANO method, which is based on the classical conjoint analysis model, is then innovatively applied to analyze online reviews to develop appropriate product improvement strategies. Moreover, an empirical case study using the new method is conducted with the data we acquired from JD.com, one of the largest electronic marketplaces in China. The case study indicates the effectiveness and robustness of the proposed approach. Our research suggests that the combination of big data and classical management models can bring success for big data commerce.     

A generic genetic algorithm for product family design

Product family design (PFD) has been well recognized as an effective means to satisfy diverse market niches while maintaining the economies of scale and scope. PFD essentially entails a configuration problem by “combination," where combinatorial explosion always occurs and is known to be mathematically intractable or NP-hard. Although genetic algorithms (GAs) have been proven to excel in solving combinatorial optimization problems, it is difficult to adopt the traditional GA to deal with the complex data and interrelationships inherent in the PFD problem. This paper proposes a generic genetic algorithm (GGA) for PFD. A generic encoding scheme is developed to adapt to diverse PFD scenarios. A hybrid constraint-handling strategy is proposed to handle complex and distinguishing constraints at different stages along the evolutionary process. The design and implementation procedures of the GGA are discussed in detail. An application of the proposed GGA to motor family design is reported. The GGA efficiency is also tested through efficiency analysis in terms of the probability of generating feasible solutions, as well as through analysis of the GGA complexity.     

面向顾客选择的产品联合分析系统的研究与实现

运用联合分析原理建立数学模型,根据数学模型和项目情况进行系统分析设计,运用程序开发等相关技术实现在线产品分析系统,该系统提供给设计和销售人员进行产品调查和分析,从而提供产品各属性水平的效用值、重要性及产品潜在的市场占有率,以指导产品研发设计和产品进货选择.     

A hybrid of sequential rules and collaborative filtering for product recommendation

Customers’ purchase behavior may vary over time. Traditional collaborative filtering (CF) methods make recommendations to a target customer based on the purchase behavior of customers whose preferences are similar to those of the target customer; however, the methods do not consider how the customers’ purchase behavior may vary over time. In contrast, the sequential rule-based recommendation method analyzes customers’ purchase behavior over time to extract sequential rules in the form: purchase behavior in previous periods ⇒ purchase behavior in the current period. If a target customer’s purchase behavior history is similar to the conditional part of the rule, then his/her purchase behavior in the current period is deemed to be the consequent part of the rule. Although the sequential rule method considers the sequence of customers’ purchase behavior over time, it does not utilize the target customer’s purchase data for the current period. To resolve the above problems, this work proposes a novel hybrid recommendation method that combines the segmentation-based sequential rule method with the segmentation-based KNN-CF method. The proposed method uses customers’ RFM (Recency, Frequency, and Monetary) values to cluster customers into groups with similar RFM values. For each group of customers, sequential rules are extracted from the purchase sequences of that group to make recommendations. Meanwhile, the segmentation-based KNN-CF method provides recommendations based on the target customer’s purchase data for the current period. Then, the results of the two methods are combined to make final recommendations. Experiment results show that the hybrid method outperforms traditional CF methods.     

Market segmentation and ideal point identification for new product design using fuzzy data compression and fuzzy clustering methods

In product design, various methodologies have been proposed for market segmentation, which group consumers with similar customer requirements into clusters. Central points on market segments are always used as ideal points of customer requirements for product design, which reflects particular competitive strategies to effectively reach all consumers’ interests. However, existing methodologies ignore the fuzziness on consumers’ customer requirements. In this paper, a new methodology is proposed to perform market segmentation based on consumers’ customer requirements, which exist fuzziness. The methodology is an integration of a fuzzy compression technique for multi-dimension reduction and a fuzzy clustering technique. It first compresses the fuzzy data regarding customer requirements from high dimensions into two dimensions. After the fuzzy data is clustered into marketing segments, the centre points of market segments are used as ideal points for new product development. The effectiveness of the proposed methodology in market segmentation and identification of the ideal points for new product design is demonstrated using a case study of new digital camera design.     

Relationship between meanings,emotions, product preferences and personal values. Application to ceramic tile floorings

This work aims to validate a conceptual framework which establishes the main relationships between subjective elements in human–product interaction, such as meanings, emotions, product preferences, and personal values. The study analyzes the relationships between meanings and emotions, and between these and preferences, as well as the influence of personal values on such relationships. The study was applied to ceramic tile floorings.     

质量驱动产品开发方法及其应用研究

对质量驱动的产品开发方法进行了介绍,针对我国仪表行业新产品开发能力薄弱的现状和现代工业仪表设计技术,建立了仪表产品质量驱动的计算机辅助产品开发集成设计系统框架模型,指出该设计系统的关键是实现产品质量功能配置,生命周期故障模式与效应分析（ＦＭＥＡ）,及产品设计决策系统的集成设计。文末给出了应用实例。     

Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer

Most marketers have difficulty in identifying the right customers to engage in successful campaigns. So far, customer segmentation is a popular method that is used for selecting appropriate customers for a launch campaign. Unfortunately, the link between customer segmentation and marketing campaign is missing. Another problem is that database marketers generally use different models to conduct customer segmentation and customer targeting. This study presents a novel approach that combines customer targeting and customer segmentation for campaign strategies. This investigation identifies customer behavior using a recency, frequency and monetary (RFM) model and then uses a customer life time value (LTV) model to evaluate proposed segmented customers. Additionally, this work proposes using generic algorithm (GA) to select more appropriate customers for each campaign strategy. To demonstrate the efficiency of the proposed method, this work performs an empirical study of a Nissan automobile retailer to segment over 4000 customers. The experimental results demonstrate that the proposed method can more effectively target valuable customers than random selection.     

混合遗传算法研究及其应用

为了求解基于智能制造环境所建立的生产规划模型，解决维数灾、局部解等问题，本文对遗传算法进行了研究，提出并设计了一种线性规划和遗传算法相结合的启发式优化方法，并对其应用作了进一步的探讨。     

An in-process customer utility prediction system for product conceptualisation

Customer involvement in new product development, especially in the early stage of product conceptualisation, plays an important role for a successful product. In this study, a customer utility prediction system (CUPS) is proposed. The system comprises two modules, namely design knowledge acquisition module and customer utility evaluation module. In the design knowledge acquisition module, a knowledge acquisition technique called general sorting is utilised to establish a design knowledge hierarchy (DKH), in which design options can be generated. In the same module, customer voices towards diverse design options called customer-sensitive design criteria are solicited from customer requirements. Subsequently, in the customer utility evaluation module, a measurement for customer desirability, i.e. customer utility index (CUI), is formulated using conjoint analysis (CA) technique. Finally, the rated criteria are also used as inputs to a radial basis function (RBF) neural network for in-process customer utility prediction. A case study on cellular phone design is used to illustrate the proposed approach.