A Fuzzy Segmentation Approach to Guide Marketing Decisions

The aim of this chapter is to present a fuzzy segmentation model that combines statistical and Artificial Intelligence techniques to identify and quantify multifaceted consumers. One of the primary challenges faced by companies is getting to know their consumers. The latter are increasingly complex, versatile, ever-changing, and even contradictory; in other words, they are multifaceted. There is thus a need for techniques and tools to be able to segment this type of consumer in order to provide companies with the realistic information they need to make the appropriate marketing decisions. A real case study from the Spanish energy industry is included in this chapter to demonstrate the potential of the segmentation model being proposed.

Fuzzy Target Groups in Analytic Customer Relationship Management

Scoring models yield continuous predictions instead of sharp classifications. Scoring customers for profitability, loyalty, or product affinity corresponds to an inductive fuzzy classification: The model represents a continuous membership function mapping the set of customers into the fuzzy set of interesting customers – the fuzzy target group. This chapter presents a method for membership function induction based on normalized likelihood ratios. Applications of this method are proposed for selection, visualization, and prediction in the field of analytics in general, and for customer profiling, target group definition and customer scoring specifically for analytic customer relationship management. A real world case study is described. Furthermore, an implementation of the proposed method, developed at the research center for fuzzy marketing methods (FMsquare1), is presented.

Fuzzy Clustering of Web User Profiles for Analyzing their Behavior and Interests

One of the keys to building effective e-customer relationships is an understanding of consumer behavior online. However, analyzing the behavior of customers online is not necessarily an indicator of their interests. Therefore, building profiles of registered users of a website is of importance if it goes beyond collecting obvious information the user is willing to give at the time of the registration. These user profiles can contribute to the analysis of the users’ interests. Important tools for the analysis are data-mining techniques, for example, the clustering of collected user information. This chapter addresses the problem of how to define, calculate, and visualize fuzzy clusters of Web visitors with respect to their behavior and supposed interests. This chapter shows how to cluster Web users based on their profile and by their similar interests in several topics using the fuzzy and hybrid CORD (Clustering of Ordinal Data) clustering system, which is part of the Gugubarra Framework.

Causal Recipes Sufficient for Identifying Market Gurus versus Mavens

Prior research focusing on the market maven (MM) neglects to consider the possible existence of people who may represent an important source of marketplace information for MMs—the market guru (MG). A “market guru” is a consumer others frequently seek out for advice but who does not seek advice from others. In contrast to MG, a MM is a consumer who other consumers frequently ask for advice and who frequently seeks advice from others. This study raises the proposition that a greater share of MGs versus MMs are innovators, that is, individuals who rely on technical reports to become the first to adopt new products in her or his community. This study applies fuzzy-set qualitative comparative analysis (fs/QCA) to distinguish between MMs and MGs using multi-year data from a national U.S. omnibus survey. The findings support several propositions distinguishing MGs from MMs. MMs evaluate themselves as great influencers of consumers, highly sensitive to normative susceptibility, and possessing superior taste. However, MGs evaluate themselves exactly the opposite from MMs on these conditions.

Using a Fuzzy-Based Cluster Algorithm for Recommending Candidates in E-Elections

The use of the Internet now has a specific purpose: to find information. Unfortunately, the amount of data available on the Internet is growing exponentially, creating what can be considered a nearly infinite and ever-evolving network with no discernable structure. This rapid growth has raised the question of how to find the most relevant information. Many different techniques have been introduced to address the information overload, including search engines, Semantic Web, and recommender systems, among others. Recommender systems are computer-based techniques that are used to reduce information overload and recommend products likely to interest a user when given some information about the user’s profile. This technique is mainly used in e-Commerce to suggest items that fit a customer’s purchasing tendencies. The use of recommender systems for e-Government is a research topic that is intended to improve the interaction among public administrations, citizens, and the private sector through reducing information overload on e-Government services. More specifically, e-Democracy aims to increase citizens’ participation in democratic processes through the use of information and communication technologies. In this chapter, an architecture of a recommender system that uses fuzzy clustering methods for e-Elections is introduced. In addition, a comparison with the smartvote system, a Web-based Voting Assistance Application (VAA) used to aid voters in finding the party or candidate that is most in line with their preferences, is presented.

Fuzzy Dynamic Groups

Television audiences have been shown to be a mixture of lone individuals and groups of viewers, with groups contributing at least 50% of total ratings. Viewing with others also makes the experience more enjoyable and has important effects on cognitive processing of programs and advertisements. A major problem for researchers and managers is that groups of viewers are dynamic entities difficult to define or measure. This study frames groups of television viewers as fuzzy sets and presents fuzzy measures of group size and composition. The effects of these characteristics on individual consumption of television are assessed using statistical models, which incorporate the arithmetic forms of the proposed measures.

A Fuzzy Logic Approach for the Assessment of Online Customers

A key challenge for companies in the e-business era is to manage customer relationships as an asset. In today’s global economy this task is becoming simultaneously more difficult and more important. In order to retain the potentially good customers and to improve their buying attitude, this chapter proposes a hierarchical fuzzy classification of online customers. A fuzzy classification, which is a combination of relational databases and fuzzy logic, allows customers to be classified into several classes at the same time and can therefore precisely determine the customers’ value for an enterprise. This approach allows companies to improve the customer equity, to launch loyalty programs, to automate mass customization, and to refine marketing campaigns in order to maximize the customers’ value and, this way, the companies’ profit.

Fuzzy Data Warehouse for Performance Analysis

The numeric values retrieved from a data warehouse may be difficult to interpret by business users, or may be interpreted incorrectly. Therefore, for more accurate understanding of numeric values, business users may require an interpretation in meaningful, non-numeric terms. However, if the transition between non-numeric terms is crisp, true values cannot be measured, and smooth transition between classes may not take place. To address that problem, the authors employ a fuzzy classification-based approach for data warehouse. For that, they present a fuzzy data warehouse modeling approach, which allows integration of fuzzy concepts without affecting the core of a classical data warehouse. The essence of the approach is that a meta-tables structure is added for relating non-numeric terms with numeric values. This enables integration of fuzzy concepts in dimensions and facts, while preserving the time-invariability of the data warehouse. Additional to that, the use of fuzzy approach allows analysis of data in both sharp and fuzzy manners. The proposed approach is demonstrated through a case study of a movie rental company.

A Hybrid Fuzzy Multiple Objective Approach to Lotsizing, Pricing, and Marketing Planning Model

Given high variability of demands, a manufacturer has to decide about the products’ prices and lotsizing from a supplier. Due to imprecise and fuzzy nature of parameters such as unit costs and marketing function, a hybrid fuzzy multi-objective programming model including both quantitative and qualitative objectives is proposed to determine the optimal price, marketing expenditure, and lotsize. Considering pricing, marketing, and lotsizing decisions simultaneously, the model maximizes the profit, return on inventory investment (ROII) (as a financial performance criterion), and total customer satisfaction under general demand function with a time-varying pattern in fuzzy environment. After applying appropriate strategies to defuzzify the original model, the equivalent multi-objective crisp model is then transformed by a fuzzy goal programming method. A soft computing, particle swarm optimization (PSO) is applied to solve the final crisp problem. An industrial case study is provided to show the applicability and usefulness of the proposed model and solution method. Finally, concluding remarks are reported.

Web Analytics with Fuzziness

In the Internet economy and information society, it has become an essential task of electronic business to analyze, to monitor, and to optimize websites and Web offers. Therefore, this chapter addresses the issues of Web analytics, which is defined as the measurement, collection, analysis, and reporting of Internet data for the purposes of understanding and optimizing website usage. After a short introduction, the second section defines Web analytics, describes benefits and problems of Web analytics, as well as different software architectures and products. Third, a controlling loop is proposed for Web content and Web user controlling in order to analyze Key Performance Indicators (KPIs) and to take website- and e-business-related actions. Fourth, different Web metrics and KPIs of information, transaction and communication are defined. Fifth, a fuzzy Web analytics approach is proposed, which makes it possible to classify Web metrics precisely into more than one class at the same time. Considering real Web data of the Web metrics page views and bounce rate, it is shown that fuzzy classification allows exact and flexible segmentation of Web pages or other objects and gradual rankings within fuzzy sets. In addition, the fuzzy logic approach enables Computing with Words (CWW), i.e. the perception-based, linguistic consideration of Web data and Web metrics instead of measurement-based, numerical ones. Web usage mining with inductive fuzzy classification and Web Analytics with Words (WAW) allows intuitive, human-oriented analyses, description, and reporting of Web metrics values in natural language.