Preference mining techniques for customer behavior analysis

The thesis has studied a number of critical problems in data mining for customer behavior analysis and has proposed novel techniques for better modeling of the customers’ decision making process, more efficient analysis of their travel behavior, and more effective identification of their emerging preference.

Impact of Product Quality, Service Quality and Contextual Experience on Customer Perceived Value and Future Buying Intentions

Recognizing that high satisfaction leads to high customer loyalty, companies today are aiming for total customer satisfaction. This article explains relative impact of product quality, service quality and contextual experience on customer perceived value and intention to shop in the future. The data has been collected using a questionnaire from 205 customers of a national retailer chain. The relative importance of product quality, service quality and contextual experience on customer perceived value and thus on customer preference and future intentions was measured using multiple regression. Also, the contribution of perceived value to preference and thus on future buying intention was also measured. Structural Equation Model (SEM) using Amos 4 was used to find the overall fitness of the model. It was found that product quality, service quality and contextual experience have a major influence on customer perceived value

Aplicação do desdobramento da função qualidade (QFD) para melhoria da qualidade em um restaurante self-service

Currently, with the competitiveness that is seen in the market, it is crucial to the success of the business, develop new strategies to keep and win new customer preference. To ensure the success of a particular service or product, the secret is to continually meet the wishes and demands of the customers, which are the key parts of the business, through innovation, variety and quality assurance. To achieve this goal managers should be aware of all types of process that exist in the company, as they are primarily responsible and interested by quality service, customer satisfaction and consequently, generating favorable financial results. A tool used to ensure good results to business is the Quality Function Deployment (QFD) that seeks to hear and interpret customers requirements and turn them into essential features for a project

Dynamic job quoting system for printed circuit board fabrication

This research deals with the development of a dynamic job quotation system for printed circuit board (PCB) fabrication, which can estimate the price and completion time of a job based on customer preference and current capacity of the shop floor. The primary purpose of building a dynamic quotation system is to maximize the company's profit by quoting optimum lead-time and competitive price for the day-to-day orders received from different customers and original equipment manufacturers. The system was developed using MS-Access relational database. Evaluating the output of the system it was observed that the dynamic system provided more reliable estimation of the lead-time needed for fabricating new jobs. The overall price quoted by the system was competitive with higher profit margin when compared to traditional static systems. This system would therefore provide a vital link between the job quoting and scheduling system of the firm enabling better utilization of the available resources.

Finding optimal alternatives based on efficient comparative preference inference

Choosing the right or the best option is often a demanding and challenging task for the user (e.g., a customer in an online retailer) when there are many available alternatives. In fact, the user rarely knows which offering will provide the highest value. To reduce the complexity of the choice process, automated recommender systems generate personalized recommendations. These recommendations take into account the preferences collected from the user in an explicit (e.g., letting users express their opinion about items) or implicit (e.g., studying some behavioral features) way. Such systems are widespread; research indicates that they increase the customers' satisfaction and lead to higher sales. Preference handling is one of the core issues in the design of every recommender system. This kind of system often aims at guiding users in a personalized way to interesting or useful options in a large space of possible options. Therefore, it is important for them to catch and model the user's preferences as accurately as possible. In this thesis, we develop a comparative preference-based user model to represent the user's preferences in conversational recommender systems. This type of user model allows the recommender system to capture several preference nuances from the user's feedback. We show that, when applied to conversational recommender systems, the comparative preference-based model is able to guide the user towards the best option while the system is interacting with her. We empirically test and validate the suitability and the practical computational aspects of the comparative preference-based user model and the related preference relations by comparing them to a sum of weights-based user model and the related preference relations. Product configuration, scheduling a meeting and the construction of autonomous agents are among several artificial intelligence tasks that involve a process of constrained optimization, that is, optimization of behavior or options subject to given constraints with regards to a set of preferences. When solving a constrained optimization problem, pruning techniques, such as the branch and bound technique, point at directing the search towards the best assignments, thus allowing the bounding functions to prune more branches in the search tree. Several constrained optimization problems may exhibit dominance relations. These dominance relations can be particularly useful in constrained optimization problems as they can instigate new ways (rules) of pruning non optimal solutions. Such pruning methods can achieve dramatic reductions in the search space while looking for optimal solutions. A number of constrained optimization problems can model the user's preferences using the comparative preferences. In this thesis, we develop a set of pruning rules used in the branch and bound technique to efficiently solve this kind of optimization problem. More specifically, we show how to generate newly defined pruning rules from a dominance algorithm that refers to a set of comparative preferences. These rules include pruning approaches (and combinations of them) which can drastically prune the search space. They mainly reduce the number of (expensive) pairwise comparisons performed during the search while guiding constrained optimization algorithms to find optimal solutions. Our experimental results show that the pruning rules that we have developed and their different combinations have varying impact on the performance of the branch and bound technique.

Paradox of choice: The influence of assortment size on customer satisfaction

Recently researchers showed that more choice is not always better. Choosing from large assortments can be overwhelming, raising expectations and decreasing overall level of consumer satisfaction. Author contributes to existing overchoice studies by using real assortment of online stores to find influence of assortment size on customer satisfaction. 90 students participated in the main experiment, where they chose a smartphone case for their friend. Results of the study show that large assortment size leads to higher expectations, higher choice difficulty and higher level of satisfaction. This research does not show overchoice presence and author suggests future studies could focus more on assortment variety and more personal characteristics of consumers, like preference uncertainty.

Application of the european customer satisfaction index to postal services. Structural equation models versus partial least squars

Customer satisfaction and retention are key issues for organizations in today’s competitive market place. As such, much research and revenue has been invested in developing accurate ways of assessing consumer satisfaction at both the macro (national) and micro (organizational) level, facilitating comparisons in performance both within and between industries. Since the instigation of the national customer satisfaction indices (CSI), partial least squares (PLS) has been used to estimate the CSI models in preference to structural equation models (SEM) because they do not rely on strict assumptions about the data. However, this choice was based upon some misconceptions about the use of SEM’s and does not take into consideration more recent advances in SEM, including estimation methods that are robust to non-normality and missing data. In this paper, both SEM and PLS approaches were compared by evaluating perceptions of the Isle of Man Post Office Products and Customer service using a CSI format. The new robust SEM procedures were found to be advantageous over PLS. Product quality was found to be the only driver of customer satisfaction, while image and satisfaction were the only predictors of loyalty, thus arguing for the specificity of postal services

Emerging segmentation practices in the age of the social customer

This paper reports on an exploratory study of segmentation practices of organisations with a social media presence. It investigates whether traditional segmentation approaches are still relevant in this new socio-technical environment and identifies emerging practices. The study found that social media are particularly promising in terms of targeting influencers, enabling the cost-effective delivery of personalised messages and engaging with numerous customer segments in a differentiated way. However, some problems previously identified in the segmentation literature still occur in the social media environment, such as the technical challenge of integrating databases, the preference for pragmatic rather than complex solutions and the lack of relevant analytical skills. Overall, a gap has emerged between marketing theory and practice. While segmentation is far from obsolete in the age of the social customer, it needs to adapt to reflect the characteristics of the new media.

Customer repurchase intention: a general structural equation model

This paper develops a general service sector model of repurchase intention from the consumer theory literature. A key contribution of the structural equation model is the incorporation of customer perceptions of equity and value and customer brand preference into an integrated repurchase intention analysis. The model describes the extent to which customer repurchase intention is influenced by seven important factors – service quality, equity and value, customer satisfaction, past loyalty, expected switching cost and brand preference. The general model is applied to customers of comprehensive car insurance and personal superannuation services. The analysis finds that although perceived quality does not directly affect customer satisfaction, it does so indirectly via customer equity and value perceptions. The study also finds that past purchase loyalty is not directly related to customer satisfaction or current brand preference and that brand preference is an intervening factor between customer satisfaction and repurchase intention. The main factor influencing brand preference was perceived value with customer satisfaction and expected switching cost having less influence.

An incremental FP-growth web content mining and its application in preference identification

This paper presents a real application of Web-content mining using an incremental FP-Growth approach. We firstly restructure the semi-structured data retrieved from the web pages of Chinese car market to fit into the local database, and then employ an incremental algorithm to discover the association rules for the identification of car preference. To find more general regularities, a method of attribute-oriented induction is also utilized to find customer’s consumption preferences. Experimental results show some interesting consumption preference patterns that may be beneficial for the government in making policy to encourage and guide car consumption.

A fuzzy decision support method for customer preferences analysis based on Choquet integral

The explosion of the Web 2:0 platforms, with massive volume of user generated data, has presented many new opportunities as well as challenges for organizations in understanding consumer's behavior to support for business planning process. Feature based sentiment mining has been an emerging area in providing tools for automated opinion discovery and summarization to help business managers with achieving such goals. However, the current feature based sentiment mining systems were only able to provide some forms of sentiments summary with respect to product features, but impossible to provide insight into the decision making process of consumers. In this paper, we will present a relatively new decision support method based on Choquet Integral aggregation function, Shapley value and Interaction Index which is able to address such requirements of business managers. Using a study case of Hotel industry, we will demonstrate how this technique can be applied to effectively model the user's preference of (hotel) features. The presented method has potential to extend the practical capability of sentiment mining area, while, research findings and analysis are useful in helping business managers to define new target customers and to plan more effective marketing strategies.

Customer response to media in marketing promotion: print versus online

Il lavoro di tesi, che si compone di tre articoli di ricerca, analizza, nel contesto della marketing promotion, la risposta del consumatore ai media in termini di ricordo, intenzione di acquisto, comportamento di acquisto e preferenza per il medium. Il lavoro, in particolare, mette a confronto due tipologie di media, carta e online, nell’ambito della price e loyalty promotion, utilizzando due disegni di ricerca sperimentali ed uno correlazionale. I risultati del lavoro mostrano che la risposta del consumatore alla comunicazione promozionale e ai media è eterogenea: segmenti di clienti diversi rispondono in maniera differente sia alla comunicazione promozionale che a carta e digitale. Online e carta hanno in media la stessa efficacia sui comportamenti di acquisto dei clienti, ma differiscono rispetto all’effetto su ricordo e atteggiamento e rispetto alla preferenza per il medium espressa dalla clientela. Lo spostamento delle risorse di marketing dalla carta al digitale permetterebbe quindi di ridurre i costi mantenendo lo stesso livello di efficacia. Inoltre, il presente lavoro mostra come sia possibile aumentare la risposta dei consumatori ai media attraverso un approccio di segmentazione della clientela.

Consequences of customer loyalty to the loyalty program and to the company

Gaining customer loyalty is an important goal of marketing, and loyalty programs are intended to help in reaching it. Research on loyalty programs suggests that customers differentiate between loyalty to a company and loyalty to a loyalty program, yet little is known about the consequences of these two types of loyalty. Therefore, our study intends to make two main contributions: (1) improving our understanding of the constructs "program loyalty" and "company loyalty", (2) investigating the relative impact of the two types of loyalty on preference, intention, and purchase behavior for the case of a multi-firm loyalty program. Results indicate that company loyalty influences a customer's choice to visit a particular provider and to prefer it over competitors, but it is not a strong predictor of purchase behavior. Conversely, program loyalty is a far more important driver of purchase behavior. This implies that company loyalty primarily attracts customers to a particular provider and program loyalty ensures that once inside the store, more money is spent. © 2011 Academy of Marketing Science.

Stakeholder preference and stated vs. derived importance satisfaction research

This paper presents a case study that reveals how stakeholders in the research process, by recommending specific data collection and analytical techniques, exert significant ‘hidden’ influence on the decisions made on the basis of market research findings. While disagreements amongst stakeholders regarding research design are likely, the possibility that strategies adopted by companies are dependent upon stakeholder research preferences has not been adequately addressed in the literature. Two widely used quantitative customer satisfaction evaluation approaches, involving stated and derived importance, are compared within a real life market research setting at an international bank. The comparative analysis informs an ongoing debate surrounding the applicability of explicit and implicit importance measures and demonstrates how recommendations are dependent upon the methodological and analytical techniques selected. The findings, therefore, have significant implications for importance based satisfaction market research planning and highlight the need to consider the impact of stakeholder preferences on research outcomes.