Online Personalization And Information Sharing Under Horizontal Relationship

Customer preference information is of great importance for vendors to carry out price discrimination and targeted marketing. Advanced Internet technologies, especially web 2.0 and web-economy, have been provided accessibility and allowed vendors to acquire these information by the user-community and online personalization technologies. This study investigates an information market where the complementary firm pays to the vendor to indirectly acquire the customer preference information, which could be costly to acquire. We develop an economic model to examine vendor’s optimal information acquisition and sharing strategies under horizontal relationship under different payment formats of the complementary firm (i.e. fixed-fee or service-rate payment). We show that both payment formats improve the basic personalization service, and the basic personalization service is equal under two payment cases, but the extra personalization service under fixed-fee payment is higher than that under the service-rate payment. Nevertheless, the vendor’s equilibrium benefits are improved with information sharing under both payment formats. Moreover, although the complementary firm would get zero benefits under fixed-fee payment and positive benefits under service-rate payment, the customer preference information can be acquired under both cases. Our findings not only help researchers interpret why the vendors implement information sharing strategies, but also assist practitioners in developing better social commerce and cooperation strategy. The implications of this paper can shed light on how firms interact under horizontal relationship where a vendor possesses information superiority

Aplicação de marketing analítico na farmácia Gaia Jardim

O projecto apresentado surgiu no âmbito de uma proposta de investigação da Católica Porto Business School. Cada vez mais, os sistemas de informação fazem parte do desenvolvimento das empresas e a Farmácia Gaia Jardim procurou evoluir nesse sentido e utilizar a sua base de dados para desenvolver a sua relação com os clientes. Assim, neste trabalho pretendeu-se estabelecer estratégias de cross-selling através do uso de ferramentas de marketing analítico, nomeadamente data mining. De um modo geral, esta análise aos dados da farmácia permitiu um maior conhecimento sobre os seus clientes. Mais concretamente, este trabalho permitiu a identificação dos segmentos de clientes com base na frequência de visitas à fármacia e o valor médio gasto e a criação de regras de associação entre produtos para estabelecer as estratégias de cross-selling mais adequadas para cada segemento de clientes da farmácia.The presented project is developed in the scope of a research proposal from Católica Porto Business School. Information systems are becoming of increased importance to the development of companies. Gaia Jardim Pharmacy is taking this fact into consideration and using its data base to develop their relationship with their customers. Therefore, in this paper we aimed at creating cross-selling strategies through of the use of analytic marketing tools, as data mining. In general, this analysis enabled to obtain a more detailed knowledge about the customers of the pharmacy. uMore specifically, this study enabled the identification of segments of customers and finally on the identification of association rules between the products which allows the pharmacy to definethe most adequate cross-selling strategies for the segments of customers identified

Assessing users' product-specific knowledge for personalization in electronic commerce

While many electronic commerce (EC) companies are adopting one-to-one marketing approaches using various personalization technologies to make their products and services unique for the purpose of attracting and retaining customers and improving their completion edges in the EC ecosystem, which, nevertheless, has low entrance barriers for new players to join and further intensify the competition, none or few of them consider a fundamental issue-the user's product-specific knowledge. Our research proposed to add this new domain of the customer's knowledge on appropriate target products into the personalization process as a part of the overall EC strategy for businesses. In this paper, we present our initial design for assessing the user's product-specific knowledge using the proposed innovative method for detecting it directly in a non-intrusive way without asking users to answer or fill out any types of questionnaires. Our method is based on customer's on-line navigation behaviors by analyzing their navigation patterns through pre-trained artificial neural networks. An empirical study designed for a case of EC store selling digital cameras was conducted in our research to prove the concept, and a good preliminary result was derived from the study. For the purpose of comparing the performances between the conventional approach of using questionnaire and the proposed innovative approach of navigation pattern mining, a questionnaire based approach for evaluating the user's product-specific knowledge was designed and incorporated into our knowledge level assessment system (KLAS). Our study result shows that although the pure questionnaire-based KLAS is intrusive and may not be accepted by some users, for those users willing to complete the questionnaire, the proposed navigation pattern approach can be combined with the questionnaire-based approach to create a hybrid KLAS which has a significantly improved accuracy rate in detecting the customer's product knowledge level. (c) 2005 Elsevier Ltd. All rights reserved

The use of recommender and decision support systems for sales personalization in a mobile application

In the process of shopping, users are today faced with a large volume of information and a broad range of products and services that prevent them from being able to make rational decisions regarding the purchase of those products and services they actually require at a particular time and place and which meet their preferences, interests and needs. By defining and confirming this problem faced by users, we began with the analysis, design, development, testing and implementation of an information and recommendation system for the personalization of sales. This information system operates on the basis of a business model, where in exchange for providing important feedback, the user receives special offers or loyalty points. A lack of qualitative data about customers, their habits, future purchases and past experiences is one of the key factors in preventing companies from implementing effective personalization. Thus, even in real time, companies lack answers to important questions that concern marketing, sales and business operations. With the assistance of recommendation and decision making systems and by processing large amounts of smart data, we can offer the customer personalized products and services and thereby accelerate and increase sales volume while simultaneously improving the user and shopping experience. In the analysis and development of the information and recommendation system, we developed a hypothesis which proposed that with the use of qualitative data on user desires, needs, past experiences and future purchases, we could offer the user more personalized special offers. Personalization will also enable an increase of the CTR (Click to Rate) conversion between views of special offers and relevant responses, or rather, the execution of sales campaigns. On the basis of the developed and tested recommendation system, we conclude that the most appropriate solution for our purposes is the use of hybrid recommendation techniques which, depending on different types of situations, implement either the CF or CB method of filtering in combination with other decision rules and conditions

The use of recommender and decision support systems for sales personalization in a mobile application

In the process of shopping, users are today faced with a large volume of information and a broad range of products and services that prevent them from being able to make rational decisions regarding the purchase of those products and services they actually require at a particular time and place and which meet their preferences, interests and needs. By defining and confirming this problem faced by users, we began with the analysis, design, development, testing and implementation of an information and recommendation system for the personalization of sales. This information system operates on the basis of a business model, where in exchange for providing important feedback, the user receives special offers or loyalty points. A lack of qualitative data about customers, their habits, future purchases and past experiences is one of the key factors in preventing companies from implementing effective personalization. Thus, even in real time, companies lack answers to important questions that concern marketing, sales and business operations. With the assistance of recommendation and decision making systems and by processing large amounts of smart data, we can offer the customer personalized products and services and thereby accelerate and increase sales volume while simultaneously improving the user and shopping experience. In the analysis and development of the information and recommendation system, we developed a hypothesis which proposed that with the use of qualitative data on user desires, needs, past experiences and future purchases, we could offer the user more personalized special offers. Personalization will also enable an increase of the CTR (Click to Rate) conversion between views of special offers and relevant responses, or rather, the execution of sales campaigns. On the basis of the developed and tested recommendation system, we conclude that the most appropriate solution for our purposes is the use of hybrid recommendation techniques which, depending on different types of situations, implement either the CF or CB method of filtering in combination with other decision rules and conditions