Negotiating over Bundles and Prices Using Aggregate Knowledge

Combining two or more items and selling them as one good, a practice called bundling, can be a very effective strategy for reducing the costs of producing, marketing, and selling goods. In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a technique for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining historical sales data, condensed into aggregate knowledge, with current data about the ongoing negotiation process, to exploit these insights. In particular, when negotiating a given bundle of goods with a customer, the shop analyzes the sequence of the customer's offers to determine the progress in the negotiation process. In addition, it uses aggregate knowledge concerning customers' valuations of goods in general. We show how the shop can use these two sources of data to locate promising alternatives to the current bundle. When the current negotiation's progress slows down, the shop may suggest the most promising of those alternatives and, depending on the customer's response, continue negotiating about the alternative bundle, or propose another alternative. Extensive computer simulation experiments show that our approach increases the speed with which deals are reached, as well as the number and quality of the deals reached, as compared to a benchmark. In addition, we show that the performance of our system is robust to a variety of changes in the negotiation strategies employed by the customers.Comment: 15 pages, 7 eps figures, Springer llncs documentclass. Extended version of the paper published in "E-Commerce and Web Technologies," Kurt Bauknecht, Martin Bichler and Birgit Pr\"{o}ll (eds.). Springer Lecture Notes in Computer Science, Volume 3182, Berlin: Springer, p. 218--22

Online Learning of Aggregate Knowledge about Non-linear Preferences Applied to Negotiating Prices and Bundles

In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a procedure for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining aggregate (anonymous) knowledge of customer preferences with current data about the ongoing negotiation process. The developed procedure either works with already obtained aggregate knowledge or, in the absence of such knowledge, learns the relevant information online. We conduct computer experiments with simulated customers that have_nonlinear_ preferences. We show how, for various types of customers, with distinct negotiation heuristics, our procedure (with and without the necessary aggregate knowledge) increases the speed with which deals are reached, as well as the number and the Pareto efficiency of the deals reached compared to a benchmark.Comment: 10 pages, 5 eps figures, ACM Proceedings documentclass, Published in "Proc. 6th Int'l Conf. on Electronic Commerce ICEC04, Delft, The Netherlands," M. Janssen, H. Sol, R. Wagenaar (eds.). ACM Pres

Efficient Methods for Automated Multi-Issue Negotiation: Negotiating over a Two-Part Tariff

In this article, we consider the novel approach of a seller and customer negotiating bilaterally about a two-part tariff, using autonomous software agents. An advantage of this approach is that win-win opportunities can be generated while keeping the problem of preference elicitation as simple as possible. We develop bargaining strategies that software agents can use to conduct the actual bilateral negotiation on behalf of their owners. We present a decomposition of bargaining strategies into concession strategies and Pareto-efficient-search methods: Concession and Pareto-search strategies focus on the conceding and win-win aspect of bargaining, respectively. An important technical contribution of this article lies in the development of two Pareto-search methods. Computer experiments show, for various concession strategies, that the respective use of these two Pareto-search methods by the two negotiators results in very efficient bargaining outcomes while negotiators concede the amount specified by their concession strategy

Establishing and Monitoring SLAs in complex Service Based Systems

In modern service economies, service provisioning needs to be regulated by complex SLA hierarchies among providers of heterogeneous services, defined at the business, software, and infrastructure layers. Starting from the SLA Management framework defined in the SLA@SOI EU FP7 Integrated Project, we focus on the relationship between establishment and monitoring of such SLAs, showing how the two processes become tightly interleaved in order to provide meaningful mechanisms for SLA management. We first describe the process for SLA establishment adopted within the framework; then, we propose an architecture for monitoring established SLAs, which satisfies the two main requirements introduced by SLA establishment: the availability of historical data for evaluating SLA offers and the assessment of the capability to monitor the terms in a SLA offer

Multi-attribute bilateral bargaining in a one-to-many setting

Negotiations are an important way of reaching agreements between selfish autonomous agents. In this paper we focus on one-to-many bargaining within the context of agent-mediated electronic commerce. We consider an approach where a seller negotiates over multiple interdependent attributes with many buyers individually. Bargaining is conducted in a bilateral fashion, using an alternating-offers protocol. In such a one-to-many setting, “fairness,” which corresponds to the notion of envy-freeness in auctions, may be an important business constraint. For the case of virtually unlimited supply (such as information goods), we present a number of one-to-many bargaining strategies for the seller, which take into account the fairness constraint, and consider multiple attributes simultaneously. We compare the performance of the bargaining strategies using an evolutionary simulation, especially for the case of impatient buyers and small premature bargaining break off probability. Several of the developed strategies are able to extract almost all the surplus; they utilize the fact that the setting is one-to-many, even though bargaining occurs in a bilateral fashion

Online learning of aggregate knowledge about non-linear preferences applied to negotiating prices and bundles

In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a procedure for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining aggregate (anonymous) knowledge of customer preferences with current data about the ongoing negotiation process. The developed procedure either works with already obtained aggregate knowledge or, in the absence of such knowledge, learns the relevant information online. We conduct computer experiments with simulated customers that have emph{nonlinear} preferences. We show how, for various types of customers, with distinct negotiation heuristics, our procedure (with and without the necessary aggregate knowledge) increases the speed with which deals are reached, as well as the number and the Pareto efficiency of the deals reached compared to a benchmar

Influência dos agentes inteligentes no processo de conversão

Versão final (Esta versão contém as críticas e sugestões dos elementos do júri)O estudo da presente dissertação tem como foco a influência dos Agentes Inteligentes no processo de conversão, nomeadamente, em processos de compras efetuadas de forma eletrónica no conceito de negócio para Cliente final e de empresa para empresa. A presente investigação tem como principal foco perceber se os Agentes Inteligentes utilizados em diversas situações (e outros que possam surgir) têm real impacto nos processos de venda. Tendo como base o tópico em questão a investigação foi iniciada com a explicação e definição do tema “Agente Inteligente” de uma forma geral, sendo segmentado depois a informação do estudo para os AI aplicados ao marketing. De seguida foi elaborada abordagem ao processo de conversão, incluído no funil de vendas das entidades (tratando-se do último passo deste funil) tendo sido efetuada comparação com o processo de jornada do consumidor, que, parecido com o funil de vendas, tem no processo de conversão uma das fases finais, não sendo, no entanto, a última. O estudo teve como principal base a análise de AI desenvolvida por Kumar et al. (2015) sobre a forma como são aplicados os AI no marketing, nomeadamente em que bases, estratégias utilizadas e aplicações possíveis. Com base no estudo efetuado, foi possível criar o inquérito que permitisse perceber de que forma os AI têm ou não influência na aquisição de bens e serviços por parte dos consumidores finais no processo de compras eletrónico. No estudo do funil de vendas a base de trabalho teve como ponto de partida a análise desenvolvida por Cooper e Budd (2006) onde são analisados os diversos estados do funil de vendas bem como métodos e atuação em cada um deles. Foi igualmente analisada a jornada do consumidor, com base no estudo de Edelman (2010) onde são igualmente apresentados os estados do consumidor, mas é elaborada uma diferente abordagem à forma como o Cliente converte a ação de compra. O questionário elaborado contou com uma amostra de 105 inquiridos, onde as diferentes estratégias de abordagem dos AI foram apresentadas, por forma a perceber quais destas terão resposta mais positiva. A fase final da presente dissertação apresenta-nos a conclusão do estudo relativo ao questionário elaborado, com dados relativos às respostas dos inquiridos permitindo tirar dados sobre a influência dos AI no processo de conversão. O presente estudo permitiu concluir que os AI têm efetivamente influência no processo de conversão sendo que o impacto varia mediante cada tipo de AI aplicado e mediante o tipo de ação envolvida.The intelligent agents importance it’s growing every year in many sectors, marketing (as well digital marketing) included. The present research was developed to understand how the intelligent agents influence the conversion process of the users and companies at the digital buying process. Regarding the main topic, the study starts explaining what intelligent agents are at a general level and, in specific, at a marketing level. After presenting the intelligent agent concept the study presents the conversion concept, including the sales funnel approach, comparing it with the consumer journey and explaining how both concepts works, in which both have the conversion point, having however, at the consumer journey a different approach, different from the sales funnel. Based on Kumar et al. (2015) research, this research presents how intelligent agents are used in marketing, namely, their bases, strategies and possible applications. Based on this study it was possible to develop an online survey to try to determine if the intelligent agents has influence in the buying process. Regarding the sales funnel the research was based mostly at the Cooper & Budd (2006) study, where the different funnel states are studied as well as acting methods for each of them. For the consumer journey the study was based on Edelman (2010) research, among others, where are presented the different stages of the buyer and it’s presented a different approach how buyers convert. The online survey had 105 respondents at which different intelligent agents strategies were presented so we could understand which IA had a better answer. The studies final process presents conclusions regarding the online survey, with the data from the answers that allowed to understand how intelligent agents influences the conversion process. With this work we could understand that the intelligent agents influence the conversion process, with different impact through each intelligent agents used and through each action involved