Revisiting the Use of Customer Information for CRM

For the past decade, customer relationship management (CRM) has been one of the priorities in marketing research and practice. However, many of the CRM systems did not perform as the companies expected. As such shortcoming could be due to inappropriate data input, this study provides a comprehensive overview of the empirical CRM literature. Along the phases of the CRM process, the authors show which kind of data has successfully proven to achieve the CRM objectives. The study provides researchers with a review of the empirical research on CRM and allows practitioners insights on the usability of customer data for CRM. --Customer Relationship Management (CRM),Customer Data

Beyond Personalization: Research Directions in Multistakeholder Recommendation

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article describes the origins of multistakeholder recommendation, and the landscape of system designs. It provides illustrative examples of current research, as well as outlining open questions and research directions for the field.Comment: 64 page

Emerging Perspectives on Self Service Technologies in Retail Banking

This paper attempts to critically examine the available literature on the subject, discuss a model that provides a managerial framework for analyzing the variables associated with customer value, and to identify potential research areas. The discussion draws conceptual impetus from new technologies in banking services through self service technologies in banking as a tool for optimizing profit. The discussion in the paper also analyzes the main criteria for successful internet-banking strategy and brings out benefits of e-banking from the point of view of banks, their technology and customer values and tentatively concludes that there is increasing returns to scale in the bank services in relation to the banking products, new technology and customer value.Self service technology, retail banking, customer value, profit optimization

Optimizing box content in try-before-you-buy business models with heterogeneous customer groups

In online retailing, try-before-you-buy business models are emerging. Customers can order items to be shipped home to try and decide which items to keep or send back to the retailer. Usually, this business model is combined with personalized recommendations. In this thesis, an optimization model for supporting the decision of what to put in a box to achieve maximum profit in a sales period is introduced. Multiple periods are simulated and overall profit is analyzed. Profit is evaluated by summing the optimum identified in each period. The optimization model treats customer groups differently based on their purchasing behaviours. Policies and busi ness rules are varied to understand the effects on overall profit and customer groups. From the results, managerial implications are drawn to follow a customer-centric approach. To maximize profit, customers with high lifetime value should be treated as preferred if market demand is high and no other factors limit shipping decisions. Notable limitations include inventory availability and market demand. In market situations with limitations, higher-valued customers should be served first. Once a certain scale of the customer base is reached, it should also be focused on other customer groups. It is shown that the prediction accuracy of input data is a crucial concern for sufficiently optimizing box content. Further works could improve the chosen approach to better understand the effect of actions taking place in future sales periods.Na venda a retalho online, estão a surgir modelos de negócio try-before-you-buy. Os clientes podem encomendar artigos a serem enviados para casa para tentarem decidir que artigos guardar ou enviar de volta para o retalhista. Normalmente, este modelo de negócio é combinado com recomendações personalizadas. Nesta tese, é introduzido um modelo de optimização para apoiar a decisão sobre o que colocar numa caixa para obter o máximo lucro num período de vendas. São simulados vários períodos e o lucro global é analisado. O modelo de optimização trata de forma diferente grupos de clientes com base nos seus comportamentos de compra. As políticas são variadas para compreender os efeitos sobre o lucro global e os grupos de clientes. A partir dos resultados, as implicações de gestão são desenhadas para seguir uma abordagem centrada no cliente. Para maximizar o lucro, os clientes com elevado valor vitalício devem ser tratados como preferidos se a procura do mercado for elevada e nenhum outro factor limitar as decisões de expedição. Em situações de mercado com limitações, os clientes de valor mais elevado devem ser ser servidos em primeiro lugar. Uma vez atingida uma certa escala da base de clientes, esta deve também ser concentrada noutros grupos de clientes. Demonstra-se que a precisão da previsão dos dados de entrada é uma preocupação crucial. Outros trabalhos poderão melhorar a abordagem escolhida para compreender melhor o efeito das acções que têm lugar em períodos de vendas futuras

Optimizing B2B Product Offers with Machine Learning, Mixed Logit, and Nonlinear Programming

In B2B markets, value-based pricing and selling has become an important alternative to discounting. This study outlines a modeling method that uses customer data (product offers made to each current or potential customer, features, discounts, and customer purchase decisions) to estimate a mixed logit choice model. The model is estimated via hierarchical Bayes and machine learning, delivering customer-level parameter estimates. Customer-level estimates are input into a nonlinear programming next-offer maximization problem to select optimal features and discount level for customer segments, where segments are based on loyalty and discount elasticity. The mixed logit model is integrated with economic theory (the random utility model), and it predicts both customer perceived value for and response to alternative future sales offers. The methodology can be implemented to support value-based pricing and selling efforts. Contributions to the literature include: (a) the use of customer-level parameter estimates from a mixed logit model, delivered via a hierarchical Bayes estimation procedure, to support value-based pricing decisions; (b) validation that mixed logit customer-level modeling can deliver strong predictive accuracy, not as high as random forest but comparing favorably; and (c) a nonlinear programming problem that uses customer-level mixed logit estimates to select optimal features and discounts

Research Framework, Strategies, And Applications Of Intelligent Agent Technologies (IATs) In Marketing

In this digital era, marketing theory and practice are being transformed by increasing complexity due to information availability, higher reach and interactions, and faster speeds of transactions. These have led to the adoption of intelligent agent technologies (IATs) by many companies. As IATs are relatively new and technologically complex, several definitions are evolving, and the theory in this area is not yet fully developed. There is a need to provide structure and guidance to marketers to further this emerging stream of research. As a first step, this paper proposes a marketing-centric definition and a systematic taxonomy and framework. The authors, using a grounded theory approach, conduct an extensive literature review and a qualitative study in which interviews with managers from 50 companies in 22 industries reveal the importance of understanding IAT applications and adopting them. Further, the authors propose an integrated conceptual framework with several propositions regarding IAT adoption. This research identifies the gaps in the literature and the need for adoption of IATs in the future of marketing given changing consumer behavior and product and industry characteristics