CUSTOMER PREDICTIVE ANALYTICS USING ARTIFICIAL INTELLIGENCE

This conceptual paper exclusively focused on how artificial intelligence (AI) serves as a means to identify a target audience. Focusing on the marketing context, a structured discussion of how AI can identify the target customers precisely despite their different behaviors was presented in this paper. The applications of AI in customer targeting and the projected effectiveness throughout the different phases of customer lifecycle were also discussed. Through the historical analysis, behavioral insights of individual customers can be retrieved in a more reliable and efficient way. The review of the literature confirmed the use of technology-driven AI in revolutionizing marketing, where data can be processed at scale via supervised or unsupervised (machine) learning.

The mediator role of sales force management capability on the relation between market orientation and financial performance

Purpose: This study demonstrates that sales force management capability (SFMC) is a driver to financial performance when influenced by marketing orientation. Wherefore, the researchers explore how each SFMC ́s dimension (salesforce structuring, talent management, and customer targeting) contributes on the relation between MO and financial performance. Methodology: A survey (n=223) was applied in Information and Communication Technology (ICT) companies in Brazil. Structural equation modeling allowed us to understand how the sales management forces capability ́s dimensions influence the relation between market orientation and financial performance intensity. Findings: It is demonstrated that MO influences the three SFMC capability dimensions (salesforce structuring, talent management, and customer targeting), mainly customer targeting and talent management. Furthermore, it is shown that the relation between MO and financial performance is mediated by SFMC capability. Theoretical contributions: This study contributes to the marketing literature by showing that MO may be applied jointly to SFMC in order to improve performance. Furthermore, it is demonstrated that SFMC to enhance financial performance. Consequently, this study promotes a better understanding of the process in which MO may be related to SFMC, and how each specific dimension of SFMC influences financial performance. Practical contributions: Marketing managers and sales managers should sharing information about the market to achieve better financial results and competitive advantage. Also, practitioners should use market information strategically to segmenting and positioning sales force. In addition to customers, competitors, and environmental forces, petitioners should make an effort to manage their organizational resources better. Firms should use market information to improve performance and engage marketing managers to support sales investment.

A Framework for CRM

This chapter aims to develop a framework for CRM in order to understand CRM concepts and ecosystem better. To achieve this aim, this chapter starts with giving definitions of CRM from different perspectives. Reviewing the definitions of CRM reveals the essential pillars of CRM implementation. A well-developed CRM implementation framework can help companies see the big picture and develop successful CRM implementations. This chapter explains the vision and strategy; customer value proposition and customer experience; organizational culture and customer centric processes; and enabling technologies and information management as the fundamental components of a successful CRM initiative. After mentioning the benefits of CRM implementations, this chapter continues with analysing the architecture of the CRM ecosystem. Key functionalities and the role of analytical, operational, and collaborative CRM in managing relationships are also clarified. This chapter ends up with discussing the functionalities of CRM technologies in understanding customers, customer targeting, customer acquisition and retention, and customer service and support.

Modeling a fuzzy system for assisting the customer targeting decisions in retail companies

This paper reveals a proposal of a decision support system, enabling retail companies' marketing managers with powerful tools for the customers' portfolios targeting. The innovative methodological approach highlights a fuzzy algorithm based system that captures and computes relevant information about customers, providing a managerial support for making the most effective decisions, according to different degrees of affiliation assigned to customers. The contributions of fuzzy algorithms on targeting decisions reflects the affiliation of customers to a specific cluster, based on computation of the input vectors, income and seniority, leading to performing marketing decisions.

A Framework for CRM

This chapter aims to develop a framework for CRM in order to understand CRM concepts and ecosystem better. To achieve this aim, this chapter starts with giving definitions of CRM from different perspectives. Reviewing the definitions of CRM reveals the essential pillars of CRM implementation. A well-developed CRM implementation framework can help companies see the big picture and develop successful CRM implementations. This chapter explains the vision and strategy; customer value proposition and customer experience; organizational culture and customer centric processes; and enabling technologies and information management as the fundamental components of a successful CRM initiative. After mentioning the benefits of CRM implementations, this chapter continues with analysing the architecture of the CRM ecosystem. Key functionalities and the role of analytical, operational, and collaborative CRM in managing relationships are also clarified. This chapter ends up with discussing the functionalities of CRM technologies in understanding customers, customer targeting, customer acquisition and retention, and customer service and support.