Predictive Data Mining Model for Electronic Customer Relationship Management Intelligence

Predictive Data Mining Model for Electronic Customer Relationship Management Intelligence

Bashar Shahir Ahmed (Computer Science and Systems Engineering Laboratory, University Abdelmalek Essaadi, Tetouan, Morocco), Mohamed Larabi Ben Maâti (Computer Science and Systems Engineering Laboratory, University Abdelmalek Essaadi, Tetouan, Morocco) and Mohammed Al-Sarem (College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia)
Copyright: © 2020 |Pages: 10
DOI: 10.4018/IJBIR.2020070101
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The rising adoption of e-CRM strategies in marketing and customer relationship management has necessitated to more needs especially where a specific customer segment is targeted and the services are personalized. This paper presents a distributed data mining model using access-control architecture in a bid to realize the needs for an online CRM that intends to deliver web content to a specific group of customers. This hybrid model utilizes the integration of the mobile agent and client server technologies that could easily be updated from the already existing web platforms. The model allows the management team to derive insights from the operations of the system since it focuses on e-personalization and web intelligence hence presenting a better approach for decision support among organizations. To achieve this, a software approach made of access-control functions, data mining algorithms, customer-profiling capability, dynamic web page creation, and a rule-based system is utilized.
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1. Introduction

The current business world has adopted-to a larger extent, the concept of e-business where companies utilize the internet platform for interactions and effective communication with their customers. This has increased the interaction points compared to the initial face-to-face interactions. E-CRM is a technique employed to create a loyal online customer relationship (Berry & Linoff, 2004). Using the technique, it is also possible to discover the best and most rewarding way of realizing such a relationship using the web platform. This is an always growing process for retaining, growing and acquiring loyal and profitable customers using the internet.

E-CRM, to be specific, focuses mainly in the areas of decision support, customer management and personalized service. Personalized service refers to the provision of better customer service by presenting the right and correct information to the right and intended customers (Berry & Linoff, 2004). Normally, this is achieved on a web platform (Chua, 2011). This enables the users to acquire the information of their interest hence enhancing the experience of the user on the website. Customer management on the other hand operates through the main internet points of communication between the customer and the company (Chua, 2011). This mainly involves proactive direct communication to the customers via emails, newsletters and even interactive online help desk. This may also include automation of sales force (Chua, 2011) (Figure 1).

Figure 1.

e-CRM covers three business areas and their functions


For sound decisions by management on how to better run e-CRM, it is important that one has a better understanding of the business operations and the targeted customer base. Effective business intelligence is that which has been obtained timely. Due to the ever-changing pace of e-business, the information has been evolving too. These business areas together assist in the retaining and attraction of loyal customers (Berry & Linoff, 2004).

Generally, this paper appreciates the three main technologies of web log analyzer, web spy and data-warehousing (includes decision support modules and data-mining modules). This is in regard to the targeted areas of personalized service and decision support. Each of the technologies gives some better insight into the aspects of the business. Most particularly, use of web log analyzer is not enough to give detailed understanding of business intelligence. Nonetheless, it gives a detailed report on the statistics of the website. It is however important that other information like the market positions, customers and their behaviors and competitor information. In this article, the knowledge on the customer will be utilized in the e-personalization of the web content based on the customer’s needs (Berry & Linoff, 2004).

In order to attain the objectives as depicted in Figure 2, the Access-control architecture is proposed (Bashar Ahmed, M.L. Ben Maȃti, & Badreddine Al Mohajir, 2014). This can be built on an existing website. The key objective is to distribute the functions of business intelligence and e-CRM with the help of an agent-architecture. Another objective is to monitor the accesses by the users on the website.

Figure 2.

The conceptual view of the e-CRM architecture and the objectives


Predictive Analytics are all about finding the insights that will help you understand what might happen in the future. They help you recognize patterns in historical data, repeated transactions, and relationship cues. If you use predictive analytics effectively, you can facilitate a more proactive business approach.

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