“CS & IT Department”
Submitted By:
Syeda Razi Fatima
Class:
BSSE 6th Self
Roll no:
BSSE51F20S042
Submitted To:
Ma’am Farhana
Subject:
Software Construction & Development
Date:
03-24-2023.
Sure, here is an example of a data modeling research paper that includes abstract,
literature review, research gap, and suggestions:
Title:
"Data Modeling for Improved Customer Relationship Management:
A Review of Current Practices and Future Directions"
Abstract: Customer relationship management (CRM) is essential for businesses to
maintain customer loyalty and increase profitability. Data modeling is a critical tool for
analyzing customer data and improving CRM strategies. This paper provides a
comprehensive review of current data modeling practices in CRM and identifies research
gaps. We suggest several areas for future research to enhance data modeling for CRM.
Introduction: The introduction provides an overview of the importance of CRM in business
operations and highlights the need for effective data modeling. It also introduces the
research gap, which is the lack of a comprehensive review of data modeling practices in
CRM.
Literature Review: The literature review discusses the current state-of-the-art in
data modeling for CRM, including the most commonly used modeling techniques and data
sources. It also highlights the limitations of current practices, such as the inability to handle
unstructured data and the challenge of integrating data from different sources.
Research Gap: The research gap identified in this paper is the need for a more
comprehensive review of data modeling practices in CRM. While there have been several
studies on specific aspects of data modeling for CRM, there is a need for a more holistic
review that considers the different modeling techniques, data sources, and challenges faced
by businesses in implementing CRM strategies.
Research Suggestions: Based on the literature review and research gap, this paper
suggests several areas for future research. These include:
1. Developing data modeling techniques that can handle unstructured data such as
social media feeds and customer reviews.
2. Investigating the use of advanced machine learning techniques, such as deep
learning and neural networks, for improving the accuracy of customer segmentation
and prediction of customer behavior.
3. Exploring the integration of data from different sources, including internal
databases, social media, and third-party data providers, to develop a more
comprehensive view of customer behavior.
4. Developing data modeling techniques that can account for changes in customer
behavior over time and adjust CRM strategies accordingly.
Conclusion: The paper concludes by summarizing the current state-of-the-art in data
modeling for CRM and identifying the research gap. It also suggests several areas for future
research that can help businesses improve their CRM strategies and increase customer
loyalty.