Journal Press India®

Data Mining for Improving Customer Relationship Management in Intelligent Business

Vol 9 , Issue 1 , January - June 2020 | Pages: 37-42 | Research Paper

Author Details ( * ) denotes Corresponding author

1. * Vikas Kumar Sharma, Department of Mechanical Engineering, GLA University, Mathura, Uttar Pradesh, India (
2. Piyush Singhal, Department of Mechanical Engineering, GLA University, Mathura, Uttar Pradesh, India
3. Rajkumar Sharma, Department of Mechanical Engineering, GLA University, Mathura, Uttar Pradesh, India

Retailing has been planned to address spatial and transient gaps between the manufacturer and the end customer. The difficulty of monitoring retail sales however is exacerbated by the variety of consumers' geographical areas and the frequency of retail visits. In reality, this is a crucial knowledge for designing marketing relationships (CRM) strategies for consumers. Data mining is typically a way of extracting data from different backgrounds (also called data or knowledge discovery) and summing it up in valuable data information that could be used for the highest gain, minimization of expense, or both. Data mining is technically the practice of finding correlations or patterns in thousands of fields in large linked database networks. Data mining is conducted using proprietary software to convert and distinguish the relationships between data from various measures or perspectives. Although data extraction is a relatively new idea, it is not a science. Constant advancement in computer processing, computing and computer systems substantially improves the accuracy of the analysis and reduces costs. Transaction information in a retail data warehouse can be examined in a "customer-centric" or "visit-centric" retailing approach, which explores briefly the design and the growth of the sector and best practices. It analyzes the different aspects and problems that also need to be tackled by using data mining in retail transactions.


(CRM) strategies, Data mining, Challenges, Retailing, Retail transactions.

  1. Madan LalBhasin, “Data Mining: A Competitive Tool in the Banking and RetalIndustries", TheChartered Accountant, October 2006.
  2. ChidanandApte, Bing Liu, Edwin P.D. Pednault, Padhraic Smyth, “Business ApplicationofData Mining", May 22, 2002.
  3. Robert Groth, “Data Mining- Building Competitive Advantage", industry Applications of Data Mining, Chapter 8, pp-192-210 
  4. Sharma, R., Pathak, D.K. and Dwivedi, V.K., 2014, December. Modeling & simulation of spring mass damper system in simulink environment. In XVIII Annual International Conference of the Society of Operations Management Theme: Operations Management in Digital Economy (pp. 205-210).
  5. Rahman, A. and Khan, M.N.A., 2017. An assessment of data mining based CRM techniques for enhancing profitability. International Journal of Education and Management Engineering, 7(2), p.30.
  6. Chiang, W.Y., 2018. Applying data mining for online CRM marketing strategy: An empirical case of coffee shop industry in Taiwan. British Food Journal.
  7. Kenneth L. Reed, Joseph K. Berry, “Spatial Modeling and Data Mining in Retail -Customer Loyalty, Competition Analysis, Propensity to Defect and Ad Media Selection”, Presented at the GIS 99 Conference, Vancouver, British Columbia, March 1-4, 1999.
  8. Stephanie Roussel-Dupre, “Data Mining Provides Retail Understanding", Integrated Solutions for Retails, Feb.2001.
  9. Parvaneh, A., Tarokh, M. and Abbasimehr, H., 2014. Combining data mining and group decision making in retailer segmentation based on LRFMP variables. International Journal of Industrial Engineering & Production Research, 25(3), pp.197-206.
  10. Goodwin, K. and Highfield, K., 2013. A framework for examining technologies and early mathematics learning. In Reconceptualizing early mathematics learning (pp. 205-226). Springer, Dordrecht.
  11. Ron Kohavi, Llew Mason. Rajesh Parekh, Zijian Zheng, “Lessons and Challenges from Mining Retail E-Commerce Data”, Machine Learning journal, Special Issue on Data Mining Lessons Learned, 2004. 
  12. Sharma, R. and Singhal, P., 2014. An Optimal Treatment to Supply Chain Disruptions Using Model Predictive Control. Proceedings of SOM, 2014.
  13. Tylka, J.G. and Choueiri, E.Y., 2020. Performance of linear extrapolation methods for virtual sound field navigation. Journal of the Audio Engineering Society, 68(3), pp.138-156
  14. Jonathan Wu, “The Value in Mining Data-Business Intelligence", DM Review Online, February 1, 2002. 
  15. V. Kumar, R. Sharma, P. Singhal, Demand Forecasting of Dairy Products for Amul Warehouses using Neural Network, Int. J. Sci. Res. (2019)
  16. "Get Ready for Wal-Mart - How to Thrive in the New Global Retail Scene", John C. Williams John A. Torella, J. C. Williams Group, Nov. 2007
  17. Sandberg, J., Mathiassen, L. and Napier, N., 2014. Digital options theory for IT capability investment. Journal of the Association for Information Systems, 15(7), p.1.
  18. Linoff, G.S. and Berry, M.J., 2011. Data mining techniques: for marketing, sales, and customer relationship management. John Wiley & Sons.
Abstract Views: 4
PDF Views: 35

Advanced Search


3rd International Co...

About the Conference At the outset, the multifarious Covid-19 pande...

International Confer...

ABOUT THE INTERNATIONAL CONFERENCE Today, the world of business is ...

Call for Reviewers

In keeping with JPI’s policy of commitment to high standards of ...

Call for papers

JPI invites original and unpublished manuscripts in the areas of comme...

By continuing to use this website, you consent to the use of cookies in accordance with our Cookie Policy.