Skip to main content

A Big Data Analytics-Based Design for Viable Evolution of Retail Sector

  • Conference paper
  • First Online:
Intelligent Computing and Innovation on Data Science

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 118))

  • 933 Accesses

Abstract

Retailing is one of the world’s prominent and most diversified commercial activities, which has considerably transformed business strategies for earning more profit. Today, the retailing definition is a synonym to attractive and appropriately managed merchandise stores with incredible comfort and ambience rather than randomly stacked traditional stores. Also, the modern customer is focused towards quality/brands and expects for services delivered to them by different vendors at the ease of home with a single click. As a result, customers prefer to shop from various online shopping Websites rather than physically moving to a retail store, which in turn leads to the downfall in the sales of retailers which has become a significant threat to them. Therefore, this paper highlights this current problem faced by retailers and suggests some corrective measures, which retailers should deploy. Consequently, the retailers are required to practice corrective measures towards meeting all customers’ expectations by providing their necessary goods under the same roof. Besides, the retailers should provide several benefits and lucrative offers like discounts, cashbacks, buy one get one, free home delivery, combo purchase and other tailor-made offers to attract customers via targeted marketing by meeting their specific needs and hence to overcome diversion of their customers towards E-Commerce Websites.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. In: ACM Sigmod Record. ACM, pp 207–216

    Google Scholar 

  2. Agrawal R, Shafer JC (1996) Parallel mining of association rules. IEEE Trans Knowl Data Eng 8(6):962–969

    Article  Google Scholar 

  3. Davey I (2014) Technologies: consumers, big data, and online tracking in the retail industry: a case study of Walmart. Center for Media Justice

    Google Scholar 

  4. Farzanyar Z, Cercone N (2013) Accelerating frequent itemsets mining on the cloud: a MapReduce-based approach. In: 2013 IEEE 13th international conference on data mining workshops (ICDMW), IEEE, pp 592–598

    Google Scholar 

  5. Gatzioura A, Sànchez-Marrè M (2015) A case-based recommendation approach for market basket data. IEEE Intell Syst 30(1):20–27

    Article  Google Scholar 

  6. Gupta D, Singh SK, Malhotra D, Verma N (2017) EPRT-an ingenious approach for E-Commerce website ranking. Int J Comput Intell Res 13(6):1471–1482

    Google Scholar 

  7. Li L, Zhang M (2011) The strategy of mining association rule based on cloud computing. In: 2011 international conference on business computing and global informatization, IEEE, pp 475–478

    Google Scholar 

  8. Malhotra D, Rishi OP (2016) IMSS-E: an intelligent approach to the design of adaptive metasearch system for E-commerce website ranking. In: Proceedings of the international conference on advances in information communication technology & computing. ACM, p 3

    Google Scholar 

  9. Malhotra D, Verma N (2013) An ingenious pattern matching approach to ameliorate web page rank. Int J Comput Appl 65(24):33–39

    Google Scholar 

  10. Malhotra D (2014) Intelligent web mining to ameliorate web page rank using back-propagation neural network. In: 5th international conference on confluence the next generation information technology summit (Confluence), IEEE, pp 77–81

    Google Scholar 

  11. Malhotra D, Rishi OP (2016) IMSS-E: an intelligent approach to design of adaptive meta search system for E-Commerce website ranking. In: Proceedings of the international conference on advances in information communication technology & computing, ACM. https://doi.org/10.1145/2979779.2979782

  12. Malhotra D, Malhotra M, Rishi OP (2017) An innovative approach of web page ranking using hadoop- and map reduce-based cloud framework. In: Proceedings of advances in intelligent systems and computing, vol 654, CSI-2015. Springer, Heidelberg, pp 421–427

    Google Scholar 

  13. Malhotra D, Rishi OP (2017) IMSS: a novel approach to design of adaptive search system using second generation big data analytics. In: Proceedings of international conference on communication and networks. Springer, Heidelberg, pp 189–196

    Google Scholar 

  14. Malhotra D, Verma N, Rishi OP, Singh J (2017) Intelligent big data analytics: adaptive E-Commerce website ranking using Apriori Hadoop–BDAS-based cloud framework. Maximizing business performance and efficiency through intelligent systems, IGI Global, pp 50–72

    Google Scholar 

  15. Malhotra D, Rishi OP (2018a) An intelligent approach to design of E-Commerce metasearch and ranking system using next-generation big data analytics. J King Saud Univ-Comput Inf Sci (Elsevier). https://doi.org/10.1016/j.jksuci.2018.02.015

  16. Malhotra D, Rishi OP (2018b) IMSS-P: an intelligent approach to design & development of personalized meta search & page ranking system. J King Saud Univ-Comput Inf Sci (Elsevier). https://doi.org/10.1016/j.jksuci.2018.11.013

  17. Malhotra D, Rishi OP (2019) A comprehensive review from hyperlink to intelligent technologies based personalized search systems. J Manage Analytics 1–25 (Taylor & Francis)

    Google Scholar 

  18. Saabith AS, Sundararajan E, Bakar AA (2016) Parallel implementation of Apriori algorithms on the Hadoop-Mapreduce platform-an evaluation of literature. J Theor Appl Inf Technol 85(3):321

    Google Scholar 

  19. Sethi S, Malhotra D, Verma N (2016) Data mining: current applications & trends. Int J Innov Eng Technol 6(4):586–589

    Google Scholar 

  20. Svetina M, Zupančič J (2005) How to increase sales in retail with market basket analysis. Syst Integr 418–428

    Google Scholar 

  21. Tian L, Li L, Wang X (2012) Study of identifying cross-selling for online retailers in E-commerce. In: 2012 fourth international conference on computational and information sciences, IEEE, pp 417–420

    Google Scholar 

  22. Verma N, Singh J (2017) A comprehensive review from sequential association computing to Hadoop-MapReduce parallel computing in a retail scenario. J Manage Analytics 4(4):359–392

    Article  MathSciNet  Google Scholar 

  23. Verma N, Singh J (2015) Improved web mining for e-commerce website restructuring. In: 2015 IEEE international conference on computational intelligence & communication technology (CICT), IEEE, pp 155–160

    Google Scholar 

  24. Verma N, Singh J (2017) An intelligent approach to big data analytics for sustainable retail environment using Apriori-MapReduce framework. Ind Manage Data Syst 117(7):1503–1520

    Article  Google Scholar 

  25. Verma N, Malhotra D, Malhotra M, Singh J (2015) E-commerce website ranking using semantic web mining and neural computing. Proc Comput Sci 45:42–51

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Malhotra, N., Malhotra, D., Rishi, O.P. (2020). A Big Data Analytics-Based Design for Viable Evolution of Retail Sector. In: Peng, SL., Son, L.H., Suseendran, G., Balaganesh, D. (eds) Intelligent Computing and Innovation on Data Science. Lecture Notes in Networks and Systems, vol 118. Springer, Singapore. https://doi.org/10.1007/978-981-15-3284-9_43

Download citation

Publish with us

Policies and ethics