Singh, Manpreet and Ghutla, Bhawick and Lilo-Jnr, Reuben and Mohammed, Aesaan and Rashid, Mahmood (2017) Walmart's Sales Data Analysis- A Big Data Analytics Perspective. [Conference Proceedings]
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Abstract
Information technology in this 21st century is reaching
the skies with large-scale of data to be processed and studied to make sense of data where the traditional approach is no more effective. Now, retailers need a 360-degree view of their consumers, without which, they can miss competitive edge of the market. Retailers have to create effective promotions and offers to meet its sales and marketing goals, otherwise they will forgo the major opportunities that the current market offers. Many times it is hard for the retailers to comprehend the market condition since their retail stores are at various geographical locations. Big Data application enables these retail organizations to use prior year's data to better forecast and predict the coming year's sales. It also enables retailers with valuable and analytical insights, especially determining customers with desired products
at desired time in a particular store at different geographical locations. In this paper, we analysed the data sets of world's largest retailers, Walmart Store to determine the business drivers and predict which departments are affected by the different scenarios (such as temperature, fuel price and holidays) and their impact on sales at stores’ of different locations. We have made use of Scala and Python API of the Spark framework to gain new insights into the consumer behaviours and comprehend
Walmart's marketing efforts and their data-driven strategies through visual representation of the analysed data.
| Item Type: | Conference Proceedings |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences |
| Depositing User: | Mahmood Rashid |
| Date Deposited: | 05 Jun 2018 03:42 |
| Last Modified: | 14 Feb 2022 22:50 |
| URI: | https://repository.usp.ac.fj/id/eprint/10798 |
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