Customer Centric Sales Analysis and Prediction
Shiwani Joshi1, Lavi Samuel Rao2, B. Ida Seraphim3
1Shiwani Joshi, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Lavi Samuel Rao, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
3B. Ida Seraphim, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1749-1753 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6417048419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: For successful business management, sales prediction plays an inevitable part. Data mining techniques have been employed since a long time for sales analysis. In the past, the prediction has been done from various point of views always keeping mind the needs of the customer and the profitability of the business in consideration. Initially, sales prediction has been done using Market Basket analysis, wherein using the previous data the next item , which is most likely to be purchased is predicted. At later stages, the products on the shelf were only considered for predicting sales in a supermarket. Thereafter , for a range of supermarkets the location and time was considered to predict the sales of items. For predicting the sales a number of algorithms such as AIS, Apriori, FP Growth, FP Bonsai have been employed. In this paper, the price or the amount to be likely spent by the customer will be predicted using various other algorithms and a comparison between the different algorithms is outlined.
Keywords: Business Management, Data Mining, Sales Analysis, Prediction
Scope of the Article: Prediction