Globally retail industries are growing day by day, because retail industries give more profit in less time duration. In 2017 USD 23,460 billion was the value of global retail industry, it is expected to increase 5.3% during the forecast period (2018-2023) reaching to USD 31,880.8 by 2023 [11]. Every business revolves around one word – Profit! Every business man wants to increase the profit of his business, no one wants to loose. The best way to increase the profit is by extracting knowledge about the business and transforming that knowledge into right predictions. In retail sales business, prediction of future sales is very much essential to improve the business operation and to increase profit. Manually analysing large amount of data for predicting future sales may lead to less accurate results. Statistical techniques were used initially to forecast future sales, later Datamining techniques were inculcated into the process of prediction. Only Datamining techniques were not sufficient to accurately predict the sales, so Artificial Intelligence (AI) domain is chosen by software professionals for prediction. Machine Learning (ML) is an application of AI and Deep Learning (DL) is an upgradation of ML especially Artificial Neural Network (ANN). Various ML and DL prediction models are gaining more attention in recent days [12]. The models can be chosen based on the type of data that is being analysed and the response time of prediction models. This paper provides review of various prediction models used for sales prediction in retail industries based on data features and models.

Data Mining, Deep Learning, Machine Learning and Retail sales prediction

[1]. Patrick Meulstee and Mykola Pachenizkiy, “Food Sales Prediction: ‘If Only It Knew What We Know’”, 2008 IEEE International Conference on Data Mining Workshops.
[2]. Ritanjali Majhi, Ganapati Panda, Babita Majhi, S. K. Panigrahi and Manoj Ku. Mishra, “Forecasting of Retail Sales Data Using Differential Evolution”, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC 2009).
[3]. Pradip Kumar Bala, “Mining Changes in Purchase Behavior in Retail Sale with Products as Conditional Part”, 2010 IEEE.
[4]. M. Ahsan Akhtar Hasin, Shuvo Ghosh, and Mahmud A. Shareef, “An ANN Approach to Demand Forecasting in Retail Trade in Bangladesh”, 2011 International Journal of Trade, Economics and Finance, Vol. 2, No. 2.
[5]. Dilek Penpece and Orhan Emre Elma, “Predicting Sales Revenue by Using Artificial Neural Network in Grocery Retailing Industry: A Case Study in Turkey”, 2014 International Journal of Trade, Economics and Finance, Vol. 5, No. 5.
[6]. Anita S Harsoor. “Forecast of sales of Wallmart Sotres using Big Data Applications”, 2015 IJRET.
[7]. Yuta Kaneko and Katsutoshi Yada, “A Deep Learning Approach for the Prediction of Retail Store Sales”, 2016 IEEE 16th International Conference on Data Mining Workshops.
[8]. Yi Zuo, Katsutoshi Yada and A B M Shawkat Ali, “Prediction of Consumer Purchasing in a Grocery Store Using Machine Learning Techniques”, 2016 IEEE 3rd Asia-Pacific World Congress on Computer Science and Engineering.
[9]. Shucheng Mou, Yang Ji and Chujie Tian, “Retail Time Series Prediction Based on EMD and Deep Learning”, 2018 IEEE Proceedings of IC-NIDC.
[10]. Jing Zhang and Juan Li, “Retail Commodity Sale Forecast Model Based on Data Mining”, 2016 IEEE International Conference on Intelligent Networking and Collaborative Systems.
[11]. https://www.businesswire.com/news/home/20190102005213/en/Global-31.88-Billion-Retail-Industry-Analysis-Outlook
[12]. Venkatesh Shankar, “How Artifi cial Intelligence (AI) Is Reshaping Retailing”, Elsevier Publications, Invited Commentary / Journal of Retailing 94 (4, 2018).
[13]. Mohit Gurnani, Yogesh Korkey, Prachi Shahz, Sandeep Udmalex, Vijay Sambhe and Sunil Bhirudk, “Forecasting of sales by using fusion of Machine Learning techniques”, 2017 IEEE, International Conference on Data Management, Analytics and Innovation (ICDMAI), pp 93-101.
[14]. Rana Alaa El-Deen Ahmed, M.Elemam.Shehab, Shereen Morsy, Nermeen Mekawie, “Performance study of classification algorithms for consumer online shopping attitudes and behavior using data mining” 2015 IEEE, Fifth International Conference on Communication Systems and Network Technologies, pp 1344-1349.
[15]. Vishal Shrivastava, Prem narayan Arya, “A Study of Various Clustering Algorithms on Retail Sales Data”, International Journal of Computing, Communications and Networking -2012, Vol.1 No.2. pp 68-74.