.

the sub-category which has highest demand is storage supplies and labels supplies followed by the art supplies and other stationary products such as envelopes, binders, and papers. This histogram plot along the product dimension is shown in Figure 3.7.

Figure 3.6 Histogram plot for the customers’ frequency at city level in Maharashtra.

Additionally, box plot represents minimum, maximum, and median of sales in each category of every segment. The highest median for technology category is from consumer segment as shown in Figure 3.8. Similarly, the highest median for furniture category is from the corporate segment. Home office segment has the maximum sales in office supplies, and the highest median for the office supplies is from consumer segment.

Figure 3.7 Histogram plot for Mumbai along the product dimension.

Figure 3.8 Box plot for products across consumer segment.

In order to analyze the day that observes highest and minimum sale, authors suggest usage of pivot table as shown in Figure 3.9. From Figure 3.9, it is evident that every Saturday of August from 2011 to 2015 experiences maximum sale. However, the minimum sale is recorded on every Monday of November from 2011 to 2015. This gives an idea to retail to have an idea of its sales forecast.

Finally, the heatmap in Figure 3.10 shows the sales of various countries across the globe. From Figure 3.10, it is clear that United States records maximum sale in comparison to any other country. It is followed by sales of France and Australia. This analysis helps the retail industry to understand that there is a huge potential for increasing in sales in Southeast Asian Region and also in Oceania.

Figure 3.9 Pivot table.

Figure 3.10 Heatmap of the world.

      Thus, from the above case study, it is clear that data analytics can be quite helpful for a retail industry, and thus, it has a huge potential in retail apart from various promising fields.

3.5 Conclusion and Future Scope

      This chapter has discussed the potential and capability of ML approaches for predictive data analytics in the retail industry. Various models have also been discussed briefly. Few use cases have been presented to give readers a clear idea about the spectrum of its application in the retail industry. Although it has observed widespread applications, it still bears some challenges. These challenges as discussed above must be addressed by taking the research ahead.

      First and foremost, researchers must work in the direction of maintaining security and privacy of data as data is the most precious asset for any organization. Work should also be done in the direction of conceptualizing usage of big data so as to benefit retailers and customers. The research must be taken ahead in the direction of efficient customized promotions that basically sends promotional messages for a specific product to a specific customer at specific time. Implementation of customized promotion will further enhance the revenue generation. Additionally, it must also be ready to develop new operational models in response to the future need and growth of industry.

References

      1. Grewal, D., Motyka, S., Levy, M., The Evolution and Future of Retailing and Retailing Education. J. Mark. Educ., 40, 1, 85–93, 2018.

      2. Mangla, M., Akhare, R., Ambarkar, S., Context-Aware Automation Based Energy Conservation Techniques for IoT Ecosystem, in: Energy Conservation for IoT Devices, pp. 129–153, Springer, Singapore, 2019.

      3. Avinash, B.M. and Babu, S.H., Big Data Analytics – Its Impact on Changing Trends in Retail Industry. Int. J. Adv. Res. Comput. Eng. Technol., 7, 4, 379–382, 2018.

      4. Souza, T.T.P., Kolchyna, O., Treleaven, P.C., Aste , T., Twitter sentiment analysis applied to finance: A case study in the retail industry. arXiv preprint arXiv:1507.00784, 2015.

      5. Akhare, R., Mangla, M., Deokar, S., Wadhwa, V., Proposed Framework for Fog Computing to Improve Quality-of-Service in IoT Applications, in: Fog Data Analytics for IoT Applications, pp. 123–143, Springer, Singapore, 2020.

      6. Potluri, S., IOT Enabled Cloud Based Healthcare System Using Fog Computing: A Case Study. J. Crit. Rev., 7, 6, 1068–1072, 2020.

      7. Deokar, S., Mangla, M., Akhare, R., A Secure Fog Computing Architecture for Continuous Health Monitoring. In Fog Computing for Healthcare 4.0 Environments, pp. 269–290. Springer, Cham, 2021.

      8. Mangla, M., & Sharma, N., Fuzzy Modelling of Clinical and Epidemiological Factors for COVID-19, 2020.

      9. Chatterjee, J.M., Bioinformatics using machine learning. Global Journal of Internet Interventions and IT Fusion, 1, no. 1, 2018.

      10. Bradlow, E.T., Gangwar, M., Kopalle, P., Voleti, S., The Role of Big Data and Predictive Analytics in Retailing. J. Retail., 93, 1, 79–95, 2017, doi: 10.1016/j.jretai.2016.12.004.

      11. Krishna, A., Akhilesh, V., Aich, A., Hegde, C., Sales-forecasting of Retail Stores using Machine Learning Techniques. Proc. 2018 3rd Int. Conf. Comput. Syst. Inf. Technol. Sustain. Solut. CSITSS 2018, pp. 160–166, 2018, doi: 10.1109/CSITSS.2018.8768765.

      12. Cheriyan, S., Ibrahim, S., Mohanan, S., Treesa, S., Intelligent Sales Prediction Using Machine Learning Techniques. Proc. - 2018 Int. Conf. Comput. Electron. Commun. Eng. iCCECE 2018, August 2018, 53–58, 2019, doi: 10.1109/iCCECOME.2018.8659115.

      13. Pavlyshenko, B.M., Machine-learning models for sales time series forecasting. Data, 4, 1, 1–11, 2019, doi: 10.3390/data4010015.

      14. Aktas, E. and Meng, Y., An Exploration of Big Data Practices in Retail Sector. Logistics, 1, 2, 12, 2017, doi: 10.3390/logistics1020012.

      15. Ferreira, K.J., Lee, B.H.A., Simchi-Levi, D., Analytics for an online retailer: Demand forecasting and price optimization. Manuf. Serv. Oper. Manag., 18, 1, 69–88, 2016, doi: 10.1287/msom.2015.0561.

16. Nalchigar, S. and Yu, E., Business-driven data analytics: A conceptual modeling framework. Data Knowl. Eng., 117, 2017, 359–372, 2018, doi: 10.1016/j. datak.2018.04.006.

      17. Lam, D. A Survey of Predictive Analytics in Data Mining with Big Data. Athabasca University, 2014.

      18.