Business Analytics for Sales Performance, Profitability Assessment, and Payment-Mode Decision-Making in Retail Commerce
Keywords:
Business analytics, Retail commerce, Sales performance, Profitability assessment, Payment-mode decision-makingAbstract
The retail commerce is getting reliant on systematic elucidation of transaction records to comprehend the strength of sales, profit behaviour and business results linked to payments. This article has analyzed the data on retail transactions to assess the performance of sales, trend of profitability, and decision-making on payment-mode based on product categories, sub-categories, regions and monthly. An order level and product line descriptive business analytics approach was used with the information of sales amount, quantity, profit, customer location, category, sub-category and mode of payment. The findings indicated that total sales were 437,771, total profit was 36,963 and overall profit margin was 8.44% with 500 orders made and 1,500 product-line entries. The highest sales value of 166,267 was registered by Electronics and the highest profit of 13,325 by Clothing meaning that being the leader in terms of revenue did not automatically translate to being the leader in terms of profit. The analysis of payment-mode revealed that COD generated the highest sales value of 155,181 and the highest profit margin of 14.51 was obtained by the credit card. The sales showed a distinct performance difference on a regional and monthly basis as well with Maharashtra showing sales ahead and the high profit margin of 21.73 in February. The results showed that descriptive business analytics is able to make feasible business choices in retailing by pinpointing lucrative classifications, feeble segments, geographical differences, and payment methods that possess greater managerial worth.
Downloads
References
Adhikary, A., Diatha, K. S., Borah, S. B., & Sharma, A. (2021). How does the adoption of digital payment technologies influence unorganized retailers’ performance? An investigation in an emerging market. Journal of the Academy of Marketing Science, 49(5), 882–902. https://doi.org/10.1007/s11747-021-00778-y
Alqhatani, A., Ashraf, M. S., Ferzund, J., Shaf, A., Abosaq, H. A., Rahman, S., Irfan, M., & Alqhtani, S. M. (2022). 360° Retail Business Analytics by Adopting Hybrid Machine Learning and a Business Intelligence Approach. Sustainability, 14(19), 11942. https://doi.org/10.3390/su141911942
Alsmadi, A. A., Shuhaiber, A., Al-Okaily, M., Al-Gasaymeh, A., & Alrawashdeh, N. (2024). Big data analytics and innovation in e-commerce: Current insights and future directions. Journal of Financial Services Marketing, 29(4), 1635–1652. https://doi.org/10.1057/s41264-023-00235-7
Berg, T., Burg, V., Keil, J., & Puri, M. (2025). The economics of “Buy Now, Pay Later”: A merchant’s perspective. Journal of Financial Economics, 171, 104093. https://doi.org/10.1016/j.jfineco.2025.104093
Calixto, N., & Ferreira, J. (2020). Salespeople Performance Evaluation with Predictive Analytics in B2B. Applied Sciences, 10(11), 4036. https://doi.org/10.3390/app10114036
Fergurson, J. R. (2020). Data-driven decision making via sales analytics: Introduction to the special issue. Journal of Marketing Analytics, 8(3), 125–126. https://doi.org/10.1057/s41270-020-00088-2
González Morales, M., & Cavero Rubio, J. A. (2023). Impact of Digitalization of Sales on the Profitability of the Restaurant Industry during COVID-19. Economies, 11(11), 283. https://doi.org/10.3390/economies11110283
Griva, A., Bardaki, C., Pramatari, K., & Doukidis, G. (2022). Factors Affecting Customer Analytics: Evidence from Three Retail Cases. Information Systems Frontiers, 24(2), 493–516. https://doi.org/10.1007/s10796-020-10098-1
Kusuma, A. R., Syarief, R., Sukmawati, A., & Ekananta, A. (2024). Factors influencing the digital transformation of sales organizations in Indonesia. Heliyon, 10(5), e27017. https://doi.org/10.1016/j.heliyon.2024.e27017
Lou, L., Tian, Z., & Koh, J. (2017). Tourist Satisfaction Enhancement Using Mobile QR Code Payment: An Empirical Investigation. Sustainability, 9(7), 1186. https://doi.org/10.3390/su9071186
Mostaghel, R., Oghazi, P., Parida, V., & Sohrabpour, V. (2022). Digitalization driven retail business model innovation: Evaluation of past and avenues for future research trends. Journal of Business Research, 146, 134–145. https://doi.org/10.1016/j.jbusres.2022.03.072
Okunev, R. (2022). Epilogue. In R. Okunev, Analytics for Retail (pp. 143–145). Apress. https://doi.org/10.1007/978-1-4842-7830-7_12
Panay, B., Baloian, N., Pino, J. A., Peñafiel, S., Frez, J., Fuenzalida, C., Sanson, H., & Zurita, G. (2021). Forecasting Key Retail Performance Indicators Using Interpretable Regression. Sensors, 21(5), 1874. https://doi.org/10.3390/s21051874
Qi, M., Mak, H., & Shen, Z. M. (2020). Data‐driven research in retail operations—A review. Naval Research Logistics (NRL), 67(8), 595–616. https://doi.org/10.1002/nav.21949
Raghupathi, W., & Raghupathi, V. (2021). Contemporary Business Analytics: An Overview. Data, 6(8), 86. https://doi.org/10.3390/data6080086
Rooderkerk, R. P., DeHoratius, N., & Musalem, A. (2022). The past, present, and future of retail analytics: Insights from a survey of academic research and interviews with practitioners. Production and Operations Management, 31(10), 3727–3748. https://doi.org/10.1111/poms.13811
Samruddhi Bhosale. (2024). Online Sales Data. https://www.kaggle.com/datasets/samruddhi4040/online-sales-data
Vukovic, D. B., Spitsina, L., Gribanova, E., Spitsin, V., & Lyzin, I. (2023). Predicting the Performance of Retail Market Firms: Regression and Machine Learning Methods. Mathematics, 11(8), 1916. https://doi.org/10.3390/math11081916
Wang, C.-H., & Gu, Y.-W. (2022). Sales Forecasting, Market Analysis, and Performance Assessment for US Retail Firms: A Business Analytics Perspective. Applied Sciences, 12(17), 8480. https://doi.org/10.3390/app12178480
Yan, L.-Y., Tan, G. W.-H., Loh, X.-M., Hew, J.-J., & Ooi, K.-B. (2021). QR code and mobile payment: The disruptive forces in retail. Journal of Retailing and Consumer Services, 58, 102300. https://doi.org/10.1016/j.jretconser.2020.102300
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
