Type: | Article | |
Title: | Applied Sales Predictive Analytics for Business Development | PDF Article |
Author: | Keisha LaRaine Ingram | |
On-line: | 22-September-2024 | |
Orcid: | https://orcid.org/0000-0001-9136-1896 | |
Metrics: | Applied Business: Issues & Solutions 1(2024)11_16 - ISSN 2783-6967. | |
DOI: | 10.57005/ab.2024.1.2 | |
URL: | http://www.applied-business-solutions.eu/h24/2024_1_2.html | |
| ||
Abstract. | In the dynamic business environment, leveraging predictive analytics for sales optimization and business development has become crucial for achieving sustained growth. As the e-commerce landscape continues to evolve, many e-businesses must harness the power of predictive analytics to anticipate sales trends and optimize business development strategies. This paper explores the application of sales predictive analytics, focusing on its role in forecasting sales, optimizing resource allocation, and enhancing customer relationship management. The application of predictive analytics in sales forecasting in online marketplace platforms is also explored, through the examination of various predictive models using real-world case studies. By exploring various methodologies and tools, the study illustrates how predictive analytics can be integrated into e-businesses' operations to drive growth, and enhance decision-making, highlighting the transformative potential of analytics in making data-driven decisions, ultimately fostering sustainable growth and competitive advantage. Through the analysis of historical sales data, consumer behaviour patterns, and market trends, predictive analytics provides actionable insights that are crucial for strategic planning and operational efficiency. The paper also addresses challenges and best practices for implementing predictive analytics into the business process of e-businesses. | |
| ||
JEL: | L810, D110, D111. | |
Keywords: | Sales process automation; Customer journey analytics; Attrition modelling; Sales trend analysis; Lead scoring. | |
| ||
Citation: | Keisha LaRaine Ingram (2024) Applied Sales Predictive Analytics for Business Development. - Applied Business: Issues & Solutions 1(2024)11-16 - ISSN 2783-6967. | |
https://doi.org/10.57005/ab.2024.1.2 | ||
| ||
References. |
1. Chopra, S.; Marrow, T. (2019) Data-Driven Sales Strategy: Using Analytics to Drive Sales Performance. - McGraw-Hill Education. 2. Davenport, T. H.; Harris, J. G. (2017) Competing on Analytics: Updated, with a New Introduction: The New Science of Winning. - Harvard Business Review Press (2017). 3. Gartner (2021) Predictive Analytics in E-commerce: Trends and Technologies. - Gartner Research. 4. Hazen, B. T.; Boone, C. A.; Ezell, J. D.; Jones-Farmer, L. A. (2014) Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. - International Journal of Production Economics 154 (2014) 72-80. 5. Taylor, J.W. (2020) Evaluating Quantile-bounded and Expectile-bounded Interval Forecasts. - International Journal of Forecasting 37(2020) - https://doi.org/10.1016/j.ijforecast.2020.09.007 6. Mitchell, T. M. (1997) Machine Learning. - McGraw-Hill Education. 7. Shmueli, G.; Bruce, P. C.; Yahav, I.; Patel, N. R.; Lichtendahl Jr, K. C. (2016) Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. - John Wiley & Sons. 8. Aggarwal, C. C.; Zhai, C. (2012) Mining Text Data. - Springer Science & Business Media. 9. Goodfellow, I.; Bengio, Y.; Courville, A. (2016) Deep Learning. - MIT Press. 10. Atzori, L.; Iera, A.; Morabito, G. (2010) The Internet of Things: A survey. - Computer Networks 54(15) (2010) 2787-2805. 11. Tapscott, D.; Tapscott, A. (2016) Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and theWorld. - Penguin. 12. Provost, F.; Fawcett, T. (2013) Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. - O'Reilly Media. 13. James, G.; Witten, D.; Hastie, T.; Tibshirani, R. (2013) An Introduction to Statistical Learning: With Applications in R. - Springer. 14. Shmueli, G.; Koppius, O. R. (2011) Predictive Analytics in Information Systems Research- MIS Quarterly 35(3) (2011) 553-572. 15. Hastie, T.; Tibshirani, R.; Friedman, J. (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.) - Springer. 16. Kuhn, M.; Johnson, K. (2013) Applied Predictive Modeling. - Springer. 17. Fildes, R.; Goodwin, P. (2007) Good and Bad Judgment in Forecasting: Lessons from Four Companies. - Foresight: The International Journal of Applied Forecasting (8) (2007) 5-10. 18. Rust, R. T.; Huang, M. H. (2012) Optimizing Service Productivity - Journal of Marketing 76(2) (2012) 47-66. - https://doi.org/10.1509/jm.10.0441 19. Chen, L.; Mislove, A.; Wilson, C. (2016) An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace. - In: Proceedings of the 25th International Conference on World Wide Web - (2016) 1339-1349 - International World Wide Web Conferences Steering Committee. 20. Ngai, E.W. T.; Hu, Y.;Wong, Y. H.; Chen, Y.; Sun, X. (2011) The Application of Data Mining Techniques in Financial Fraud Detection: A Classification Framework and an Academic Review of Literature. - Decision Support Systems 50(3) (2011) 559-569. - https://doi.org/10.1016/j.dss.2010.08.006 21. Chen, H.; Chiang, R. H.; Storey, V. C. (2012) Business Intelligence and Analytics: From Big Data to Big Impact. - MIS Quarterly 36(4) (2012) 1165-1188. - https://doi.org/10.2307/41703503 |