N1(2024)

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homepage: http://www.applied-business-solutions.eu
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
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