DATA-DRIVEN BUSINESS ANALYSIS: A COMPREHENSIVE ANALYSIS OF PREDICTIVE ANALYTICS IN PRICING STRATEGIES, MARKETING DECISIONS AND OPERATIONAL EFFICIENCY

Abstract

In today’s dynamic and competitive business environment, organizations face increasing pressure to make marketing decisions that are both timely and effective. Traditional intuition-based approaches are no longer sufficient to understand complex consumer behavior and market trends. This research paper explores how business analytics, through predictive and descriptive statistical models, can optimize marketing decisions. Using a real-world-inspired case study of 1,000 taco delivery orders across multiple U.S. cities, the study applies descriptive statistics, correlation analysis, and regression modeling to uncover insights into customer behavior, pricing strategies, and operational efficiency. Findings reveal that toppings and taco size significantly influence pricing, while weekend orders lead to higher customer tips. Delivery inefficiencies were also identified as critical areas for optimization. Beyond the food delivery industry, the implications of this study extend to marketing professionals and managers across sectors such as retail, corporate services, healthcare, energy, and finance. For example, retailers can leverage analytics to refine product assortments and promotional campaigns, healthcare organizations can optimize patient engagement strategies, energy firms can forecast demand more accurately, and financial institutions can enhance customer segmentation and risk assessment. Business analytics tools such as Tableau, Power BI, R, Python, SAS, and advanced Excel modeling can further support these applications, enabling professionals to translate raw data into actionable insights. Future research could expand the scope of this study by incorporating larger and more diverse datasets, integrating real-time data streams, or applying advanced techniques such as machine learning, sentiment analysis, or predictive demand modeling. This would provide deeper insights into consumer behavior and allow for more adaptive decision-making. Ultimately, the findings highlight that adopting analytics can significantly enhance decision quality by improving forecasting accuracy, optimizing resource allocation, and reducing inefficiencies. In marketing contexts, efficiency improvements may translate into shorter campaign cycles, increased ROI, and better alignment of products or services with customer preferences.