Background of the Study
Artificial intelligence (AI) has the potential to revolutionize sales forecasting by analyzing large datasets, identifying patterns, and predicting future trends with greater accuracy (Smith & Rogers, 2024). Retail businesses in Plateau State, which often deal with fluctuating customer demands, can significantly benefit from AI applications in sales forecasting to optimize inventory, improve marketing strategies, and maximize revenue. This study will evaluate how AI is being implemented in sales forecasting by retail businesses in Plateau State.
Statement of the Problem
Many retail businesses in Plateau State continue to rely on traditional sales forecasting methods, which can be inaccurate and inefficient. With the growing availability of AI tools, it is crucial to examine how these tools are being integrated into sales forecasting practices and their impact on operational efficiency. This study aims to assess the effectiveness of AI-driven sales forecasting in improving business operations.
Objectives of the Study
Research Questions
Research Hypotheses
Scope and Limitations of the Study
The study will focus on retail businesses in Plateau State that have implemented AI-driven sales forecasting tools. Limitations include the potential lack of transparency in business operations and the complexity of measuring AI’s direct impact on sales outcomes.
Definitions of Terms
Artificial Intelligence in Sales Forecasting: The use of machine learning algorithms and AI technologies to predict future sales trends based on historical data and other variables.
Sales Forecasting: The process of predicting future sales revenue based on historical data, market conditions, and trends.
Retail Businesses: Companies that sell goods or services directly to consumers, often in small to medium-sized operations.
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Chapter One: Introduction
1.1 Background of the Study...
BACKGROUND TO THE STUDY
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Abstract
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