1. Data Collection: Gather relevant data from various sources such as sales records, market research, and financial statements. 2. Data Cleaning: Ensure the data is accurate and consistent by removing any errors or inconsistencies. 3. Model Selection: Choose the appropriate quantitative model based on the business problem at hand. 4. Model Building: Develop the model using statistical software or programming languages like R or Python. 5. Validation: Test the model's accuracy by comparing its predictions with actual outcomes. 6. Implementation: Use the model's insights to inform decision-making and strategy formulation.