Cafe-Chain-Sales-Optimization

Propose strategies to optimize Sales and Customer Satisfaction

Overview

This data analytics project aimed to uncover sales trends, optimize product offerings, and enhance customer satisfaction for a cafe chain by analyzing historical sales data. The goal was to develop actionable strategies to increase sales and boost customer satisfaction metrics. Using Machine Learning models and interactive dashboards, the project focused on data-driven decision-making, discount optimization, and forecasting sales for upcoming periods.

Steps Involved

1) Data Analysis and Model Building

• Assessing Data Sets and Identifying Anomalies
• Correlating External Factors with Sales Performance
• Analyzing Sales Performance Across Categories
• Forecasting Sales for next quarter
• Analyzing the Effects of Discounts on Sale Amount Volume
• Different Analysis on Dataset(View Tableau Dashboard here: https://public.tableau.com/views/CafeSalesanalysisDashboard_17290924435120/CafeSaleAnalysisDashboard?:language=en-US&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link)
• Forecasting Sales for the Next 6 Months

2) Strategic Recommendations and Documentation

• Formulate Strategic Recommendations
• Discussing Areas for Further Investigation
• Ensuring Comprehensive Documentation

Recommendations

• Engage with stakeholders to understand the cause of negative Sale Amount and Quantity Sold values.
• Prioritize and promote coffee, as it is the top-selling category.
• Apply discounts within the optimal range of 10% to 20% to maximize sales.
• Investigate the limited impact of events on sales and consider increasing promotional activities during events to boost sales.
• Focus marketing efforts on product quality and customer experience instead of weather conditions. Weather is not a factor in Sales growth.
• Explore the potential for seasonal factors in future sales by generating more data and to make more accurate forecasts.

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