Upliance_Sales_Analysis

• Tools- Microsoft Excel 2019, MS Word.
  

Business Objective-
Analyze the datatsets related to orders, users and cooking sessions to uncover key insights to make business recommendations for an AI cooking assistant system. The analysis aims at exploring factors influencing user’s preferences, popular dishes and reveal trends based on cooking patterns. The findings will inform strategies to improve decision-making through data-driven visualizations and actionable recommendations.

Summary-

• Data Cleaning and merging-
  

Key Insights-

• ‘Oatmeal’ and ‘Pancakes’ are the dishes were least rated which could explain why they were also least ordered and contributed the least to Sales.
  
• the oldest age group and the youngest age group in the dataset request the least number of orders. This might be because the oldest age group usually find it difficult to use technology and the youngest group might prefer to cook the least or rather cook more simpler dishes.
  
• Even though, sales trend for order ratings and total sales remained steady but did not improve. Also, most users rely on the AI assistant for food at night. This might be because of the time constraints during the day or more complex meals at night.
  

Business Recommedations made-

• Promote low-performing dishes like Oatmeal and Pancakes-
• Revamp the recipes and making them more appealing by adding toppings or subcategories.
• Highlight health benefits and marketing them as healthy and quick breakfasts.
• Promote them at breakfast hours when people usually like quick meals and add attractive visuals for such dishes.
• Leverage high performing dishes like Grilled Chicken and Spaghetti-
• Recommending them at dinner hours.
• Use appealing prompts such as ‘most Loved Dishes’.
• Personalize Order recommendations based on the age group such as-
• For oldest, suggest health-oriented dishes.
• For youngest, more trendy dishes that are budget-friendly as well.
• Simplify the interface for oldest age group by adding large texts and step-by-step guidance.
• Collect more comprehensive feedbacks from users such as ease of use, order satisfaction etc.
• Leverage ‘Night-time’ sales by adding recommendations such as “Late-night specials”.
• Introduce more quick-cooking options and recommend them during the day.
• Introduce a mobile application that connects to AI assistant to increase user engagement. It would enable to send push notifications as well.
• Conduct surveys to gather more information about Day-time barriers.