📊AppPoint-Pro-A-Pareto-Based-Complaint-Analysis

🔍 Project Overview

AppPoint, an app store platform, aimed to enhance user satisfaction by analyzing customer complaints received in the months of April, May, and June. The goal was to identify underperforming apps and the most frequent types of complaints using Pareto Analysis. This project enables the company to take focused actions on improving app quality and customer experience.

🗂️ Dataset Information

• Source: Internal dataset from AppPoint (April–June)
• Fields include:
  • Complaint ID
  • App Name (Photo Editor Apps)
  • Complaint Type
  • Date of Complaint

All apps had an equal number of downloads to ensure fairness in the analysis.

❓ Questions to Answer

1. Which apps account for the majority of customer complaints?
2. What are the most frequent types of complaints for these underperforming apps?
3. What percentage of complaints are caused by the top few apps and complaint types?
4. How can this data be visualized to support better business decisions?

📊 EDA & Visualization

The project includes the following analyses and dashboards:

✅ Task 1: Descriptive Analysis

• Summary statistics about complaints and app types.

✅ Task 2: App-wise Pareto Analysis

• Pivot table showing total and cumulative complaints per app.
• Apps contributing to 69.96% of total complaints are:
  • FilterFlex
  • LightArt
  • PixelPerfect

✅ Task 3: Complaint Type Pareto Analysis

• Pivot table filtered for underperforming apps.
• Major issues (covering 70.25% of total complaints):
  • Too Many Ads
  • Frequent Pop-Ups for Upgrades
  • Limited Free Features

✅ Task 4: Overall Dashboard

• A visual dashboard combining app-wise and complaint-wise insights.
• Slicers and charts enhance interactivity for deeper analysis.

✅ Task 5: Insights Report

• Detailed insights and recommendations for management.
• Summary of key complaint trends and their impact on user satisfaction.

✨ Key Insights

• FilterFlex, LightArt, and PixelPerfect collectively contribute 69.96% of total complaints.
• The top 3 complaint types—Too Many Ads, Frequent Pop-Ups, and Limited Features—are responsible for 70.25% of these complaints.
• Focused improvements in these areas can significantly reduce user dissatisfaction.

🛠 Tools Used

• Google Sheets – Data analysis, pivot tables, charts, dashboard
• Pareto Principle – To identify key contributors
• Slicers – For interactivity and focused filtering
• Charts – For intuitive data storytelling

---

📌 Conclusion

This project empowers AppPoint to take a data-driven approach to app improvement and user satisfaction by prioritizing the key complaints and underperforming apps. Focusing resources on the top issues can bring substantial performance gains across the platform.