https://github.com/yeshunit/meesho-userpulse
The Personalized Discovery and Engagement Enhancement System for Meesho leverages user interactions, search behaviors, and product analytics to deliver customized shopping experiences. By monitoring KPIs like voice search adoption and trending products, the system aims to boost user engagement and retention in the competitive e-commerce market.
https://github.com/yeshunit/meesho-userpulse
Last synced: 3 months ago
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The Personalized Discovery and Engagement Enhancement System for Meesho leverages user interactions, search behaviors, and product analytics to deliver customized shopping experiences. By monitoring KPIs like voice search adoption and trending products, the system aims to boost user engagement and retention in the competitive e-commerce market.
- Host: GitHub
- URL: https://github.com/yeshunit/meesho-userpulse
- Owner: Yeshunit
- Created: 2024-10-30T13:46:24.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-30T14:07:56.000Z (over 1 year ago)
- Last Synced: 2025-03-05T01:41:57.755Z (over 1 year ago)
- Size: 11.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Personalized Discovery and Engagement Enhancement System
## Project Overview
The **Personalized Discovery and Engagement Enhancement System** is designed for Meesho to improve user engagement and product discovery through personalized recommendations and insightful analytics. This project leverages user interaction data, search behavior, and notification efficacy to create a more tailored shopping experience.
## Objectives
1. **Enhance User Engagement**: By analyzing user interactions, we aim to provide personalized content that resonates with user preferences.
2. **Optimize Product Discovery**: Implementing trending product features and recommendations based on user behavior to facilitate easier product discovery.
3. **Measure User Interaction**: Evaluate user engagement through key performance indicators (KPIs) to inform future strategies.
4. **Assess Search Behavior**: Analyze search methods to identify trends in voice vs. text searches, aiming to improve search functionality.
## Key Performance Indicators (KPIs)
1. **Voice Search Adoption Rate**: This KPI measures the percentage of searches performed using voice recognition compared to total searches over the past 24 hours. It helps identify user preferences and potential enhancements in the search functionality.
2. **Trending Products**: By tracking view counts for products, we can identify which products are currently popular among users. This insight assists in product placement and marketing strategies.
3. **User Engagement Metrics**: Measuring the total interactions per user over a specified period allows us to identify the most engaged users, helping to focus marketing efforts and improve user retention.
4. **Search Trends**: Comparing voice and text search interactions over time provides insights into user preferences and helps inform future search feature developments.
## Main Files
- [key_performance_indicators.sql]([queries/key_performance_indicators.sql](https://github.com/Yeshunit/Meesho-UserPulse/blob/main/key_performance_indicators.sql)): Contains SQL queries to calculate key performance indicators, including voice search adoption rate and trending products.
- [users_data.sql](data/users_data.sql): SQL file to insert sample user data into the Users table, which includes user details and segments.
- [products_data.sql](data/products_data.sql): SQL file to insert sample product data into the Products table, detailing product names, categories, prices, and stock levels.
- [KPIS.md](documentation/KPIS.md): A dedicated markdown file describing the KPIs used in this project, including methodologies and their significance in measuring success.
## Conclusion
The **Personalized Discovery and Engagement Enhancement System** leverages data analytics to create a user-centric shopping experience for Meesho. By focusing on KPIs such as voice search adoption and trending products, the project aims to enhance user engagement and optimize product discovery. This initiative not only enriches the user experience but also provides valuable insights that can guide future product strategies.
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For detailed insights into queries and data structures, please refer to the respective folders.