amanjot-git / realworlddatachallenges

Solving Real World Data Problems with SQL

RealWorldDataChallenges

Solving Real World Data Problems with SQL

1. Analyzing Registered User Data

It focuses on providing in-depth insights into the number of registered users per customer in anticipation of an upcoming board meeting.

Steps:
- Collecting and ensuring the integrity of relevant datasets,
- Defining key metrics, 
- Considering the timeframe and product subscriptions

The project aims to calculate the all-time average number of registered users per customer.

The results will be presented in a concise and visually appealing manner, empowering the leadership team with valuable information to make informed decisions and enhance the impact of the upcoming board presentation.

2. Analyzing User Engagement in Recent Email Campaign

It focuses on a comprehensive examination of the recent email campaign's performance.

Steps:
- Collecting and ensuring the integrity of relevant datasets,
- Identifying tracking events for link clicks, and
- Analyzing the distribution of user engagement

The project aims to uncover insights into user behavior. Visualizations will be created to effectively communicate these insights, and recommendations will be provided to optimize future email campaigns.

The analysis is documented for transparency and shared through a concise presentation, empowering the marketing team with actionable information for strategic decision-making and campaign enhancement.

3. Payment Funnel Analysis

It delves into understanding the payment process's efficiency and identifying potential bottlenecks in the user journey.

Steps:
- Collecting pertinent data and ensuring its accuracy
- Finding the furthest point user reached in payment process
- If an error occurs, differentiate between user error and vendor error

The project aims to provide insights into user behavior at each stage of the payment funnel.

Utilizing analytics techniques, such as tracking status identification and distribution analysis, the project seeks to uncover patterns and anomalies, facilitating data-driven decision-making. Recommendations based on these findings will aid in optimizing the payment process, ultimately improving user experience and increasing conversion rates.