Customer data analysis for educational app
Budget: $25 – $50 USD
I need to do a data analysis project for a customer. Would you be able to help?
The customer has an educational app with more than 1 million users worldwide.
They would like to use machine learning and data science to improve the app.
Identify users who are about to churn (stop using the app)
Identify users who are most likely to spend money on the app
Identify causes of these behaviours and how to influence them
Key questions are:
How can we increase revenue?
What drives users to install?
What drives users to subscribe to the paid service?
What drives users to stop using the app or unsubscribe?
Are there key markets where users are prepared to pay more?
Any geographical patterns?
How the project may look:
1. first conduct an analysis and exploration of the data
2. train a machine learning model or recommendation system to identify key customers, etc.
3. deploy the model to production.
The project could start with just the exploration stage and later move onto model deployment, but I need you to be comfortable with basic tools such as Dash or Fast API as well as just working in Jupyter notebooks. So the budget is flexible depending on the scope, but the first stage will be the data exploration,
The customer has an educational app with more than 1 million users worldwide.
They would like to use machine learning and data science to improve the app.
Identify users who are about to churn (stop using the app)
Identify users who are most likely to spend money on the app
Identify causes of these behaviours and how to influence them
Key questions are:
How can we increase revenue?
What drives users to install?
What drives users to subscribe to the paid service?
What drives users to stop using the app or unsubscribe?
Are there key markets where users are prepared to pay more?
Any geographical patterns?
How the project may look:
1. first conduct an analysis and exploration of the data
2. train a machine learning model or recommendation system to identify key customers, etc.
3. deploy the model to production.
The project could start with just the exploration stage and later move onto model deployment, but I need you to be comfortable with basic tools such as Dash or Fast API as well as just working in Jupyter notebooks. So the budget is flexible depending on the scope, but the first stage will be the data exploration,