This project aims to verify whether or not geolocation interfers with conversion rates (the sale of the product) though statistical test.
This analysis is applicable, for example when a company carries out a Digital Marketing campaign in different geographic areas.
During the project, it was verified which statistical test (parametric or non-parametric) is the most appropriate for the distribution of the data.
The dataset used has random and fictitious data, which consists of 5 columns.
- user_id = user identification
- group = user were diveded between two geographic groups to allow A/B test
- latitude and longitude = user's geographic location
- conversion = conversion rate
Python libraries used for this project are:
- Pandas
- Numpy
- Matplotib
- Seaborn
- Scipy
Based on the analysis of the data, we have no statistical evidence to state that geographic region has an influence on the average conversion rate in the data analyzed.
