Test if the ads that the advertising company runs resulted in a significant lift in brand awareness. Our task here, is to design a reliable hypothesis testing algorithm for the Brand Impact Optimiser (BIO), which is a lightweight questionnaire served with every campaign.
The BIO data for this project is a “Yes” and “No” response of online users to the following question
Q: Do you know the brand SmartAd? O Yes O No The users that were presented with the questionnaire above were chosen according to the following rule:
- Control: users who have been shown a dummy ad
- Exposed: users who have been shown a creative, an online interactive ad, with the SmartAd brand. The data is collected from 3-10 jul 2020 from SmartAd advertising agency.
- Exploratory data analysis
- Classical Type of A/B testing
- Sequential A/B testing
- Obtaining statistically valid insights in respect to the business goal
The notebooks in this repository contains data exploration and implementation of classical p-value based algorithm and the sequential A/B testing algorithm in Python.