Data Science Hacks – Google Causal Impact
Learn how to use Google’s Causal Impact package in Python to analyze the impact of events on variables like sales and website visits. Calculate the ROI of marketing campaigns and promotions. Perfect for data analysts and anyone interested in causal inference techniques and data analysis with Python.
What you’ll learn
- Inferring the Causal Impact of a event (Promotion, Marketing Campaign, etc) over sales, website visits, download apps or any other variable you want to analyse
- How to use the pythons implementation of Google Causal Impact package
- How to calculate the ROI of a marketing campaign or a sales promotion
Welcome to our Google Causal Impact Course.
This course I’ll teach you how to use the google’s package Causal Impact in your on job or personal projects.
The Causal Impact model developed by Google works by fitting a bayesian structural time series model to observed data which is later used for predicting what the results would be had no intervention happened in a given time period. The idea is to used the predictions of the fitted model (depicted in blue) as a reference to what probably would had been observed with no intervention taking place.
After this course you will have a powerful tool, to measure (with statistical significance):
* The extra number of sales / app downloads / clicks / web site visits caused by a marketing campaign
* The ROI of a Marketing Campaign
* The effect of a promotion over demand
* Any change of behavior in a series, caused by a known event
Who this course is for:
- Data Analysts and Data Science Students
- Anyone interested in causal inference techniques and data analysis with python
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