Black Friday/Cyber Monday - Forecasting Sales & Causal Impact | Incrementality
Автор: Marketing Analytics With Kisholoy
Загружено: 2025-10-13
Просмотров: 73
This video will present a case study on how to forecast sales during BFCM, even if you are months away from the event. I show how using extended regression (mini MMM with important ingredients like adstock, controls like day of the week seasonality and saturation) how this can be done with a reasonable amount of accuracy (less than 10% MAPE).
Additionally, I show how you can carry out an incrementality test to check if the marketing initiatives that led to greater ad spend during BFCM were actually incremental or not. I use Google's CausalImpact package in Python, one that uses Bayesian Structural Time Series approach (BSTS).
From the preparation of the data, to the actual analysis itself to potential pitfalls, this is an end-to-end case study. An absolute must-watch!
00:00 - Forecasting and data preparation
18:57 - Causal impact analysis
I have previously uploaded other videos related to BFCM, check out this playlist:
• BFCM
Search my YouTube Channel for more videos on Incrementality Testing and Causal impact analysis if that's what interests you!
#marketinganalytics #marketingmeasurement #causalinference #causality #blackfriday
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