Data Science: Measuring No-ID Campaigns with Causal Impact (Social Point)

Alicia Horsch is a Data Scientist in the Marketing and Analytics teams at Social Point, a mobile game developer based in Barcelona. She is also an ambassador for Women in Data, a nonprofit organization that focuses on increasing diversity in data careers. 

Questions Alicia Answered in this Episode:

• What is Causal Impact and why do you want to use it?
• What’s so special about using offline campaigns as far as Causal Impact?
• How does Causal Impact work?
• How do you split traffic into the treatment group and the control group?
• Would you like to elaborate on the math behind it and how the model is built?
• How do you know if the predictions are good?
• What are the shortcomings of the Causal Impact package?
• What are the most important things that you look for in a dimension to split the events on?
• What resources can you recommend to our listeners who want to learn more about Causal Impact?

Timestamp:

• 1:25 What is Causal Impact?
• 2:22 Causal Impact for when you can’t track the user
• 3:47 How does Causal Impact work?
• 5:45 Control group and uplift
• 7:05 How does the BSTS model work?
• 10:24 What is a prior in bayesian statistics?
• 11:25 Evaluating prediction accuracy
• 14:28 Shortcomings of Causal Impact
• 21:18 Causal Impact resources and background

Quotes:

(3:49 - 4:04) "Causal impact works by using some information to make a prediction on what would've happened if there wouldn't have been a marketing campaign, which is also often called the counterfactual."

Mentioned in this Episode:

• Alicia Horsch’s LinkedIn
• Social Point
• Social Point’s career website
• Women in Data
• The CausalImpact package