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Introduction to Attribution and Mix Modeling

Introduction to Attribution and Mix Modeling

1h 41mIntermediate2017-03-03

Authors

Corey Koberg

Corey Koberg

Certified AdWords Trainer, Cofounder of Cardinal Path

Course details

When all your marketing culminates in a conversion, who gets the credit? Take your analytics to the next level by applying attribution models. An attribution model is the set of rules that determines how credit for sales and conversions is assigned to touchpoints in conversion paths.

In this course, marketing expert Corey Koberg dives into the basics of attribution and mix modeling. Corey explains what the models are and goes over a few of the most common ones. He shows how to approach offline data, goes into how attribution modeling and marketing mix modeling (MMM) work together, and shares best practices for using different attribution models. Plus, he walks through the challenges you may face when you start analyzing marketing data with models, and goes over an action plan that can help to ensure that your deployment goes as smoothly as possible.

Learning objectives
Determining why attribution is important
Reviewing last click, first click, linear, rule-based, custom, and data-driven models
Deciding when to use MMM or multi-touch attribution
Considering challenges
Evaluating and deploying

Skills covered

Google AnalyticsMarketing StrategyGoogleMarketing

Concepts

0. Introduction

  • 01 - Welcome

1. Get Started

  • 02 - What is multi-touch attribution (MTA)
  • 03 - Why is attribution important

2. Attribution Models

  • 04 - Last click - The one everyone knows
  • 05 - First click and linear models
  • 06 - Rule-based models
  • 07 - Custom models
  • 08 - Analysis - Upper funnel and pathing
  • 09 - Data-driven models

3. Marketing Mix Modeling

  • 10 - Overview of marketing mix modeling (MMM)
  • 11 - When to use MMM or MTA
  • 12 - Holistic view - Unified attribution and MMM
  • 13 - TV and search, frequency capping, and budget optimization

4. Evaluate and Deploy

  • 14 - Challenges and considerations
  • 15 - Software and skills
  • 16 - Data quality basics - Campaign tagging and data prep
  • 17 - Deploy and operationalize

Conclusion

  • 18 - Next steps

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