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Marketing Attribution and Mix Modeling

Marketing Attribution and Mix Modeling

1h 39mIntermediate2021-10-11

Authors

Michael Taylor

Michael Taylor

Cofounder and COO of Ladder.io

Course details

Marketing attribution is a critical but complex topic, and it’s only getting harder to decode what’s driving your marketing performance and decide where you invest your limited marketing budget. Getting it right can give you a significant competitive advantage, but if you make the wrong decisions, you can’t buy your way to success. In this course, Michael Taylor shares a practical approach to the complex analysis technique of marketing mix modeling to help marketers accurately measure the impact of their marketing and advertising efforts. Michael covers key aspects like multi-touch attribution models, marketing mix modeling, and incremental and A/B testing. To demonstrate the intricacies of mix modeling, he shares a practical exercise file so that you'll be equipped to make the right marketing investments that drive sales and profit.

Skills covered

Web Marketing AnalyticsMarketing StrategyMarketingData ScienceDeep Dive (X:Y)

Concepts

0. Introduction

  • 01 - Measuring marketing performance

1. Multi-touch Attribution Models

  • 02 - Last-click attribution - The default model
  • 03 - Time decay and conversion lags
  • 04 - Linear attribution - Treating all touches equally
  • 05 - First-click models - From awareness to acquisition
  • 06 - Position-based models and assigning credit
  • 07 - Data-driven attribution and machine learning
  • 08 - Click windows and view-through conversions

2. Marketing Mix Modeling

  • 09 - Before and after an event - Trend analysis
  • 10 - Linear regression with a single variable
  • 11 - Variables with positive and negative correlations
  • 12 - Multivariable regression - Building your marketing mix model
  • 13 - Feature transformation with diminishing returns and adstocks
  • 14 - Statistical tests to validate your model's accuracy
  • 15 - Forecasting future scenarios for planning

3. Incrementality and A B Testing

  • 16 - A B testing for statistical significance
  • 17 - Bandit testing - Optimizing for results over accuracy
  • 18 - Geo and lift testing to prove incrementality
  • 19 - How did you hear about us - Surveys and panel studies
  • 20 - Working with multiple attribution methods

Conclusion

  • 21 - Continuing to improve your model accuracy

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