Can attribution modelling dispel the darkness in a post-IDFA world?

When we had IDFAs and deterministic attribution, this sort of estimation based modeling was not necessary. However, as it is curtains on IDFAs, attribution modelling could be a way to retain marketing metrics like LTV and ROAS.

How is this possible? Will it be accurate?

We find out in our conversation with Chris Farm today, co-founder and CEO of Tenjin, in which we talk about what data we do have at our disposal, and how to make the most of it to build attribution models - from simple spreadsheet ones to more sophisticated ones.

KEY HIGHLIGHTS

🏃‍♀️ How attribution has shifted to Apple
🧐 The gaps in ROI metrics for developers
⚙️ How attribution modelling works
📥 The inputs of an attribution model and the downstream metrics they impact
💭 Let’s not forget the IDFV
💪 The IDFV is more powerful that you would expect
🤼 Signal-based vs deterministic
🌱 How to start with a simple model and then refine with layered variables
🚗 The drivers of model accuracy and inaccuracy
🎯 We need to change how we define accuracy
🙋 Last-click is just one possible model
🔍 It is possible to create models without postbacks
💡 How to work with aggregate data for good decision-making
🏝️ Why attribution modeling is unnecessary when running a single channel
🕹️ Attribution modelling is especially suited to gaming companies
⚖️ The difference between attribution modeling and last-touch
🦄 Why true multi-touch still doesn’t exist
📝 How to create a V1 model in Excel
🦸 Developers will rise to the challenge

Check out the show notes here: https://mobileuseracquisitionshow.com/episode/the-power-of-attribution-modeling-to-calculate-ltvs-in-a-post-idfa-world-with-chris-farm-co-founder-and-ceo-at-tenjin/

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