📐 More than just conversion values: how to make the most of your SKAdNetwork data - with Paul Bowen, GM at AlgoLift by Vungle 🗄️

Paul Bowen, the GM at AlgoLift, has 20 years of experience in digital advertising and among the folks we look to for his expertise on SKAdNetwork.

In our conversation today, Paul breaks down the various ways in which developers need to think about measurement for SKAdNetwork. He touches upon the complexities and limitations of the conversion value framework - and how it might be used along with IDFV based data to infer probabilistically the value or LTVs of users or campaigns.

This is an episode with quite a few technical details and nuances, and we strongly recommend listening to it carefully to absorb all the wisdom Paul has shared. Enjoy!

KEY HIGHLIGHTS

🌱 How SKAdNetwork came about
🔄 Everything revolves around the conversion value
6️⃣ 64 combinations of 6-bit conversion values
🔮 How to align conversion values to predict LTV for different apps
🌅 Early purchase behavior is a good LTV indicator for games
💰 Monetization in the first week helps to determine the appropriate conversion value
💸 It can be more challenging to understand the LTV for an app with no day 0 monetization
🤩 The best indicator of LTV is past spending behavior
🔗 Developers should crack the connection between in-app engagement events and LTV
📶 The 3 data signals needed to define conversion value
🤖 The challenge of programming an LTV signal to trigger a conversion value
🎯 The trade off between accuracy and campaign optimization
⌛ How long is too long to wait for accurate attribution?
⏰ The debate around Facebook’s 24-hour attribution window
🌐 The impact of 24-hour attribution window on other ad networks
📋 Day 1 data is not enough for campaign attribution
🥠 Day 1 behavior is definitely not enough to predict LTV
🧩 Who owns the conversion value pieces of the ecosystem
🙃 Why some developers want to bypass the MMPs for postbacks
✈️ How postbacks travel from ad networks to developers
💔 This is not the time to break away from MMPs
📊 Breaking down probabilistic campaign attribution
💾 The 2 data sets needed to define a probabilistic attribution model
🍰 The 0-100% structure of a probabilistic model
🏗️ The skill sets needed to build these models
📈 Building the model isn’t challenging; extrapolating it is


Check out the show notes here: https://mobileuseracquisitionshow.com/episode/more-than-just-conversion-values-how-to-make-the-most-of-your-skadnetwork-data-with-paul-bowen-gm-at-algolift-by-vungle/


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