iOS 14.5 Killed Your Attribution. Here's What to Do About It.
iOS 14.5 didn't just tweak your attribution model; it detonated it. The old methods of tracking and 'attributing' value are now about as reliable as a psychic reading.
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By Joris van Huët, Founder & CEOUpdated 3 min read
iOS 14.5 didn't just tweak your attribution model; it detonated it. The old methods of tracking and 'attributing' value are now about as reliable as a psychic reading. But don't panic.
The iOS 14.5 Apocalypse: What Actually Happened?
Apple's App Tracking Transparency (ATT) framework, launched with iOS 14.5, gave users the power to say "no" to being tracked across apps and websites. And guess what? Without it, traditional attribution crumbled. You're left with fragmented data, biased insights, and a whole lot of guesswork. Kiss your accurate reports goodbye.
What's Wrong with Traditional Attribution?
Traditional attribution models - first-touch, last-touch, linear, you name it - were always flawed. They assume correlation equals causation, a rookie mistake that any self-respecting data scientist would laugh at. These models assign credit based on arbitrary rules, ignoring the complex web of factors that influence customer behavior. Now, with iOS 14.5 crippling even their limited tracking capabilities, these models are not just flawed; they're actively misleading. A very expensive black hole.
Is Cookieless Tracking Even Possible?
Yes, absolutely. The problem isn't the lack of cookies or IDFAs; it's the reliance on outdated methodology. You don't need to track individual users to understand the impact of your marketing efforts. You need to understand the causal relationships between your actions and customer behavior. That's where causal inference comes in.
Causal Inference: The Antidote to Broken Attribution
Causal inference is a statistical method that identifies true cause-and-effect relationships. It goes beyond correlation to determine whether a specific action (like running an ad campaign) actually caused a specific outcome (like an increase in sales). Forget about relying on flimsy tracking and biased models.
Causal inference identifies true cause-and-effect relationships, going beyond correlation to determine if an action actually caused a specific outcome.
- By comparing the outcomes of your business with the synthetic control, we can isolate the incremental impact of your actions.
What Are the Benefits of Switching to Causal Inference?
- No black boxes here.
- Increase your ROAS and sharpen your campaigns with confidence.
Stop Guessing, Start Knowing
iOS 14.5 was a wake-up call. It exposed the fundamental flaws in traditional attribution and forced marketers to confront the reality of a cookieless world. But this isn't the end of marketing; it's an opportunity to evolve.
FAQ
Yes.
Ready to ditch the broken attribution model and embrace the power of causal inference?
Sources and Further Reading
- Harvard Business Review on Marketing Attribution
- McKinsey on Marketing ROI
- Causality Engine Resources
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Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Terms in this article
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- Attribution ModelAn Attribution Model defines how credit for conversions is assigned to marketing touchpoints. It dictates how marketing channels receive credit for sales.
- Causal InferenceCausal inference is the set of methods for estimating how much an outcome changes because of a cause, rather than alongside it. It works by comparing what happened with an estimate of what would have happened without the cause.
- CausalityCausality is the relationship where one event directly causes another, essential for identifying specific actions that drive desired outcomes in marketing.
- CausationCausation is the relationship where a change in one variable directly causes a change in another.
- CorrelationCorrelation is a statistical measure showing a relationship between variables; it does not imply causation.
- Marketing AttributionMarketing attribution assigns credit to marketing touchpoints that contribute to a conversion or sale. Causal inference enhances attribution models by identifying true cause-effect relationships.
- Marketing ROIMarketing ROI (Return on Investment) measures the return from marketing spend. It evaluates the effectiveness of marketing campaigns.