Been digging into our attribution data lately. Something feels off.
The models we’re using seem to be masking some weird traffic patterns. Not sure if it’s a setup issue or if we’re missing something important.
Anyone else run into this? What did you find?
Forget fancy models. Just look at what users do. Track their actual steps before buying. Simple works.
Yeah, I’ve seen this a few times. Last year, we noticed our paid search was getting way more credit than it deserved.
Turns out, a bunch of users were hitting our ads after already finding us organically. The model was giving all the credit to that last click.
We fixed it by:
It took some work, but we got a much clearer picture of what was actually driving signups.
Don’t trust the default models blindly. There’s usually more going on under the surface.
I’ve seen this issue a lot. Default models often miss crucial touchpoints.
Here’s what works:
These catch hidden interactions that standard models miss.
Also, compare your model against raw session data. Look for discrepancies in high-value channels.
Don’t overcomplicate it though. Focus on actionable insights that actually move the needle on your KPIs.
Raw data’s your friend here. Look at actual user paths before conversions. Maybe try different attribution windows too. Could show some hidden stuff.
Attribution models can sometimes obscure key details. I inspect raw data to get a clearer user path.
Try adjusting the tracking window to reveal hidden trends.