Hey everyone, new-ish here but been living in our product analytics dashboards for the past few months. I’m trying to implement a time decay attribution model for a new signup flow, and I’ve hit a weird snag that’s making me question my setup.
I’m using a pretty standard 7-day half-life. The idea is to see the influence of our blog content and social ads over the week before conversion, not just the last click. But when I run the report, it’s like the model isn’t “decaying” at all—over 95% of the credit is still getting shoved into the final direct visit and the paid search click from the day before. Touches from 5 or 6 days out are getting virtually zero, which defeats the whole purpose.
I’ve double-checked the timestamp logic on our touchpoints, and the sequence looks correct in the raw data. The model configuration *seems* right, but the output is basically a last-touch model with extra steps. 😕
Has anyone else run into this? I’m wondering:
* Could this be a data granularity issue? Our sessions are tracked at full-day resolution, not down to the second. Would that break the decay math?
* Or is there a common pitfall in how the “half-life” parameter interacts with the defined lookback window? My window is 30 days, which should be plenty for a 7-day half-life to show a curve.
Any insight on what to audit next would be super helpful. I can share more specifics about the platform config if needed.
Yeah, the day-level resolution could definitely be flattening your curve. If all touches within a day share the same timestamp, the model can't properly space them out for the decay calculation. That might be lumping days 6 and 5 together, then the weight plummets because the math sees them as equally "old."
A common gotcha I've seen is mixing up the *lookback window* with the *half-life* itself. If your model is set to only consider the last 7 days total, but you're using a 7-day half-life, the decay is too aggressive for that window. Touches from 5 days out are already past the halfway point of your entire eligible period, so they get crushed. Try extending the lookback window to 14 or 30 days and see if the distribution starts to make more sense.
That's a sharp observation about the lookback window. It's often overlooked because teams get fixated on the half-life parameter alone. There's a practical implication here, though: extending the lookback to 30 days might introduce noise from old, irrelevant sessions, especially for a considered action like a signup. You'd need to validate that your touchpoint data that far back is even reliable and representative of the current user journey.
Let's keep it constructive