Attribution
Also called: marketing attribution, attribution model, multi-touch attribution, last-click attribution
Definition
Attribution is the method of assigning credit for a conversion, such as a signup or sale, to the marketing touchpoints that led to it, so you can tell which channels and campaigns actually drive results.
Attribution answers "what made this customer sign up?" In practice the answer is usually several things: a Reddit thread, a Google search a week later, a retargeting ad, then a direct visit to pricing. Attribution decides how to split the credit.
How it works
An attribution model is a rule, set of rules or algorithm that assigns credit to touchpoints along the path to a conversion. Google Analytics currently offers three in its attribution reports:
- Data-driven attribution: credit is assigned based on how each touchpoint changes the probability of converting, using your account's data.
- Paid and organic last click: all credit goes to the last channel clicked before converting.
- Google paid channels last click: all credit goes to the last Google Ads click.
Google also notes that its models exclude direct visits from receiving credit unless the entire path consists of direct visits.
Other tools add models like first touch, linear or position-based. No model is "true". Each is a lens.
Why it matters for marketers
Attribution drives budget. If a channel gets no credit, it eventually gets no money. The risk is that the channels easiest to measure (paid search, retargeting) collect credit for demand created elsewhere.
That was the point of dark social when The Atlantic's Alexis Madrigal described it in 2012: much sharing happens through channels that leave no referrer, so it shows up as direct traffic and gets undercounted.
How it plays out on Reddit
Reddit is one of the hardest channels to attribute and one of the easiest to undervalue:
- People read a thread on mobile, then sign up on desktop days later.
- They search your brand name after seeing it mentioned, so Google gets the last click.
- Most mentions are unlinked, so there is no click at all.
- A thread that ranks on Google keeps sending people for months after the reply.
What works in practice:
- Tag what you can. Use UTM parameters on links you post where the subreddit allows links.
- Ask. A free-text "How did you hear about us?" field at signup. It catches what referrer data misses.
- Watch leading signals. Branded search volume and direct signups after a well-received thread.
- Compare windows. Look at results over months, not just the week a reply went up.
Self-reported data is imperfect too, but combined with tagged links it gives a far more honest picture of community channels than last click alone.
Common mistakes
- Treating last click as the whole story. It rewards the final step and ignores what created the interest.
- Expecting precision. Attribution is an estimate. Use it to compare channels, not to justify exact dollar figures.
- Changing models mid-analysis. Comparing months under different models produces fake trends.
Frequently asked questions
What are the main attribution models?
Common ones are first touch, last click, linear, position-based and data-driven. Google Analytics currently offers data-driven, paid and organic last click, and Google paid channels last click.
Why is attribution so hard?
People switch devices, share links privately and come back days later through search or direct. Many touchpoints leave no data.
How do I attribute Reddit traffic?
Combine UTM-tagged links where allowed, a self-reported "how did you hear about us" question and trends in branded search and direct signups.