Growth
Attribution models get debated endlessly. The bigger issue is usually that the underlying tracking isn't trustworthy in the first place.
Ask five marketers which attribution model is "correct" and you'll get five different answers — first-touch, last-touch, linear, data-driven, or some in-house blend. That debate matters less than most teams think, for one simple reason: none of it matters if the underlying tracking is broken.
Before debating attribution models, confirm the basics: are conversions being tracked once, not duplicated across platforms? Are UTM parameters applied consistently across every campaign? Is cross-device behavior being reasonably accounted for? Most "attribution problems" we're brought in on turn out to be data quality problems wearing an attribution costume.
Last-touch attribution is popular because it's simple, but it systematically overvalues bottom-of-funnel channels (like branded search) and undervalues awareness channels that started the journey. First-touch has the opposite bias. Neither reflects how most considered purchases actually happen.
Linear, time-decay and position-based models spread credit more realistically, but they still rely on accurate cross-channel tracking to mean anything. A sophisticated model built on incomplete data produces confidently wrong answers — arguably worse than a simple model, because it looks more credible.
For most mid-sized businesses, a pragmatic setup beats a theoretically perfect one: consistent UTM tagging, deduplicated conversion tracking, a CRM that records lead source at the point of conversion, and a multi-touch model reviewed periodically rather than treated as gospel.
The point of attribution isn't to assign perfect credit — it's to make budget decisions better than guessing would. If your current setup can't confidently tell you whether to shift budget from one channel to another, the model isn't the problem yet. The data underneath it is.