If the only source of truth you have for which channel “works” is your analytics platform’s last-click report, you’re probably making budget decisions based on an incomplete picture.
What last-click gets wrong
The last-click model gives 100% of the credit for a conversion to the last touchpoint before purchase. This systematically favors channels that capture already-existing demand (like brand Search) and penalizes the channels that generated that demand in the first place (like Social or Display), even when they were the real reason the user ended up buying.
The typical symptom
If you’ve ever seen a report where “Brand Search” looks like your star channel and decided to cut budget from discovery channels to reinvest there, that’s a classic symptom of looking at the problem through last-click: you’re rewarding the channel that closes the sale and penalizing the one that generated it.
More reasonable alternatives
You don’t need a perfect attribution model (there isn’t one). Anything better than last-click helps:
- Linear attribution: splits credit equally across every touchpoint.
- Position-based attribution: gives more weight to the first and last touch, splitting the rest among the middle ones.
- Data-driven models: use machine learning on your own conversion data to assign credit based on each channel’s actually observed impact.
What matters isn’t picking the “correct” model
No attribution model is 100% accurate, and obsessing over finding the perfect one is time poorly spent. What actually matters is no longer making budget decisions based exclusively on last-click, and complementing it with at least one model that recognizes the contribution of upper-funnel channels.
If your attribution today is last-click only and you need a fuller picture, message me on WhatsApp and we’ll figure out which model makes sense for your business.