First touch attribution makes brand look brilliant. Last touch makes paid search look brilliant. Multi touch makes whoever configured the weights look brilliant. Every model is wrong in a direction that flatters somebody, and the somebody is almost always the person who chose the model.
This is not a scandal. It is arithmetic. Attribution assigns credit for an outcome with many causes, and there is no objectively correct way to split a joint cause. The literature has known this for decades. What is strange is that marketing keeps shopping for a model as though one of them is going to turn out true, and then presents the output as measurement rather than as the interpretation it actually is.
The useful question is not which model is true. It is which decision the model is supposed to change.
Start from the decision and the model gets simple fast. If the decision is where to move next quarter's incremental spend, you do not need a model at all. You need a holdout. Turn the channel off in a matched geography, watch what happens to pipeline, and you have a causal answer no weighting scheme can give you. If the decision is which content to make more of, last touch is fine and cheap, because you are ranking within a stage rather than across the funnel. If the decision is whether brand investment is working, accept that you are running a two year experiment and stop asking the dashboard to settle it every month.
Most attribution work fails because it is not attached to a decision at all. It is attached to a narrative obligation. Somebody has to explain the number to the board, and a model produces an explanation on demand, so the model gets built and then defended long past the point where anyone consults it before spending money. You can identify these instantly. Ask a team what they would do differently if the model showed the opposite result. If there is no answer, the model is theater.
I run attribution as a reporting convention, not as truth. Pick a model, write down why you picked it, keep it stable long enough for trends to mean something, and never let it decide anything important on its own. Real budget decisions come from incrementality tests, from sales conversations about what buyers actually said, and from the uncomfortable judgment calls that are the job. The dashboard tells you where to look. It does not tell you what to do.
The seventy twenty ten split holds up better than any model I have used. Seventy percent into what is already performing, twenty into what is showing early signal, ten into novel bets. It requires no attribution to operate and it survives being wrong, which is more than the models can say.