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A customer rarely converts on the first visit. They arrive from a paid ad, come back from an email, search for you by name, and then buy. That is one conversion and several touchpoints, and an attribution model is the rule for dividing the credit between them. Soran divides each conversion so the total always adds up to one. This is the difference between Soran’s numbers and your ad platforms’: each platform claims a conversion it had any hand in, so their totals overlap and, added together, overstate what your store actually took.

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The models

Soran builds the path from the sessions that led to the conversion, ordered by when each session started, and keeps only those inside the attribution window.

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Choosing between them

Last touch flatters whatever closes — often brand search, which was going to happen anyway. First touch flatters whatever discovers. Both are single-touch models and will disagree sharply on the same data, which is informative in itself: a channel that looks strong on last touch and weak on first touch is harvesting demand rather than creating it. Linear, time decay, and position based are the multi-touch middle ground. Use time decay when your buying cycle is short and recency genuinely matters; use position based when discovery and closing are both worth naming. Participation answers a different question — which channels were involved at all — so its totals are not comparable to the others.

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Milestones

Position-based attribution anchors on the first and last touchpoints, and can also anchor on sessions that contained a milestone event you have configured. That lets a step you consider important in the journey earn a named share rather than being averaged into the middle.

Goals

Attribution is always calculated against a goal.

Sessions and touchpoints

How each touchpoint on the path is identified.