An attribution model is a rule or set of rules used to assign credit for a conversion to the various marketing touchpoints a customer interacted with on their path to that conversion, such as first-click, last-click, linear, or data-driven models.
Last-click attribution — giving all the credit to the final touchpoint before conversion — remains the default in many basic analytics tools because it's simplest to calculate, but it tends to overvalue bottom-of-funnel channels like branded search while undervaluing earlier touchpoints that first introduced the customer to the brand. More sophisticated models like linear, time-decay, or data-driven attribution attempt to correct for this, at the cost of being harder to explain.
If a customer sees a social ad, later clicks a search result, and finally converts after clicking a retargeting email, a last-click model gives 100% of the credit to the email, while a linear model would split credit evenly across all three touchpoints.
Last-click attribution, which gives full credit for a conversion to the final touchpoint before it happened — it's the easiest to explain and calculate, which is why it's the default in many analytics tools.
Because each applies a different rule for splitting credit across touchpoints, so a channel that looks strong under last-click attribution can look much weaker under a first-click or linear model, and vice versa.
No — the right model depends on the business's typical customer journey length and goals; many marketers use several models side by side to see how differently each channel's performance is portrayed.