A linear model is a rules-based model and one of the simplest ones for those who are starting out when moving from single-touch attribution models to multi-touch attribution models. A linear model allocates an equal amount of credit to all involved touchpoints of a conversion path. For instance, let’s say your conversion path consists of Email >> Video >> Display >> Paid Search >> Retargeting. The linear model allocates an equal amount to each touchpoint, so Email gets 20%, Video gets 20%, Display gets 20%, Paid Search gets 20% and Retargeting gets 20%.
Optimizing marketing channels based on an even model means that the advertiser is rewarding frequency alone but not any external factors such as seasonal or macro-economic factors. An issue with this model is that diminishing returns and relative channel effectiveness are not accounted for as all channels and path positions are credited equally so more spend leads to linearly more conversion.
Note that events may happen beyond a particular lookback window — and these will not receive any credit. For example, if the marketer has a 30-day lookback window in the above example, and a Paid Social event actually happened 31 days before the conversion event — then that Paid Social event does not receive any of the credit because it occurred before the 30-day lookback window.
