There are several types of attribution models, each with its approach to credit assignment. 'First-Click Attribution' assigns all the credit to the first interaction, while 'Last-Click Attribution' gives all the credit to the final touchpoint before conversion. 'Time Decay Attribution' distributes credit based on recency, giving more weight to recent interactions, and 'Position-Based Attribution' splits the credit between the first and last interactions, with a smaller portion spread across the middle touchpoints. 'U-Shaped Attribution' attributes most of the credit to the first and last interactions, with some credit assigned to the middle touchpoints as well.

'Algorithmic Attribution' and 'Data-Driven Attribution' are more sophisticated models that use machine learning and statistical analysis to assign credit. These models analyze large sets of data to identify patterns and determine the contribution of each touchpoint. 'Multi-Touch Attribution' considers all interactions a customer has with a brand before converting, providing a more holistic view of the customer journey.

'View-Through Attribution' tracks the impact of ad impressions that did not result in clicks but later led to conversions. 'Click Attribution' focuses solely on clicks as touchpoints. The 'Attribution Window' refers to the time frame during which interactions are considered for attribution. 'Lookalike Audience' is a related concept where marketers target audiences similar to their existing customers, often using insights gained from attribution models to identify these audiences.