The goal of a marketing attribution platform is to enhance the ability of marketers to optimize the channels and campaigns that are working, adjust the ones that aren’t, and highlight where there are opportunities to increase the return on marketing investments.
Attribution platforms do this by tracking and crediting the interactions on a website that lead to conversions.
Conversions can be any event the user defines as important to the company’s objectives, e.g., filling in a form, booking a demo, or completing a purchase. In the e-commerce world the most important conversion event is of course a purchase by a customer, as revenue is the key measure of success for a store.
In the section below we summarize the pros and cons of the more widely used attribution models above.
AdAmplify’s Machine Learning
What User Interaction (Touchpoint) Is Measured: Developed by AdAmplify using its Machine Learning (ML) algorithms, the weighting of channels or campaigns is automatically calculated based on customer behavior.
Pros: The ML model dynamically adjusts daily based on real customer behavior, reflecting accurate contributions of each channel.
Cons: None
AdAmplify’s Attribution Enhancement: AdAmplify’s ML algorithm customizes attribution based on user behavior for each store.
Linear
What User Interaction (Touchpoint) Is Measured: Equal weight or credit is assigned to each touchpoint leading to a conversion.
Pros: Gives equal credit to all touchpoints.
Cons: All touchpoints are not equal, and behavior varies by site.
AdAmplify’s Attribution Enhancement: Purchases are attributed based on touchpoints leading to purchases.
Linear Non-Direct
What User Interaction (Touchpoint) Is Measured: Equal weight is assigned to each touchpoint, excluding Direct touchpoints unless it’s the only one.
Pros: Focuses on touchpoints influenced by marketing efforts.
Cons: Not all touchpoints are equal.
AdAmplify’s Attribution Enhancement: Highlights the influence of marketing-controlled touchpoints, excluding Direct ones.
Custom Attribution Models
What User Interaction (Touchpoint) Is Measured: User assigns weights to touchpoints to determine credit for conversions.
Pros: Allows more realistic customization.
Cons: Manually assigning weights can be time-consuming and dynamic.
AdAmplify’s Attribution Enhancement: Not supported by AdAmplify; the ML model automatically adjusts weights.
First Click
What User Interaction (Touchpoint) Is Measured: Revenue is attributed to the first touchpoint.
Pros: Shows the channel’s power in attracting first-time purchasers.
Cons: Disregards other contributing touchpoints.
AdAmplify’s Attribution Enhancement: Repeat purchases are attributed to the first click, showing its importance in gaining loyal customers.
Last Click
What User Interaction (Touchpoint) Is Measured: Shows the final touchpoint’s role in converting customers.
Pros: Shows the final touchpoint’s role in converting customers.
Cons: Disregards previous touchpoints that contributed.
AdAmplify’s Attribution Enhancement: Attribution to the last touchpoint for all purchases helps assess repeat purchasers’ behavior.
Last Non-Direct Click
What User Interaction (Touchpoint) Is Measured: Revenue is attributed to the last touchpoint, excluding Direct unless it’s the only touchpoint.
Pros: Focuses on touchpoints that marketing can influence.
Cons: Disregards other contributing touchpoints.
AdAmplify’s Attribution Enhancement: Shows the effectiveness of marketing-controlled channels by excluding Direct touchpoints.
Position Based
What User Interaction (Touchpoint) Is Measured: 40% of credit is given to the first and last touchpoints, with the rest distributed evenly.
Pros: Attempts to balance the importance of first and last touchpoints.
Cons: May overvalue these touchpoints, undervaluing the others.
AdAmplify’s Attribution Enhancement: Not supported; AdAmplify’s ML model offers more accurate channel weights.
Time Decay
What User Interaction (Touchpoint) Is Measured: Recent touchpoints receive more credit, while older ones get less.
Pros: More balanced than Last Click since it credits earlier touchpoints.
Cons: Overvalues recent touchpoints, undervaluing first click’s contribution.
AdAmplify’s Attribution Enhancement: Not supported; AdAmplify’s ML model provides a better representation of channel weights.
*Note: AdAmplify allows drill-down into ad groups or ads running under your campaigns where applicable.
See also: Which Attribution Model Should I Use?