This video answers:
How the different Attribution models in Klar work & how they attribute orders to touchpoints/channels
Which Attribution model to use when
Attribution Models Explained
We're looking at this specific customer journey to go through the attribution models:
Static Attribution Models
Last Touch - last touch gets all the value -> 100% Email Automation in this case
First Touch - first touchpoint gets all the value -> 100% TikTok in this case
Linear - every touchpoint gets the same ratio of the full value -> 20% for each of the 5 touchpoints
U-Shape - first and last touchpoint get a bit more, other touchpoints in between get less but equally distributed part of the value -> 30% TikTok, 13% each Facebook Paid, Influencer and Branded Paid Search, 30% Email Automation
Unique - full value for each touchpoint -> 100% TikTok, 100% Facebook, 100% Influencer, 100% Branded Paid Search, 100% Email Automation
Dynamic Attribution Models (Klar unique)
Data-Driven - reallocating the value of touchpoints dynamically based on time spent on site, user intent, channel type, order of channels and time lags and also Zero-Party-Data like discount codes and post-purchase-survey (PPS) answers
If the touchpoint that's mentioned in the Zero-Party-Data (in a discount code or PPS answer) does not exist already in the journey, we'll inject a new touchpoint, adding it to the journey as last or first touchpoint
Marketing Mix Model (built on top of Data-Driven model) - factoring in correlations between channels and reallocating branded/direct traffic to the original source where the demand was generated, based on Machine Learning algorithm
PPS answers, correlations of spend impact, impression impact and engagement impact on branded traffic and conversions
When to use which Attribution Model
Unique: might be useful for day-to-day optimization, "which creatives have some sort of impact on a conversion"
We wouldn't recommend relying on the other static models as the reality is way more dynamic and blurry
Data-Driven stacked with Marketing Mix Model: giving you an understanding of what marketing channels are actually driving conversions -> to allocate the marketing budget to the channels that are most relevant & effective in your user's journey
MMM especially useful for short user journeys that seem to be mainly driven by branded/direct channels (based on clicks observable)
Factoring in things that don't generate a "touchpoint" like Word of Mouth as a new channel (mainly via PPS)
Reallocating direct / branded traffic to the original source the demand was generated
-> Understanding what the impact of my marketing channels really is and which of them are actually driving conversions
-> Best option to allocate marketing budget because it factors everything in