If you are a marketer, return on investment from various advertising platforms is probably the most important metric you have to pay attention to on a weekly, or even daily basis. However, those numbers you obtain from your platforms do not capture your real marketing ROI accurately, and in many cases, they will be significantly inflated.
Why are my marketing ROIs inflated?
Most of the platforms over the internet use a “conversion window” model when calculating conversions.
- As long as a customer buys or converts within a certain number of days, this ad will take full credit for that conversion.
- This creates an insane double-counting problem when you are looking at ROI for either platform
Linear Attribution
The linear attribution model attributes credit to all traffic sources that are involved in the conversion process evenly.
- However, in many cases, the credit should NOT be distributed evenly. For example, if Mike was unimpressed by his first visit via Facebook and left the homepage of the website without any interaction with your product or your brand, and only to come back impressed by your display ads on Google?
Position Based Attribution
The position based attribution model gives 40% credit to the first and last interaction of the entire conversion journey, while linearly distributing the remaining 20% to rest of the visits.
- In case of only two visits, it acts very similarly to linear attribution model, and attribute 50% to both the first & last visit.
The Humanlytics Take On Attribution Model
The most important factor that determines the importance of a visit is what users actually did during that visit.
- How intense was the engagement between the brand and the users? This can be measured by a combination of engagement metrics (such as pages/session and session duration) and completion of what we call “engagement objectives”.
- Did the visit result in a business conversion? This is measured by the number of “business conversions” accomplished at each visit, such as buying a product.
Variations of Last Interaction Attribution
The “Last Non-Direct Click” attribution
- Assigns all the credit to the last visit of the users that is not from the “direct” channel, as it is very hard to measure user intention from that specific channel.
- “Last Google Ads Click” Attribution model assigns all the credits to the user that is a result of Google Ads.
What is an Attribution Model?
Attribution modeling describes various methods marketers use to properly break up and assign conversion credits to various different channels in case users take multiple website visits, via multiple channels, to arrive at the ultimate conversion behavior.
- A valid attribution model for your business must cover ALL channels your users might visit your website from, or else your calculation will be inaccurate and mostly inflated
First Click Attribution
Similar to last interaction attribution, but instead of giving all credits to the last channel user visited through, this gives all credit to the first channel.
- In Mike’s case, Facebook will get the full $100 credit, while Google Ads gets none.
Time Decay Attribution
More advanced variation of linear attribution
- Gives more credit to traffic sources that are closer (in time) to the ultimate conversion
- Does not resolve the concern of assigning too much credit to useless visits
- The closer a visit is from conversion, the more important it is
- Engagement on the website is a much more important factor compared to time distance to conversion
Attribution Model Examples
At the core of attribution modeling are attribution models – the logic you will use to assign credit to various traffic sources of your customers’ conversion journey
Last Interaction Attribution
The last interaction attribution model is the default conversion model of Google Analytics
- It gives credit ONLY to the very last traffic source that resulted in the conversion of a user
- Google Analytics will completely ignore the fact that Mike performed his first visit via Facebook, and assign the full conversion credit to Google
