ChannelMix builds several rule-based attribution models into its Multi-Touch Attribution dashboard to help users understand what digital marketing activities, channels and campaigns are creating the desired results.
A rule-based model gives credit for a conversion event as measured by ChannelMix Conversion Tracking, such as a session or a form submission, by using a standard rule that’s applied the same way in almost every situation.
(This is in contrast to ChannelMix's data-driven attribution model, where credit is awarded and weighted according to a marketer’s unique pathing data as captured with ChannelMix ID and ChannelMix Conversion Tracking. The amount of conversion credit could vary greatly by datasets.)
ChannelMix currently offers the following rule-based models:
All the conversion credit is awarded to the first interaction in a conversion path.
All the conversion credit is awarded to the last interaction in a conversion path.
Conversion credit is divided equally among each interaction in the conversion path. If there were five interactions, each one receives 20 percent of the credit.
Most of the conversion credit is divided between the first and last interaction — 40 percent each. The remaining 20 percent is equally divided between the remaining interactions.
The first and last interactions receive most of the credit for conversion, but the amounts are dynamically adjusted and smoothed so that mid-path conversions receive more credit compared to a U-Shaped model.
More recent interactions are awarded exponentially more credit for conversion while earlier ones receive less.
Attribution and Direct Traffic
ChannelMix also offers two versions of each rule-based model: “keep direct,” which includes direct traffic to a website, and “remove direct,” which removes that traffic from consideration in the conversion path.
Direct traffic is when a visitor arrives at a website because they’ve typed its URL into the browser or because they’ve clicked on a bookmark.
Marketers might choose to leave direct traffic out of their analysis if it obscures the real source of a web session.
For example, a marketer using a last-touch model might ignore direct traffic so they can see what paid media sources led a visitor to the marketer’s website.