ChannelMix Modeling Engine | Rule-Based Attribution Models

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:

First Touch

All the conversion credit is awarded to the first interaction in a conversion path.

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Last Touch

All the conversion credit is awarded to the last interaction in a conversion path.

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Linear

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.

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U-Shaped

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.

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Smooth U-Shaped

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.

Time Decay

More recent interactions are awarded exponentially more credit for conversion while earlier ones receive less.

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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.

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