the return on your advertising investment. Over time, this enables better budget allocation

across your marketing channels.

During this lesson we will review: what attribution modeling is

the pre-made attribution models in Google Analytics and when they are useful

and how to get started with the Model Comparison Tool.

An attribution model is a rule, or set of rules, that determines how credit for sales

and conversions is assigned to a channel within a conversion path.

The attribution model that Google Analytics uses in most reports, is the "last-click"

attribution model, but there are many other types of models available in Google Analytics

through the Model Comparison tool. Before we take a look at the tool, let's review a

few simple examples of attribution models.

The Last Non-Direct Click model ignores direct visits and attributes 100% of the conversion

value to the last channel that the customer clicked through from before buying or converting.

Again, this is the model that Google Analytics uses by default. This model is useful if you

consider direct visits to be from customers who have already been won through a different

channel.

Unlike the Last Non-Direct Click model, the Last Interaction model attributes 100% of

the conversion value to the last channel, regardless of whether or not it was a direct

visit. This model is used as the default benchmark in the Model Comparison tool.

The First Interaction model attributes 100% of the conversion value to the first channel

with which the customer interacted. This model is appropriate if you run ads or campaigns

to create initial awareness.

The Linear model gives equal credit to each channel interaction on the way to conversion.

This model is useful if your campaigns are designed to maintain contact and awareness

with the customer throughout the entire sales cycle. In this case, each touchpoint is equally

important during the consideration process.

Other default models in Google Analytics include the Time Decay model, which most heavily credits

touch points closest to the time of conversion, and the Position Based model, which splits

gives a percentage of credit to each step in the conversion path based on it's position

-- first, last, or in between.

In addition to these more basic models, you can use the Model Comparison Tool to create,

save, and apply a custom model that uses the rules you specify. This allows you to tailor

models specifically to the set of assumptions you wish to evaluate.

For more information about the pre-defined models and creating custom models, check out

the Attribution Modeling examples in the Google Analytics help center.

Now let's take a look at the Model Comparison Tool. This tool allows you to compare how

different attribution models impact the valuation of your marketing channels. By selecting multiple

attribution models in the tool, you can compare the the number and value of conversions as

calculated by each model you've selected. You can select up to three attribution models

at a time for this comparison.

The calculated conversion value (and the number of conversions) for each of your marketing

channels will vary according to the attribution model used.

When comparing models, look for channels whose value changes significantly from one model

to another. This is an indicator that a channel is more suited to a particular part of the

customer acquisition funnel.

For example, in this report, we can see that under both the linear and first interaction

models, our social network and referral traffic is considered more valuable.

The actionability of these reports is to look for some commonalities between various models,

and then use the insights you find to test new hypotheses.

For example, you might increase your investment in a particular channel that was previously

undervalued according to the last click model. Or you might decide that budget for a channel

is more valued under the first interaction model should be shifted towards marketing

campaigns that drive awareness, rather than ones focused on direct response. The point

is you must experiment to validate your model.

For more information on how to get started with the Attribution Model Comparison tool,

check out the Google Analytics Help Center and the additional resources in this lesson.