Use and interpret MCF Data-Driven Attribution
|This feature is only available in Google Analytics 360, part of Google Marketing Platform.
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You can use Multi-Channel Funnels (MCF) Data-Driven Attribution to discover new ways to optimize your ROAS. Here are some suggested steps to get you started:
- Choose a conversion type you want to analyze: Review your Analytics Goals and Ecommerce transactions, and determine which conversion type you’d like to improve. For example, let’s say you’d like to get more people to fill out your lead-generation form, and you’ve set that as a Goal. You can use the Conversion Type selector in your Model Comparison, ROI Analysis, and Model Explorer reports to focus your analysis on this conversion type.
- Select a campaign, channel, or set of keywords to optimize: Look at your Goal (completed lead-generation forms), and evaluate the techniques you’re currently using to drive conversions. Suppose that you’ve been doing display advertising, and you want to improve returns from that channel.
- Compare the MCF Data-Driven Attribution values to those from your standard attribution model: Many advertisers use the Last Click model as their default attribution model, but you should compare the new values to whatever model you’ve been using. View the MCF Model Explorer to see how the Data-Driven values were calculated.
- Identify the touchpoints with the biggest changes across models: Use the MCF Model Comparison Tool to compare the conversion credit between your Data-Driven model and up to two other models. Sort your data based on percent change in CPA (cost-per-acquisition) to find the channels or campaigns where changes will have the greatest impact.
- Shift budget and resources to support high ROAS opportunities: Now that you’ve identified which channels (or campaigns, or keywords) have the greatest potential, adjust your programs and test the results.
- Adopt the MCF Data-Driven Attribution model: Once you’ve reviewed the MCF Model Explorer and seen the impact of your budget and resource changes, you’ll learn how the MCF Data-Driven attribution model relates to your prior default model. Then you won’t need to compare anymore—you can leverage on the ROI Analysis report, which lets you focus on optimization insights using only your custom MCF Data-Driven attribution model.
The underlying probability models used by MCF Data-Driven Attribution have been shown to predict conversion outcomes better than first- and last-click methodologies, but no model is 100% perfect. MCF Data-Driven Attribution seeks to best represent the actual behavior of customers in the real world, but it is an estimate that should be validated as much as possible using methods such as controlled experimentation.