How does it work?
Smart Lists are built using machine learning across the millions of Google Analytics websites which have opted in to share anonymized conversion data, using dozens of signals like visit duration, page depth, location, device, referrer, and browser to predict which of your users are most likely to convert during a later visit.
Based on their on-site actions, Analytics is able to calibrate your remarketing campaigns to align with each user’s value.
If you use eCommerce transaction tracking and have enough traffic and conversions, your Smart List will be automatically upgraded. Marked as [My Smart List], your list will be customized based on the unique characteristics that cause your visitors to convert. Only you will have access to this list, and no new data will be shared whether you use this feature or not (learn more).
For practitioners, the promise of big data is also the burden - there are so many analyses to run, so much opportunity. With Smart Lists, as with Data Driven Attribution, Google Analytics is operationalizing statistical analysis - making us not just smarter marketers - but faster and more nimble.
While we might have been able to achieve similar results with ongoing statistical analysis and a complex cookie structure, Smart Lists are simply plug and play. This speeds us along, so we can focus not on list management, but on growing the business.
For best results, make sure your Google Analytics goals and transactions are being imported into AdWords, then combine your Smart List with Conversion Optimizer using Target CPA or ROAS in AdWords.
If you’re new to remarketing, the Smart List is a great way to get started with strong performance results. As you get comfortable with remarketing you can tailor your creatives and apply a variety of remarketing best practices.
If you’re a remarketer already employing a sophisticated list strategy, stay tuned while we gear up to extend this signal directly for your current lists as an optimization signal used in AdWords bidding.
We’ll be continuing to iterate on these models in order to help users better understand and act on their data. We’re also working on surfacing these signals elsewhere in your reports and in the product so you can dive into what factors help predict whether a user will likely convert.
We welcome your feedback and ideas. Please leave them right in the comments!
Happy Analyzing,
Ismail Sebe and Dan Stone
on behalf of the Google Analytics Team
