In 2023, Google Tag Manager (GTM) is probably the single most useful tool that your team can use to collect data and inform smart marketing decisions.
Understanding the benefits of GTM and how to properly utilize it can be a challenge, so we’ve put together this guide as a reference point.
Over the last years, Google Data Studio has evolved from an appealing but clunky application to a tool that we recommend to any digital marketer.
Data Studio allows you to communicate data simply and in a repeatable format, and their expanded integrations, customizations, and editability have made Data Studio dashboards extremely powerful.
A relatively new feature, data blending, came out last year. This underused function can do a lot of cool things; it also has some limitations. Once you’ve got your head around the basics, the possibilities are endless.
Analyzing the customer journey is pivotal to conversion optimization. But how do you track user journeys in a way that is digestible, visual, and useful?
With funnels, of course! Funnel tracking in Google Analytics is one of the best ways to identify—in detail—where you’re going wrong.
But, cutting through the noise can be a challenge.
Strategically leveraging YouTube’s robust analytics can help you make data-backed decisions and improve performance.
In this post, we’ll tell you how to use YouTube analytics to grow your brand and generate more video content views.
Google plans to end Chrome’s support of third-party cookies by 2022, and they created a Privacy Sandbox to test new ideas and solicit feedback. Decisions that affect Chrome—with a nearly two-thirds market share—are decisions that affect the Internet, especially paid advertising.
When you hear “data segmentation”, it’s tempting to feel overwhelmed. Why? Segmentation can seem daunting (or boring) to those unfamiliar with it.
It’s an unfortunate because segmentation is perhaps one of the most effective tools at our disposal. The ability to slice and dice your Google Analytics data is the difference between mediocre, surface-level insights and meaningful, useful analysis.
In this article we’ll show you how to setup your Google Analytics to unlock actionable insights.
Not long ago, it was common for marketers and web analysts to spend the bulk of their day staring at Excel spreadsheets, manually collecting and organizing ad spend data across dozens of sources.
You had to go to each advertising account and export statistics on advertising campaigns, such as ad impressions, clicks, and costs, then export data from the web analytics system, and, finally, combine all the data manually.
Not an optimal use of time.
For a long time, I considered standard Google Analytics reports to be the best way to get useful insights. From time to time, I struggled with sampling, limitations, and weird results, but I didn’t see a way around it—until I discovered Google Analytics 360 and raw data exports into Google BigQuery.
After a few hours playing around with SQL, I was already able to deliver insights I never could have with aggregated Google Analytics reports. Since that day, I’ve been exploring how raw data can be a web analyst’s best friend.
Google Analytics shows 104 conversions. Your CRM shows 123 new leads. Heap reports 97. And so on.
For most companies, Google Analytics is a—often the—primary source of analytics data. Getting its numbers aligned with other tools in your martech stack keeps results credible and blood pressure manageable.