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.
According to Amplitude, product analytics “show you who your users are, what they want, and how to keep them.”
I remember the first time that a client told me how much analytics had helped their business. They were able to increase their sign-up rate for their product by 22% while reducing their marketing costs. The secret to their success?
They simply used their analytics data to make informed decisions.
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.
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.
For a web analytics analyst or a data-driven marketer, these are words to live by: “Without data, you’re just another person with an opinion.”
Optimization isn’t about educated guesses and hunches, no matter how many years you’ve been in the industry. It’s about doing the research, asking the right questions, digging for clues in problem areas, paying attention to the signs when they appear, and running smart A/B tests.
Web analytics analysis is a big part of that. It helps separate the optimizers from just another person with an opinion.
Google Analytics shows 104 conversions. Your CRM shows 123 new leads. Heap reports 97. And so on.
It’s easy to get frustrated by data discrepancies. Which source do you trust? How much variance is okay? (Dan McGaw suggests 5%.)
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.