“Statistics are like bikinis. What they reveal is suggestive, but what they conceal is vital,” said Aaron Levenstein, a former professor of business administration at Baruch College. [Tweet it!]
The same is true of your data in Google Analytics. Most of what you spend your time looking at (and re-looking at) is merely suggestive.
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.
Like many young SaaS startups, we had no shortage of marketing and sales data, but it wasn’t easy to comprehend. The information was there, but it was scattered all over the place.
Some bits and pieces could be found in Google Analytics, while other data was stored in BigQuery and ProfitWell. This arrangement made it challenging to give a quick answer to basic questions on user conversions or to comment on traffic rates and MRR. It wasn’t until we began creating custom dashboards to visualize our data that everything started to click.
Google Analytics 4 (GA4) officially launched in October 2020. Google’s update has left marketers and business owners scrambling to figure out how GA4 will affect their current (and future) marketing and data efforts.
Do you need to rush to install it on all your sites? What makes GA4 so different compared to the current version of Google Analytics?
In this article, we’ll fill you in on what you need to know.
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.
Subscriptions are an increasingly common way to buy products online, whether consumables like coffee and energy supplements, or personalized lifestyle boxes and fashion.
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, founder and CEO of McGaw.io, 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.
Traffic attribution identifies which sources drive visitors to a web property. And it’s impossible to credit a conversion to the correct source without first knowing how a visitor got to a website.
In other words, the foundation of conversion attribution is traffic attribution. Simple as it may sound, attributing a session to its traffic source can be tricky, even impossible.