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.
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.
For companies that build their analytics on Google products, purchasing Google Analytics 360 is a symbol of maturity.
As a business grows, it inevitably runs up against limitations of analytics tools. For example, while the data aggregation process in Google Analytics seems like a “normal” feature, it might be a hurdle if your business needs to process data at the hit level instead of by sessions or campaigns.
Peter Drucker famously said, “What gets measured gets managed.” But what if your data is wrong? What if you’re not measuring correctly or completely? What if there’s a whole pile of things you think you’re measuring when really…you’re not?
A lot of the people relying on Google Analytics are relying on bad data. No, not because Google Analytics is awful. Because their configurations are broken. That’s why you need to conduct a Google Analytics audit.
The first step toward plugging the leaks is identifying where the leaks are. Which funnel steps, which layers of your site, which specific pages are leaking money? Google Analytics can provide answers.