Four Things Google Analytics Will Never Tell You
GA tracks events, sessions, and conversions. It doesn't track confusion, hesitation, failed interactions, or UX regressions after deploys. Those are four separate blind spots with real revenue consequences.
Google Analytics is good at what it does. Sessions, events, conversions, retention, funnel drop-off. You can build a reasonably complete picture of what users did on your product. For the questions it answers, it answers them well.
The problem isn't what GA gets wrong. It's what it's not built to see.
Blind spot 1: the interaction that failed
GA tracks clicks. It doesn't track whether the click did anything.
If a user clicks a button and nothing happens, GA records a click event. If that same user clicks it four more times, GA records four more click events. The session looks normal. The funnel looks normal. The user is quietly furious.
Dead clicks (clicks on elements that don't respond) and rage clicks (rapid repeated clicks on the same element) are invisible to analytics because they look like normal interaction from the outside. They're only distinguishable by pattern: frequency, timing, and whether subsequent actions occur.
A checkout page with a 15% conversion rate and a "Place Order" button receiving 40% rage clicks has a problem analytics can't see. The conversion rate is real. The rage clicks are also real. The gap between what should be converting and what is can often be traced to interactions that technically registered but didn't work as expected.
Blind spot 2: the question that went unanswered
Loop navigation is what happens when a user visits the same two pages 4+ times in 90 seconds. They have a question. They think one of those pages has the answer. It doesn't, so they keep checking.
This pattern looks like engagement in GA. More pageviews, longer session duration, lower bounce rate. All positive metrics. The user is, in fact, frustrated and about to leave without converting.
Analytics is built around actions. It counts things that happened. It doesn't count the thing that didn't happen: the user finding the answer they were looking for. Loop navigation is the behavioral signal for "the answer they needed wasn't there," and it's invisible to event-based tools.
Blind spot 3: hesitation on a specific field
GA can tell you your signup form has a 62% abandonment rate. It can tell you most abandonment happens at step 2 of 3. What it can't tell you is that 78% of the abandonment traces to users who spent more than 14 seconds on a specific field before leaving.
Form hesitation is a timing signal. The field was filled out, or not filled out, and the user left. The analytics event for both looks identical: field_abandoned or form_abandoned. The distinction between "didn't know how to answer this field" and "didn't want to give this information" is in the hesitation time, which GA doesn't track.
Fixing form abandonment without field-level hesitation data means guessing. Shorter form? Different copy? Remove the phone field? All of those might be wrong. The hesitation data tells you which specific field to fix.
Blind spot 4: UX regressions after deploys
Deploy #447 shipped last Tuesday. Error rates: stable. Latency: stable. Conversion rate: down 8% over the following week.
There's no signal in GA that points back to Tuesday's deploy. The conversion rate decline looks like noise, or seasonality, or a bad traffic day. By the time someone connects it to the deploy, two weeks have passed.
What deploy correlation does differently: it captures a confusion snapshot before the deploy, then monitors confusion scores for 90 minutes after. If any page's score increases 15+ points, an alert fires within the hour with the specific deploy commit hash and which elements spiked.
The same UX regression that would take two weeks to diagnose through analytics shows up in 90 minutes through behavioral monitoring. That's the practical difference between a tool that tells you what happened and a tool that tells you when something changed.
What analytics is actually for
I use GA. Most teams should. Funnel analysis, acquisition source performance, retention cohorts, feature adoption, A/B test measurement. GA is the right tool for those questions.
The blind spots above aren't arguments against analytics. They're arguments for not expecting analytics to answer questions it wasn't built for.
The questions about failed interactions, unanswered questions, hesitation, and UX regressions require behavioral signal data. Not more events, not better funnels. A different category of measurement that watches how users behave rather than which events they trigger.
Both are necessary. The teams that run one without the other are missing either the "what happened" layer or the "why are users confused" layer. Usually the second one, because analytics came first and looks like it covers the monitoring use case. It doesn't.