Google Analytics mistakes can really derail your data collection strategy, no matter what your level of expertise is.
Some mistakes are so common that nearly all sites, without the foresight, will make them. Sometimes, they’re quickly remedied and sometimes not.
Other mistakes are a bit sneakier, and you may not notice them until it’s too late.
The best thing you can do is avoid Google Analytics mistakes upfront. That way, you’ll avoid technical debt and have data you can trust and base decisions on.
This article will cover 30 Google Analytics mistakes to avoid.
GA Mistakes #1: Trusting Your Data Blindly
Approach your data with skepticism. Ask questions. Measure twice, cut once.
If you trust your data after scrutiny, it will go a long way in guiding your business decisions.
While the typical analyst, in my experience anyway, is a person prone to skepticism (and probably cynicism), too many people are far too optimistic about the precision of their data. Whereas in many arenas, this critical lens may slow you down, it’s crucial to look at things critically in analytics.
Here’s how Jeff Sauro, Founder at Jeffalytics, explains it:
“For example, beginner analysts may look at the cumulative conversion rate metric in Google Analytics and report “we had a 120% conversion rate last week, woohoo!” when in reality, the website had no conversions of value.
The reason for the 120% conversion rate? Someone set up a goal where every visit over 30 seconds counts as a conversion + a second goal where more than 2 pageviews is a conversion.
Add these two up, and the conversion rate is off the charts. Only it’s representing a meaningless metric that’s not tied to actual company success.
Most of the time, these mistakes are caught, but I’ve been in many situations where conclusions from bad data are brought all the way to the top of an organization.
How do beginners get past these false conclusions? They learn to be skeptics. The first thing I consider when I look at a new data set is “how is this data wrong?” If I see a major increase or spike in traffic? I assume it’s incorrect tagging or bot traffic.
If it sounds too good to be true, IT PROBABLY IS. Veteran analysts know this, because they have been burned before. Newbies trust blindly. Veterans are skeptics.”
The important part here is “if it sounds too good to be true, it probably is.” This is a rephrasing of Twyman’s Law. Essentially, look at things with a lens of skepticism, and you’ll avoid most critical failures of judgement.
GA Mistake #2: Spending Little Time on Strategy
A similar yet different mistake is when analysts and marketers move too quickly without stewing on the strategy.
This is an age old problem, where there’s usually an inherent tradeoff between strategy and execution (zooming in and zooming out).
While execution and simply “getting shit done” is certainly important, it helps to have someone in the room that asks, “are we even moving in the right direction?” or “are we asking the right questions?”
Here’s how Joao Correia, Solutions Director at Igloo Analytics, puts it:
“I think the biggest mistake setting up GA is not spending enough time on strategy, understanding business requirements and how GA will contribute to business growth or optimization.”
Thinking about your business goals, what areas of opportunity exist, and what the most impactful focus areas are can help you avoid technical debt as well as focus you on the right things for the long term. While you may lament the lack of motion (or the appearance thereof), you’ll definitely work more efficiently in the long run if you take a strategic view of things.
GA Mistake #3: Moving Quickly Without Checking Your Setup
You may be noticing a trend: most common Google Analytics mistakes are macro-level things dealing with how you approach tracking and analysis. Once you conquer those things — your attitudes, knowledge, and approach — a lot of the other stuff falls into place.
One very important thing to stand by — related to the skepticism bit, by the way — is to “trust but verify” your setup. Make sure things are properly configured before setting things live (or risk the build up of technical debt).
Chris Mercer, founder at measurementmarketing.io, puts it this way:
“Your first step should be setting up Google Analytics, so it will report back to you what we call as “useful version of the truth.”
For example, if you aren’t properly tagging your traffic with UTM’s, that’s the priority. Only then will the “Source/Medium” reports start to provide really useful details. If you have UTM’s all setup, then move on to goals. That way, that same “Source/Medium” report will not only tell you where your visitors are coming from, it’ll start telling you just how effective those traffic sources are.
Take it one step at a time. It’s like riding a bike… a bit wobbly at first and you’ll skin your knees a bit when you fall down.. but pretty quickly you’ll get the hang of it and start doing backflips. :)”
Additionally, it helps to take a look at your Real Time reports in Google Analytics to make sure the right events and goals are firing (if that’s what you’re implementing):
You can (and should) also do a full audit from time-to-time, but it also helps to get in the habit of “measure twice, cut once” while you’re actually setting things up.
GA Mistake #4: Not Tracking Every Page
Something you’ll uncover in a Google Analytics audit more frequently than you’d believe: Google Analytics tracking not being on every webpage.
Obviously, you can see the problem here. You’re completely missing certain data.
Now, you could walk through your site and check to see if your Analytics script is on every page. However, if you have more than 5 web pages, that would be a headache. Luckily, there are dozens of tools that will crawl your site and report where there’s any missing tracking (or duplicate tracking, depending on the tool). This list provides 6 such tools.
Be particularly careful if part of your website is a single page app.
As Ryan Farley, co-founder of LawnStarter Lawn Care says:
See Google’s Documentation if you use React, Angular or any other single page app framework.
GA Mistake #5: Not Using Google Analytics Filters
Google Analytics filters give you tons of value, especially at the organizational level and especially when you operate a large site with many pages (and many different business functions).
Additionally, filters can help clean up your data. According to Johannes Mehlem, Senior Web Analyst at HubSpot, not using Google Analytics filters is one of the most common and critical mistakes even sophisticated users sometimes make. According to him, it’s a mistake to operate without a separate view that includes filters such as:
- excluding sessions from internal IPs
- prepending hostname to Request URI
- removing query strings
These are pretty universal problems with Google Analytics reports, things that will muddy up your numbers and make it more difficult to make decisions. So, start using filters (in addition to one unfiltered raw data View) to sort your data better.
Note: to get any good with filters, you’ll need a good understanding of Regular Expressions. This is the simplest and most useful guide I’ve found (nothing bad comes from Lunametrics).
In addition, it helps to read up on best practices for Google Analytics filters. This article from Online Metrics explains things well.
GA Mistake #6: Incorrect Cross-Domain Tracking
We’ve done a whole article on cross-domain tracking–and if you have more than one web property, or a subdomain (subdomain tracking is slightly different but along the same implementation lines), you should pay attention.
If you haven’t set up cross-domain tracking (much easier in GTM by the way), then you’re severely fracturing your analytics data. You’re missing out on a huge piece of the online behavior puzzle.
This is a super common problem, too, even among top “data-driven” companies (some household brand names haven’t figured this out yet).
Read this previous KlientBoost article on cross-domain tracking to get a full walkthrough on how to set it up.
GA Mistake #7: Not Linking Other Google Platforms
An inherent benefit of the Google ecosystem is that things integrate very well.
So, if you’re using Google Tag Manager and Google Analytics, many tracking solutions are simply plug-and-play. If you’re using Google Analytics and Google Optimize, it’s so easy to integrate and analyze experiment data. If you’re using Google Analytics and Google Data Studio, it’s so easy to port data, create dashboards, and share your reports with your team.
You get the picture.
Especially with regards to Google Analytics, you need to enable AdWords and Search Console. It’s pretty much the click of a button (so easy) and you get much greater insights as well as the opportunity to target specific user segments.
For example, if you have Enhanced Ecommerce enabled (and if you’re integrated with Google AdWords or Doubleclick), you can create an audience off of anyone who abandons at a particular step and use them in your AdWords campaigns. The segmentation and targeting capabilities are endless and really empower the marketing department to get precise with targeting.
If you haven’t integrated your systems (particularly AdWords and Search Console) head to your admin section and do so now.
Once you’ve set that up, you can do cool things like build custom audience to use for remarketing in AdWords. You set that up in your Admin panel as well, under Audience Definitions > Audiences:
Anything you can set up as a custom segment in Google Analytics, you can also set up as an audience for targeting on AdWords. Just set it up like you normally would and import your audience to AdWords with a few clicks:
GA Mistake #8: Not Automating Reporting When Helpful
A philosophy that helps bring efficiency to any program (marketing, analytics, etc.) is to automate and scale what you can, and continue working on errors that require more human touch.
A lot of what we do in Analytics can and should be automated. Think about it: if you’re continually repeating the same tasks, how can you reduce the amount of manual work without reducing the effectiveness or impact?
There are so many ways to do this, especially with reporting (analysis, at this point in time, still requires a human mind to make sense of the numbers).
There are two main ways people automate reporting with Google Analytics:
In my opinion, Data Studio is way easier, especially after you’ve played around in it for a bit. But you can actually customize more if you’re using the Analytics API and Google Sheets (or R or Python if you’re really adventurous).
Whatever route you choose, make sure it makes sense for the organization and is producing the results you need.
At the very least, if you just need a basic benchmark report each week, just automatically send yourself one in the Google Analytics interface. Create custom report and click “share” on the top right side of the screen. There you can choose who to send your report to and how often:
GA Mistake #9: Sloppy Campaign Tracking
As a marketer, you need to know about campaign tracking and be able to use UTM tags well. They look like this:
These campaign parameters are how you can analyze the effectiveness of traffic sources as well as specific campaigns you’re running.
There are five UTM tags used by Google Analytics:
My background is in marketing, so I want to apologize to analysts on behalf of marketers: many of us mess up campaign tracking.
It’s a common frustration, and it’s not just a technical solution. See, even if you’re using UTM parameters (and you should be) and you’re setting them up right (it’s not super difficult), it can become a mess if no one knows the standards and rules of thumb for creating campaigns.
That’s why there should always be centralized documentation, like this spreadsheet from Cardinal Path, where everyone can create campaign tracking from the same place and document it for others to see.
GA Mistake #10: Poor Views Strategy
Lots of companies only have one view setup in Google Analytics, and implement and view all their reports there.
Big companies especially need to operate with filters and have different access points for different teams. But even small companies should (at least) have three views:
- Master view – where you work from
- Sandbox view – where you try new implementations first to see that they work
- Raw data view – untouched, to keep a fresh copy in case SHTF
The big reason you want a raw data view (as well as a sandbox/test view) is that certain features, like filters, permanently alter the data in the view. Once you set it up, you can’t go back.
This article goes quite into detail about how you should structure your views at the basic level (there are always specific circumstances that depend on your company and team). But at the very least, set up the basics: a master/working view, a test/sandbox view, and a raw/untouched view.
GA Mistake #11: Not Using Annotations
Annotations are simple: they’re basically notes you can use to add context to your Analytics reports.
If you’re a one-person team, you may not need to add context like this. But if you have multiple team members using Google Analytics, and especially if you’re hiring new people that don’t have historical company knowledge, annotations are life savers.
Annotate anything you think might need context:
- Big traffic spikes (what’s the reason?!)
- Start of marketing campaigns
- Website redesigns or massive experiments
- Website outages
- Public relations or virality events
- Competitor activity
- Any seasonality explanations
Basically, add notes for any time series anomalies.
GA Mistake #12: Not Using Google Tag Manager
There’s simply no reason to hard code Google Analytics onto your site. Google Tag Manager is free. Use it to organize your tags, including Google Analytics (unsurprisingly, the two tools work well together). Installing GA using GTM will open up further opportunities for advanced tracking down the line as well.
According to Mark Lindquist, marketing strategist at Mailshake:
“We had GA hard-coded on our site (Mailshake) for a long time. I didn’t even realize how limiting it was until we moved to Google Tag Manager. I didn’t want to bug our dev too often, so I tried to bunch together requests for simple stuff like adding code snippets of tools like Sumo or Hotjar. That meant things that took our developer 30 seconds wouldn’t get implemented for days, which sometimes would hold up marketing campaigns we wanted to run.
Once we added GTM, I was able to add those things myself, and set up fairly sophisticated tracking without ever having to tap our developer. It’s condensed what were week-long projects into a few hours.”
Realistically, whether you’re just setting up Google Analytics or you’re managing tons of domains and subdomains, Google Tag Manager will save you time, energy, and frustration.
For instance, if you want to enable any sort of advanced event tracking, you’ll want to use Google Tag Manager. Similarly, when you’re operating at scale, it’s simply inefficient to manage all of your scripts and tags outside of a tag management system (GTM isn’t the only one out there; it’s just the easiest to work with Google Analytics and it’s free).
Pro tip: take this excellent tag manager course from Chris Mercer if you’re a beginner. Take this one from Simo Ahava if you’re advanced.
GA Mistake #13: Not Setting Up Goals
If you don’t set up goals, you can’t use Google Analytics. Harsh? Maybe. True? Definitely.
If your website has any purpose whatsoever, you should have goals set up. I guess if it’s just a novelty site, you can get away with no goals. But for 99% of people, you’ll want to set them up.
Given how simple it is to set up goals in Google Analytics, it’s surprising to learn that a large percentage of sites don’t ever set them up (including some sites that aren’t doing too poorly in terms of revenue).
However, you can’t meaningfully use GA features like segments, audiences, events, etc., if you don’t first define what a meaningful goal is on your site and set up the adequate tracking.
Plus, you can’t analyze your funnel behavior if you don’t have goals set up. This is one of the core tools an optimizer will use to begin optimizing a website:
GA Mistake #14: Not Setting Up Events (Correctly)
Once you’ve got goals set up, ideally you’d be able to track some more micro-behaviors that don’t necessarily mean a purchase or conversion.
These actions could be things like hovering over a promotion (or just viewing it), interacting with a video on a product page, or clicking an interactive site element. All of this is done by setting up events in Google Analytics.
Take a step back and strategically think about what behaviors matter on your site and what you’d like to (and are able to) track. Implement event tracking for these.
GA Mistake #15: Overvaluing Certain Metrics (Like Bounce Rate)
Out of the box and without context, bounce rate means almost nothing, yet it’s almost universally looked at as an important metric (we must lower bounce rate). What’s more important is to look at metrics in their appropriate context. In the case of bounce rate, that’s asking things like:
- How do we calculate bounce rate on our site?
- What’s the purpose of this page?
- How does the bounce rate on this page compare to others of similar purpose?
Then, given the context, you can judge if there is an issue with your metric.
Don’t simply think things like: high bounce rate = bad, or high click through rate = good.
The same goes for other “engagement” metrics like time on page. The goal is to get as close as you can to core business metrics (revenue, purchases, etc.), and optimize for those. If you can’t get close to those, continue up a level or two–but make sure that the metrics you’re optimizing for contextually matter and contribute to your overall goals.
GA Mistake #16: Not Using Custom Alerts
Custom alerts help you notice things and become more productive. You can set them up quite easily in Google Analytics:
To create a custom alert:
- Sign in to Google Analytics.
- Navigate to your view.
- Open Reports.
- Click Customization > Custom Alerts.
- Click Manage custom alerts.
- Click + NEW ALERT.
Most people use them to catch anomalies in your data, like giant drops in conversions or traffic. Then, catching this earlier, you can investigate as to the cause. You can also set up alerts to find technical problems like broken a/b tests and broken links.
GA Mistake #17: Double Tracking Pages
Lack of tracking on certain pages is a problem, but so is the reverse: many Analytics implementations have duplicate tracking in place.
How does this happen? Maybe you have both hard coded and Google Tag Manager implementations on certain pages, old forgotten code on pages still, using internal UTM parameters on your own site (e.g. using UTM tags from one blog post that links to another).
Whatever the case, it’s not good, because it’s simply another implementation error that messes up your data.
The best way to avoid this mess is just by using a tag manager, as well as having a solid campaign tracking process in place (both mentioned in this article).
The best way to detect this is by analyzing your landing pages and looking for near zero bounce rates. Of course, extremely low bounce rates can also occur because of a bad event tracking set up, but it can also result from two pageviews being fired on a page.
GA Mistake #18: “All Data in Aggregate is Crap”
The gold is in the segments, as they say.
Or, as the joke goes, “Bill Gates walks into a restaurant and everyone becomes a millionaire…on average.”
That’s all to say, you should go beyond the aggregate data that’s shown on most primary Google Analytics reports. Sure, it’s great to see how much organic traffic and conversions you have. But to answer real business questions, you usually have to dive into the segments.
It’s tough to be too prescriptive here, because everyone’s situation is different. But at the very least, take a look at the behavioral differences between:
- Page load speeds
- Different browsers and devices
- Converters vs non-converters
- Those who fired [X] event and those who didn’t
- Bounce sessions vs non-bounce sessions
There are lots more, obviously — the list of segmentation possibilities is endless, and only limited by your creativity and time. This is a good article to get you started.
GA Mistake #19: Looking at Data Points Without Context
These questions are super common:
- What’s a good conversion rate?
- What’s a good bounce rate?
- What’s a good amount of organic traffic?
Everyone wants to know where they stand, and that’s why competitive benchmarks are so popular. But it’s really the wrong way to frame the question.
Peep Laja of CXL puts it really well: a good conversion rate is one that’s better than what you had last month. Context matters. Here’s how he put it:
“The average conversion rate of a site selling $10,000 diamond rings vs an ecommerce site selling $2 trinkets is going to be vastly different. Context matters.
Even if you compare conversion rates of sites in the same industry, it’s still not apples to apples. Different sites have different traffic sources (and the quality of traffic makes all the difference), traffic volumes, different brand perception and different relationship with their audiences.”
Data points are great to collect, but you need data trends and comparisons to truly understand the context of a given number. Otherwise, what are you going to do if you have an average bounce rate of 55%? There’s no actionable insight there.
GA Mistake #20: No Site Search Reports
If your site has a search feature, and you haven’t enabled site search analytics, do so now. It’s easy to set up (instructions here).
Of course, it’s self-evident why it’s valuable to see what people are searching on your site:
- You gain qualitative insight on what people are interested in (especially with a blog).
- You find content gaps where you’re currently not showing anything for a given search result.
- You can quantitatively look at search trends to find new content to create.
Generally speaking, it’s just another great passive customer feedback mechanism to find out what your visitors want and are looking for. It’s so easy to set this up and the insights are valuable, so there’s no reason not to set it up.
GA Mistake #21: Bot & Spam Traffic
A spike in traffic feels good–but if it’s fake traffic, it’s worse that useless, it’s misleading and toxic, skewing your actual numbers.
The good news: there are some simple things you can do to curb spam and fake referral track.
The bad news: you probably can’t completely eliminate the problem.
One of the best descriptions of the bot & spam traffic problem is from Optimize Smart. Highly recommend you read it.
The best fix? Check your referral traffic sources whenever you notice a spike or a steady climb. Simply monitor it (or set a custom alert for significant spikes). Then weed out the clear spam URLs and hostnames that are either missing or fake.
You’ll then need to either block the spam referrers or filter them out. Again, read this article for full instructions on how to do so (it’s a bit long to describe in this article).
Something that also might help you out: a list of common spam referral sources. Incomplete, of course, but a good start and a good resource to reference check.
GA Mistake #22: Self-Referrals
Self-referrals are when you see your own domain show up in your referral reports.
Why do self-referrals occur? Could be for many reasons. According to Distilled, these are the main two:
- A session can be split in two when moving around your site (e.g. because the cookie was stored on blog.example.com and you moved to a shopping cart like payment.example.com).
- A session started on a page without tracking code–so a user moved from that page to one with tracking code, the only available referrer was your own site.
It can also be because you’re using UTM parameters on your own site to link to other site pages (don’t do that).
Realistically, if you’ve set up Universal Analytics through Google Tag Manager, you’re sure you’re tracking every page, you’ve correctly set up cross domain tracking, and you’re not using internal UTM parameters, you should be good to go.
Still, give it a check and if you’ve got self-referrals, do some debugging.
GA Mistake #23: Tracking PII
Google is very clear in their terms of service that you’re not allowed to collect Personally Identifiable Information (PII).
According to Google, PII includes, but is not limited to, information such as email addresses, personal mobile numbers, and social security numbers.
However, since laws in countries differ, this list could include other factors–so if you’re hesitant, check with Google/a legal consultant.
Anyway, it’s very common to accidentally collect things like email addresses, especially if you’re using User ID tracking. Stuff happens, and even if you didn’t plan on collecting or using this information, you can get in trouble if you’re doing so.
If you find any of this info. in your reports, make sure you remedy the issue ASAP.
GA Mistake #24: Accepting Your Out-of-the-Box Setup Forever
Sessions, pageviews, goal conversions. The basics are great. But there’s so much you can do with Google Analytics that accepting your basic setup is a real shortcoming, especially as you scale your data maturity in your organization.
For instance, consider for a moment how much you can do with Google Analytics measurement protocol. You can send hits to Google Analytics from any device or system that can be connected to the Internet. Yeah, you can go way beyond page views and bounce rates.
The use cases here are quite broad, ranging from gaming to in-person commerce transactions and more.
There are always cutting-edge cases of using Google Analytics if you’re following the right innovators (Simo Ahava & Analytics Pros are two great places to start). Some things you can look into to get greater data clarity and granularity:
Some crazy ideas to get you inspired:
In short: get good at the basics, but don’t think you have to stop there.
GA Mistake #25: Confusing Correlation with Causation
How many times have you heard something along these lines, “the campaign is working, our numbers are way up.”
Or, “users that use search convert 2 times better.”
These are interesting insights, and they’re certainly grounds for further exploration. But they’re not causal. You need to run a controlled experiment to attempt to find signs of causality. These are simple observations of “correlation.”
The extreme examples that illustrate the difference well come from Spurious Correlations:
Note correlational trends in your analysis, sure, but don’t confuse correlation with causation. Continue to investigate and run A/B tests.
GA Mistake #26: Confusing Taxonomy and Terminology
While a lot of analytics knowledge is based in the fundamentals (causation vs. correlation, mean, sampling, etc.), some of it is very platform specific. With Google Analytics, there are so many terms that are confusing to people and cause slight variations in how we interpret data.
For example, what’s the difference between a “visit” and a “visitor?”
In Google Analytics, a “visit” is a session and it has very clear parameters and a “visitor” is a user (which could be made up of multiple sessions).
These definitions matter, because they factor into how you view your reports and even how you calculate certain metrics (like conversion rate) as well as how you run attribution models.
This post is not long enough to cover all the Google Analytics jargon, and it largely comes with time spent in the tool. But you can read up more here.
GA Mistake #27: Poor Data Storytelling and Visualization
So, you need to trust your data collection setup to trust your analysis. But if you can’t tell a concrete and clear story with your analysis, how are you going to convince anyone else to take action?
It’s a huge problem in the analytics community. Those who spend the most time looking at the data (and thus understanding it) have the most trouble explaining to people less familiar with the data what the hell it means.
There are tons of examples of really bad data visualization (check this out)–but even if it’s not so extreme, most presentations I’ve seen include some pretty subpar data storytelling. Just as with conversion optimization, the goal is to make people less confusing. In other words, you want to lower cognitive load and increase the fluency of understanding.
This is an absolutely huge topic on its own, so I recommend you follow Lea Pica’s work on the topic.
GA Mistake #28: “Swimming in Data”
The way analytics tools, GA included, have evolved, it’s easy to simply jump in, look at some nice charts and think you’ve gotten some value out of the tool. While there’s nothing wrong with peeking at a well built dashboard periodically, you should really be approaching digital analytics by first asking important business questions and seeking to find the answers using data.
What do I mean by business questions? Some examples could be…
- I wonder if there are any differences in conversion by device type or browser (and if so, why)?
- What online behaviors do our top customers have in common?
- What demographics do our top customers have in common?
- Is there a specific leak in our marketing funnel where many customers are dropping out? Why?
- What’s really happening when a user churns from our product, and can we detect behavioral differences in the analytics?
- Can we use this segment in our marketing efforts to produce more effective, customized messaging?
These are just examples, and of course you want to ask questions specific to your business, but you get the picture.
Justin Rondeau, Director of Marketing at DigitalMarketer, recently railed against the ever-seductive process of “swimming in the data” — because by and large (and for most companies), it ends up being a time suck with no tangible results or improvements to the business.
Make your analytics adventures about asking impactful business questions and seeking action and improvement.
GA Mistake #29: Not Acting on Your Data
Sounds obvious: use your data.
Too often, we get into a state of analysis paralysis and spend too much time stewing on decisions when clear cut decision making is what’s necessary. Even worse (because it seems like it’s more valuable), we start “swimming in the data,” which a I previously mentioned, doesn’t necessarily guide important decisions (though it can in the right hands).
Make sure your data doesn’t lie dormant. Think of every implementation as a cost, or better yet an investment–but to reap the reward, you need to actually make use of your measurements.
GA Mistake #30: Not Continually Learning and Improving
One of the most exciting (and stressful) things about digital marketing is how fast things move. Sure, there are principles that remain the same–but even within those, the rabbit hole runs deep.
I think that’s even more extreme when you start jumping into data and analytics.
You can even break out data and analytics into separate categories that warrant their own jobs and career progression, from the analysis and insights side, to the implementation and technical side, to the management and strategy side. That’s not even including “elevated” levels of analytical capabilities that we’re calling data science (and moving further into artificial intelligence and applied machine learning).
There’s a lot to know.
Even within “simple” business intelligence and analytics, there’s the harder-than-it-appears task of connecting disparate data sources and making sense of pre- and post-purchase data.
If you’re passionate about business, data, and analytics, it’s easy to stay interested in all of this stuff. But sometimes, it may be tempting to throw up your hands and say you know enough.
Avoid that temptation. Keep learning and improving, and the world will certainly reward your curiosity and tenacity.
Final Thoughts on Google Analytics Mistakes
Google Analytics mistakes aren’t only for novices; we all make them. Some are more common than others, but any mistake can mess up your system and produce inaccurate or incomplete data. Of course, that’s not a good thing if you’re using your data to make business decisions.
There are best practices you can follow to avoid the common mistakes, though. You can breeze past the mess ups and start tracking like a champion.
These are 30 common mistakes, but obviously there are more out there. Any crazy mistakes you’ve seen in a Google Analytics set up? Tell your story in the comments section.