Transcript for: Google Analytics Reporting TroubleShooting
Hi, welcome back to our video series all about
reporting in Google Analytics. I'm Krista Seiden and I'm here today to help you troubleshoot
some common issues and questions you might see when looking at your analytics report. Quick reminder that this
video is actually part of a video series all about
reporting in Google Analytics. Together we've been looking
at how to find answers to your basic questions
about how people are finding and interacting with your website or app. You can find all of the previous
videos linked in the videos description on YouTube. Today is the final video
of this week long series, but we'll be back with more reporting videos for you in the future. Comment, what questions
came up for you this week, or which topics you'd most
like us to cover going forward? - So today we're answering
some reporting questions that you've already submitted and there are some clear common themes. We'll address your biggest
question about values like not set or other and tables, and we'll also talk about how
to approach trend oscillations and apparent data discrepancies. One caveat, the answers to
these questions will vary and be unique to your situation. So while we ultimately can't
guarantee a specific answer to your specific property,
we'll still be able to cover some common causes
and solutions to these issues. With that in mind, let's get started. First, let's end to the question,
how do I validate my data? Many of you want to know how to make sure your data is
flowing into Google Analytics properly and is working correctly. So there are a lot of
ways to go about this, but let's start with implementation. If you are the one implementing and using Google Tag Manager, you should use the Google Tag
Manager built-in preview mode to test your tags and ensure
that data is firing correctly. If you're not the one implementing or if you just wanna see that data is actually following
in on the Google Analytics side, a great resource
directly in analytics is the real-time report. Let's briefly look over
at the real-time report so I can show you what I mean. So the first thing to look for is that you actually have
traffic coming into your site. So here we see we do have
active traffic on our site, so that's a great sign,
data is flowing. And specifically,
if we wanna know if certain events are being collected,
we can use the event count by event name card. Maybe we've just implemented
a new promotion on our website and we wanna make sure that
that's actually showing. We can come to this View
promotion event, click in and then find the promotion name and look it up. So if we just launched the
calling all YouTube fans promotion, we can see that yes, this is showing people are
interacting with it in real time, which gives me a good sense
that it's working as I intended. Another thing you can use the
real time report for is to see that traffic is coming in
from different sources. So in this case, we are looking at users by first user source. We can change that if we
want, maybe source medium and see, okay, great, I've
got some direct non-traffic, Google Organic, Google CPC, and if you've just launched
a new marketing campaign with specific tags for source and medium, you would see those show up here if you're testing out those links. The next set of questions was all around undefined values and reports. So things like, not set and other. Let's address these one by one. So what does a not set event
or an empty name actually mean? Not set often means that there's a setup issue
preventing Google Analytics from receiving and reporting
information for a dimension. If you see a not set value,
this would be a great place to troubleshoot and
dig in to ensure that all of your events are
being properly tracked, all of your pages
properly tracked, and that your implementation
code is firing everywhere that you want it to be. The other common question we get here is what does the other row mean? The other row appears when the number of rows in a table exceeds
the table's row limit. Analytics will show the
most common dimension values in the table and
condense less common values under the other row. The table's row limit will
vary based on property type, individual report and complexity
of your query and data. So there isn't a hard and fast rule for avoiding the other row. That said, there are a few best practices that might help you to
reduce the likelihood of seeing the other row. A best practice here is to
not use high cardinality data, meaning that the data has hundreds or even thousands of assigned values unless absolutely necessary. So things like a user ID or a client ID could have
a lot of individual values. If you have a lot of
users coming to your site, in which case these could
cause high cardinality data. And along those same lines,
don't create a custom dimension to distinguish individual users. Instead, use the built-in user ID feature. And finally, use standard
reports when possible to help avoid the other row. We also get a lot of questions
about data discrepancies and trend oscillations that
you might see in your data. And while there isn't one hard and fast rule for what
might be causing this, there are a few things
that you can look at to help troubleshoot. So let's go ahead and look at that. If we move on over to
the report snapshot, here we'll see a graph
of our users over time. And specifically, we see
this one spike in our data. Now if you see a spike in your data, this could be an anomaly. Google Analytics has built an intelligence to detect anomalies, and in this case here in the
report snapshot, this tells us that while there was a spike
in users on this particular day, it expected to
see about 10,000 users, but actually it saw
closer to 20,000 users, which was a 95% increase
in the expected value. Digging in further, since we
know this is a spike in users, if we go over to acquisition and user acquisition, we can
try to dig into where the spike of users actually came from. So here we see that same spike and it looks like direct
traffic was a big part of it, the biggest part of that spike, and also some from organic search. One cool thing that you can do for most reports in Google
Analytics is actually open up the same report in explore. If we come over to this little button here that says edit comparisons
down at the bottom, you can click explore. This will open the same
report in explorer. Here we have our table report
and we also have a line chart. And on this line chart we
do see those same anomalies and this time broken out
by new users per channel. So direct we see there is
7,800 visits from direct and expected was 2100, so a 267% increase. This looks like the majority of our traffic came
from direct on this day. We can dig in farther by trying to understand if this
is legitimate traffic or potentially spam traffic. If I go back over to the table report, what I wanna do is add
a few dimensions here that can help me understand
the legitimacy of this traffic. Now there are a lot of
different dimensions that you might use to
help understand traffic such as country, browser type,
browser version, device type, operating system and more. And we'll just add a couple of those here to see if we can narrow it down. So I'll go ahead and add
some of those dimensions such as country device category, browser version, and operating system with version. And since I know a lot of
this traffic was from direct, I'm gonna go ahead and
include only this selection by right clicking this row. And now I'm gonna add
a couple of dimensions. So first I'm gonna start with
operating system with version. I can double click that
to add it to this table. And when I add this operating
system with version, it does look like I'm getting a lot of traffic from Macintosh
Intel 13.5 specifically. So perhaps some of this traffic
might not be legitimate. You can continue to dig in
using this method to try to narrow down the traffic
that you would expect and the traffic that you wouldn't expect. And then you can create a
segment of that traffic so that when you analyze
the rest of your data, you can make sure to exclude
that segment of traffic. Going back over to reporting,
we do often see lots of trend oscillations in data and it's also important to know if those are
expected or unexpected. So for example, maybe
your business does most of its sales during the week. You would probably expect to
see dips in your data over the weekend and that were
to reflect on the graph. So if you see a sudden
dip over the weekend, but it goes back up Monday through Friday, this might be expected behavior and not something to worry about. However, if you do see these
little bubbles on these graphs that call out an anomaly, that's an area where you might wanna dig in deeper. Okay, so today we covered
validating your data, interpreting report values,
addressing unexpected data spikes or dips, and comparing data. To go deeper on any of these topics, check out the resources linked
below this video on YouTube, including our help center and our analytics academy
courses on Skill shop. If you have other questions
about understanding analytics reports or where to find
certain answers, comment below. We may not get to answer every question, but your comments can help
inform future videos. In the meantime, bring your
troubleshooting questions to our Discord server, join the community and the conversation happening on Discord, and stay tuned to our YouTube channel for more reporting videos. Habby measuring.