Google Analytics is widely used. But most marketers only scratch the surface when it comes to reports.
You can find insights for conversion optimization from tons of reports—and the juicier reports are lesser known.
I asked some of my friends in the industry to share underutilized reports that they often turn to when looking for insights.
1. Advanced segments based on buyer personas
This report is built by taking the “site average pages per visit” and “site average time on site.”
We then create four segments:
- Methodical Marys visit the site for longer than the average user and view more pages than average. They take their time to make a decision.
- One-Hit Juans visit few pages but stay on the site for a long time. For sites with video, there may be a lot of One-Hit Juans.
- Lost Lucys hit a lot of pages in a short time. They seem to be looking for something and not finding it.
- Bouncy Bobs visit fewer pages than average and spend less time than average on the site. Bobs typically include the least qualified traffic.
Once we have the segments, we can examine their:
- Purchase rates;
- Page flows;
- Devices used.
It’s a way to start with segmentation as you begin to understand the visitors to your site.
2. Motion charts
One lesser-known report is really an aspect of many different reports—Motion Charts.
Motion Charts are great at visualizing where a specific change may have happened. Phrased differently, motion charts visually segment data much better than standard charting in Google Analytics.
(Note: You must have Flash enabled to use Motion Charts.)
7 Ways that Predictive Analytics Is Transforming Ecommerce
Predictive analytics help you understand what your customers are going to buy before they do.
The following example explores an ecommerce conversion rate over time segmented by browser type:
Right away, I noticed that there was a big spike for all browsers in mid-March. Using Advanced Filters, I can drill down from there to look at the performance on a browser-by-browser level:
If one browser lags behind the spike seen in others, you’re clued in right away to a potential cross-browser compatibility issue that is impacting the site’s conversion rate.
With that knowledge, you could go further and explore funnel abandonment and exit pages for the worst browser segments before and after mid-March. Diagnose the potential problem, then pass it back to developers to fix the issue.
3. Integrating split-test data with Google Analytics via custom variables
Getting insights from Google Analytics during your research phase is crucial to identifying optimization opportunities and developing solid test hypotheses.
But it’s just as important to stay on top of things while you’re running your A/B tests. Moreover, it’s vital that you’re able to get the full picture after you conclude your tests.
As Peep says, “averages lie,” and if you’re looking only at overall conversion rates in your testing tool, you’re not getting the full picture.
A number of factors impact the decisions and actions of your potential customers, so it’s risky to assume that they’ll display uniform behavior.
- Have they visited your website before?
- What was the traffic source?
- Did they find your website via a campaign?
- What day of the week did they visit your website?
- Did they use a laptop, tablet, or smartphone?
- What was the browser version?
If traffic from a particular source or device behaves significantly different than the rest, it’s enough to screw up your test data completely—and also what you’re taking away from the test, in terms of insight and revenue.
In cases like these, it’s crucial to identify the anomaly and take it into consideration before you draw conclusions about what to implement permanently on your website.
Some testing tools offer a degree of segmentation, but it’s nothing compared to the level of insight you can get via Google Analytics if you integrate the data from your test variants into your Google Analytics account.
By sending your test data to Google Analytics as custom variables, you can do in-depth with segmentation, run custom reports, and get the full insights that your analytics setup offers. This means that you get the full picture of how your test variants affect potential customers and their behavior on your website.
Here’s an example of a VWO experiment where the data has been collected under custom variables:
You can see the bounce rates, page visits, total revenue, and ecommerce conversion rates for individual test variants, offering deeper insight than you’d get by default from your test tool.
In this next example, we’re digging into Variation 1 to compare performance across devices. Notice that the bounce rate is significantly higher (and ecommerce conversion rate significantly lower) on tablets versus desktop or mobile.
And here’s an example that considers performance across different browsers:
If you use Optimizely or VWO, setting up your integration with Google Analytics is super simple. However, if you use a custom implementation of Google Analytics, you might run into some issues.
4. Site speed reports
I’m a performance junkie—because I started life as a network and security engineer with routers, TCP/IP, and lots of cool tools for analyzing and fixing problems.
I carried this into my life as a UX and CRO practitioner. Performance—and the perception of performance—plays a vital part in how people rate their experience. By fixing performance issues, you avoid sucking millions of hours of productivity from people’s lives as they wait for your stuff to load and make lots of money.
In the past, people used to use synthetic tools—bots placed on the Internet to measure your site. Now you have something much better. Real user performance monitoring of how long pages took to load and render. Where? In Google Analytics of course.
Go to Behavior > Site Speed > Page Timings. At the top of the report, click on the “DOM Timings” tab.
In the drop-down menus in the second and third columns of the report, you can now see three metrics:
- Avg. Document Interactive Time. This is the time it takes the browser to start handing control back to the user. This metric is really important because it’s when people can start navigating, clicking, and scrolling.
- Avg. Document Content Loaded Time. Depending on how your website builds pages, you may need to use this measure instead. Basically, this measurement refers to running scripts in the page. If you’re using stuff that blocks a lot of user interactions until all scripts load, this may be a better metric. I tend to look at both.
- Avg. Page Load Time. This metric—the “entire time” to load everything on your page—is less useful these days. It gets skewed by all the third-party tags, tracking stuff, and social buttons you might have on your site. It simply isn’t reliable for real performance metrics (IMHO), but it does tell you if you’re loading heaps of crap on slow connections.
By focusing on these metrics and creating a “Suck Index” report, you can start to chip away at things. Create the index simply by ranking each page with the formula Pageviews x aAvg. Document Interactive Time. That way, the slowest and most used pages will bubble up to the top.
Running performance reports around the pages that really suck in delivering a fast load time will do wonders for your bounce rate, user delight, and conversion rate. If you want to prove all this to the boss, the resource list below has everything you need.
Resources on performance and materials to convince your boss:
- The mother lode of tools and tips, from Google;
- A/B testing software Always Slows Down The Site;
- For desktop and tablet experiences, WebPageTest. (Note: Please use the timeline photo feature—it’s ace.)
5. Flow reports
Google Analytics has an inordinate number of reports, both standard and custom, that when considered as an abstraction of your customer behavior, they can be overwhelming to interpret.
In fact, many Analytics users don’t examine all reports because they think them either unnecessary, not worthwhile, or irrelevant to their business goals.
While that may or may not be true, there are several standard reports that are “lesser known”—and, thus, used less frequently, even though they can be helpful to the analyst.
These reports yield additional insights if understood properly and in context. On such example requires a bit of history to understand. In traditional “web analytics,” the notion of a “path”—the discrete series of clicks (i.e the clickstream) taken by a visitor—was disdained as not very useful years ago.
Even then, the nascent analytics industry criticized pathing reporting because constructs like identical links on the same page couldn’t be pathed accurately. Rich Internet experiences (where there was no pageview) were becoming a popular replacement to static sites, and pathing couldn’t capture these events.
Google Analytics addresses these concerns and more in “Flow” reports. While Analytics doesn’t name its “Flow” reporting as “pathing,” the concepts, presentation, and utility of it is reminiscent of traditional pathing.
Yet, Google Analytics recasting and evolution of “pathing” as “flow” has made this type of data representation and visualization more usable and helpful. As such, the following “Flow” reports make my cut for useful, lesser known reports, especially when combined with custom segments:
- Visitor flow is another representation of traditional web analytics pathing but with more features. This reports lets you see where visitors came from and their next and subsequent interactions. You can hover over a page for more information (and select to view the flow from a particular page). A nice feature is the ability to segment the flow by available dimensions and custom segments. This report is helpful for marketing analysts.
- Behavior flow is also a representation of where visitors started on the site and where they went next. The report is pre-configured to begin with landing pages (first page in visit) instead of acquisition sources (like visitor flow). Thus, it’s helpful for landing page optimization and to verify that your audience is viewing the best possible content.
- Goal flow is almost identical to visitor flow but contains the Goals you’ve configured as part of the conversion funnel. In this sense, Goal flow is similar to the funnel visualization report; however, it provides more information and enables deeper exploration. Hovering on a Goal also provides additional information (as in all flow reports).
- Event flow is represented like the Goal, behavior, and visitor flows but represents Events configured in Analytics to represent page-less events within an interaction. This report is particularly helpful when you want to see how people interact with a rich application that doesn’t generate pageviews. This type of flow is helpful for product managers.
Multichannel Funnel Analysis reports are also helpful—and lesser known. They have some complexity in concept and data interpretation, which I cover in another post.
6. Site search insights
The “Site Search” section in Google Analytics can identify major areas of opportunity. This can be useful for any site in any industry, as long as the site search is used often.
On the “Overview” section, select “Start Page.” You get the top 10 pages where searches occurred.
People who search for content or items on your site are more likely to consume that content or purchase a product if they can find it on Page 1 of results.
This simple report lets you know if you’re missing critical pieces of content on a page, especially if you notice trends.
To spot search trends, select “Search Terms,” which displays all the search terms that have been typed into your search box. Here, I like to do two things:
- Select “Start Page” as a secondary dimension to see the keyword next to the page that spawned the search:
- Select “Exit Page” as a secondary dimension. If they left on a search results page, I know they couldn’t find what they needed. On the other hand, I can see what pages people did end up navigating to if they didn’t exit.
If you get a ton of queries, I highly recommend making a custom report. Include the “Start Page,” “Exit Page,” and maybe even a “Conversion Goal.”
7. Content grouping
The built-in features for content grouping in Google Analytics are powerful and flexible. Even if I have to resort to using daisy-chained, hard-coded rules to get a structure that works, the effort is worth it.
The reason the effort pays off is because these functions now appear in so many places in the Google Analytics Behavior (content) reports.
Here’s an example of a “grouped” view when applied to a Navigation Summary report to illustrate just how useful grouping can be. The Navigation Summary used to be good for deep-dive forensics on a particular page, but with grouped content, it starts to make aggregated user-journey reporting more efficient.
This feature is built right in to standard reports, which is great if you’re more interested in what people do on a sites and not concerned only with getting more people to the site.
When you’re looking for insights in Google Analytics, be ready to put in the hours. Whenever I start to analyze a new site, I easily spend two full working days in their analytics.
The reports mentioned here will help you find more insights faster.