GOOGLE ANALYTICS TIPS · 7-MINUTE READ · By Paul Koks on February 27, 2017
Google Analytics is a fantastic tool, but in my experience more than 80% of the companies actively using it, are not getting everything out of it. Or even worse, are not able to increase their bottom line.
There are a lot of factors that determine how useful a tool is for your organization.
In this post I will address several issues with Google Analytics that can prevent you from uplifting your ROI.
1. No or The Wrong Questions are Asked
To keep things easy I would say there are two phases:
- Google Analytics implementation/configuration
- Google Analytics analysis
Your implementation and configuration should be directly in line with your measurement plan. What is important for your company to achieve and what do you need to measure in order to accomplish this? You should put in enough efforts to answer these questions first.
“You can’t optimize what you don’t measure.”
Finally you have set up your Google Analytics and gathered a month of data. Now it is time to dig through the data. Hey, but wait… Where are you going to look at?
“Your analysis can only be as good as your question asked.”
You can ask your boss or colleague to define a specific question, but if you have some experience you will be able to define some great questions by yourself as well!
If you belong to an agency – working for a wide range of clients – this is an article about the art of asking questions†you don’t want to miss reading.
2. Incomplete Measurement Plan
In probably four of the five Google Analytics setups that I have audited in the last month, I found that there was a lack of useful event tracking in place. This is just a simple example of a big gap in the measurement plan.
Not even fancy stuff like custom dimensions and metrics, but measuring simple interactions is neglected by many companies.
Let’s assume you are an online marketer or CRO specialist. What can you accomplish if you only measure pageviews and not all important interactions on the website?
These additional insights†deliver great value in understanding how your website functions and what your visitors are exactly using and asking for.
3. Inaccurate Configuration
In the first chapter I explained about asking the right questions. Sure, this is a great step, but if your configuration isn’t inaccurate or incomplete, these questions don’t make a lot of sense.
Here is a 10-point list of things that often go wrong:
- Setup that lacks multiple views (raw data view, master view, test view etc.).
- Goals that are defunct.
- Important actions that are not set up as a goal.
- Default channel grouping that is inaccurate. -> (Other) traffic bucket is greater than 10%.
- Incomplete setup of referral exclusion list.
- Ecommerce site with goal and ecommerce values configured.
- Non-ecommerce site without goal values.
- Content grouping incorrectly applied.
- Incomplete list of filters and/or filters that adversely affect the data.
- Site search query parameter is not stripped from the URL.
I could easily make a list of 100 items here. The point is that in many cases the configuration is inaccurate so you shouldn’t trust your data.
4. Domination of†Reporting Squirrels
Yes, sometimes you need to build reports, but if this is the main Analytics job in the company, you are doing things dead wrong.
Make sure to minimize reporting time and maximize on doing meaningful analysis and optimization.
A great start to reduce reporting time is to†pay a visit to Supermetrics’ Google Sheets Template Gallery.
5. Misinterpretation of Analytics Data
This is a big one as well. There are many different reasons why you could possibly draw the wrong conclusion based on your data. Even if you have collected the best possible data in the world.
Here are five†common ones:
- Your dataset isn’t large enough.
- Your analysis†didn’t account for behavioral differences between weekdays and weekends.
- Your dataset was heavily sampled without you knowing it.
- You found a correlation and interpreted it as a causal effect. Correlation and causation are two different things.
- You mixed up metric and dimension scope.
6. Not Testing Your Findings
Google Analytics is not your final destination.
Your data collection process and data evaluation might or might not support a hypothesis for (A/B) testing.
This is different from pure channel optimization. You still need to test, but not always by applying website changes. This could be as simple as changing your Google Ad title or bought keywords. Or investing more money in retargeting for example. But still you need to test here.
However, if you talk about website A/B testing, you need to test all your findings with multiple A/B tests to find additional proof that supports your hypothesis and data.
All too often changes are implemented without testing your data findings first. And you know what, sometimes it will even negatively impact your bottom line.
7. Being Caught in Google Analytics Silo
Google Analytics is good at a lot of things, but not good at everything.
Directly surveying your visitors – as an example – is very useful in addition to just collecting and acting on quantitative data.
It’s a great start if you correctly implement and configure Google Analytics, but make sure to take a broader approach in Analytics and Conversion Optimization.
8. Comparing Different Data Sources and Periods
Comparing different data types and sources can be valuable for sure, but make sure to know exactly at what data you are looking at.
Here are three†examples when things can go wrong:
1. Comparing Google AdWords clicks with Analytics sessions
Both metrics are useful, but they measure something totally different. A list of six reasons why this is the case:
- Single AdWords click which results in multiple sessions.
- More AdWords clicks in the same session.
- Invalid AdWords clicks that still result in multiple sessions.
- Cookie law implementation that results in less sessions than clicks.
- Improperly tagged Ad URLs.
- Website server doesn’t accept GCLID parameter.
2. Comparing on months instead of days
Let’s assume you compare March with February. March counts 31 days and February 28 (or 29).
This can make a huge difference if you compare on absolute metrics (10% more days in March compared to February). Make sure to choose equal data periods so that you don’t take the wrong decisions based on your data.
You could compare on a weekly or four-weekly basis for example.
3. Comparing without taking into account external events
Google Analytics annotations will help you to keep track of external events that might have a huge impact on your data.
If you make a month-to-month comparison, you need to know the context of your data in addition to the numbers.
This will enable you to make better data-driven decisions.
9. Not Telling Convincing Data Stories
Telling meaningful stories with data is what can make or break your Analytics efforts.
If you want to drive change, you have to go beyond simple numbers or Google Analytics screenshots.
Include stories, add names to each persona and even your HiPPo can be convinced to support your recommendations!
10. Not Visualizing Data
Visualizing data is something very closely related to my last point.
There are at least two reasons why you would want to visualize data:
- This makes it easier to find relationships between certain data points; it might reveal website or channel mix areas for improvement.
- It is one of the most powerful ways to†convey your message to any audience.
Make sure to read this article if you need some inspiration on data visualization tools!
This is it from my side! Any feedback is more than welcome!
About Paul Koks
Paul Koks is an Analytics Advocate at Online Metrics and a guest writer for Supermetrics. He is a contributor to industry leading blogs including Kissmetrics, SEMRush, Web Analytics World and Online Behavior and the author of Google Analytics Health Check. Paul helps companies to capture valuable insights from simple data. You can find him on Twitter or LinkedIn.