Google Analytics Mistake #1: Google Analytics tracking code not installed on all pages of your website
This is a very common issue and is more likely to happen if you are not using Google Tag Manager.
You need to make sure that all of your web pages have got Google Analytics Tracking Code installed. Otherwise, you will never see all of the data in your analytics reports.
Websites that do not use template files are more likely to have the tracking code missing from some or many web pages. In order to find web pages with missing tracking code, you would have to do the tag audit of your website.
Google Analytics Mistake #2: Not filtering out internal traffic
You must exclude internal traffic from your analytics reports.
Internal traffic is the traffic to your website which is generated by you, your employees (especially web designers and developers), suppliers, and other service providers.
These people are not your target audience and therefore you do not need to track them.
Internal traffic can easily and greatly skew your analytics data (esp. if you run a low traffic website) and therefore you should exclude it from your reports.
Without this information, it’s almost impossible to evaluate the effectiveness of your website and marketing campaigns.
There is no point in setting up a Google Analytics account if you do not have the desire to track goals or conversions in it. It is simply pointless to carry out any analysis without setting up conversion tracking.
Similarly, there is no point in tracking a goal conversion to which no goal value has been added.
A goal conversion without a goal value (or economic value) is known as a bogus conversion because it does not add any value to the business bottom line.
Google Analytics Mistake #4: Missing or incorrectly set up ecommerce tracking
If you are managing an ecommerce website then you have to set up ecommerce tracking in order to get ecommerce data (revenue, sales, average order value, transactions, etc.) into your analytics reports.
You cannot just depend upon the analytics reports provided by your shopping cart.
Without ecommerce tracking set up, you will never get a complete picture of the performance of your ecommerce website.
Such type of correlation analysis is required in order to understand the performance of your website landing pages and marketing campaigns. Otherwise, you may never know which landing pages or campaigns are driving sales and which are not.
Through ecommerce reports in Google Analytics, you can get detailed information about ecommerce activity on your website like total revenue generated by the website, the number of orders placed, average order value, ecommerce conversion rate etc.
Similarly, you need to make sure that the ecommerce data you are collecting is as accurate as technically possible.
Many optimisers start data analysis under the assumption that the ecommerce data they are analysing is 100% accurate. But this is often not the case.
Google Analytics Mistake #5: Missing or incorrectly set up enhanced ecommerce tracking
There are many businesses that still rely on standard ecommerce tracking even when the enhanced ecommerce tracking has been around for years.
Enhanced ecommerce is a complete revamp of the standard ecommerce tracking in the sense that it provides many more ways to collect and analyse ecommerce data. It is like ecommerce tracking on steroids.
There are many benefits of enhanced ecommerce tracking over traditional ecommerce tracking.
For example, Enhanced ecommerce provides twice as many ecommerce reports as traditional ecommerce.
But if you have installed enhanced ecommerce tracking on your website, then you will see the following 10 ecommerce reports in your Google Analytics view:
Ecommerce Overview
Shopping Behavior Analysis
Checkout Behavior Analysis
Product Performance
Sales Performance
Product List Performance
Internal Promotion
Order Coupon
Product Coupon
Affiliate Code
By installing enhanced ecommerce tracking, you can capture and analyse a lot more ecommerce data.
Enhanced ecommerce provides deeper insight into the ecommerce engagement of your users.
Ecommerce engagement is the user engagement in terms of:
Viewing your internal promotion campaign.
Clicking on internal promotion campaign.
Viewing your products in a product list.
Clicking one of the product links in the product list.
Viewing product detail page.
Adding/removing products from your shopping cart.
Starting, completing and/or abandoning the checkout process.
Asking for a refund.
For example, through the enhanced ecommerce Product Performance report you can track:
Total refund amount for each product.
Cart to detail rate (the rate at which users add products to the shopping cart after viewing the product details).
Buy to detail rate (the rate at which users buy products after viewing the product details).
Product list views – number of times a product appeared in a product list.
Product detail views – number of times users viewed the product detail page.
Product adds to cart – number of times a product was added to the shopping cart.
Product removes from cart – number of times a product was removed from the shopping cart.
Product checkouts – Number of times a product was included in the checkout process.
You can’t track such type of ecommerce engagement through standard ecommerce tracking.
Google Analytics Mistake #6: Missing or incorrectly set up cross-domain tracking
If your website checkout process occurs on a different domain (common in the case of affiliate websites) or your web session spans across multiple domains then you need to set up cross-domain tracking otherwise you will get muddy insights from your analytics reports.
Not having cross-domain tracking set up means you will not be able to understand and track a user journey that spans multiple domains or sub-domains.
For example,
Let us suppose a user landed on your website say abc.com via the search term ‘top men shoes’ on Google.
The user then made a purchase on another website say xyz.com/abc-cart (where both the shopping cart and the order confirmation page are hosted).
Now without cross-domain tracking set up, Google Analytics will treat the same user as two different users (one user visited abc.com and a different user visited xyz.com/abc-cart.
Without cross-domain tracking set up, the user session that actually spans two domains will be counted as two different sessions instead of a single session.
So the GA report of abc.com may tell you that a user visited abc.com via the search term ‘top men shoes‘ on google but didn’t make a purchase.
Whereas, the GA report of the website, xyz.com may tell you that a user visited xyz.com/abc-cart from abc.com and then made a purchase.
So without a cross-domain tracking setup, abc.com would end up getting all the credit for conversion instead of the search term ‘top men shoes‘ and google organic search traffic.
Another example,
Let us suppose a user landed on your website abc.com via the search term ‘top men shoes’ on Google.
The user went to checkout on another website, xyz.com/abc-cart (where the shopping cart is hosted but not the order confirmation page).
The user completed the purchase on your website abc.com, as the order confirmation page is hosted on your website.
Now Google Analytics may attribute the sales to xyz.com instead of the search term ‘top men shoes‘ and google organic search traffic. Thus,
Without cross-domain tracking set up, you may have a hard time determining the original source of your goal conversion and/or ecommerce transaction.
Google Analytics Mistake #7: Missing or incorrectly set up phone call tracking
If your website has been set up mainly to generate leads through phone calls (common for websites that sell high priced items like properties, cars, yachts, services, etc.) then you have to attribute phone calls to the correct traffic source.
That way you can prove the value you have added to the business bottom line through various marketing channels (SEO, PPC, email, social media, etc.) in monetary terms.
Google Analytics is not the best tool for phone call tracking. You need to use commercial call tracking software like CallTrackingMetrics.
Through call tracking software you can determine how many phone calls came from SEO, how many came from PPC, print ads, radio ads, billboard ads, TV ad campaigns, etc.
You can determine not only the volume of phone calls from each traffic source but also their quality in terms of generating sales.
Understanding exactly which marketing channels and keywords are driving phone calls is invaluable. Attributing phone calls to the correct marketing channels means you can increase the budget of the marketing channels which drive phone calls and reduce the budget of those that don’t.
Google Analytics Mistake #8: Not fixing data sampling issues
Google Analytics selects only a subset of data (called a sample) from your website traffic to produce reports. This process is known as data sampling.
As long as the sample is a good representative of all of the data, analysing a subset of data (or sample) gives similar results to analysing all of the data.
But in the case of high traffic websites (more than half a million page views each month), the selected sample may no longer remain a good representative of all of the data.
This produces inaccuracy in your reports and results in data sampling issues.
When an Analytics tool like ‘Google Analytics’ is sampling your data badly, you cannot rely on the metrics reported by it. Any marketing decisions based on such reports could also result in a huge monetary loss.
You can minimise/eliminate data sampling issues by using Google Analytics 360 or Matomo. Tools like Analytics Canvas can also help you reduce or eliminate data sampling programmatically by using query partitioning.
In query partitioning, a user’s query is broken into multiple queries in such a way that each individual query does not trigger data sampling.
Google Analytics Mistake #9: Not using Google Tag Manager
There is still a large number of businesses that do not use any tag management solution(TMS) like Google Tag Manager. They still hard code each and every tag on their website.
Google Tag Manager or GTM is a free tag management solution provided by Google.
Following are the key benefits of using Google Tag Manager:
Google Tag Manager removes the need for editing the website code over and over again.
Through Google Tag Manager you can test and deploy tags very fast.
Google Tag Manager makes advanced analytics tracking possible.
Google Tag Manager makes tag management very efficient.
By using Google Tag Manager, you can improve website speed.
When you use GTM to deploy tags, you are supposed to remove the corresponding hardcoded tags from your website. Failing to do so can result in double-tracking.
For example, if you are deploying the Google Analytics pageview tag via GTM then you should remove the hardcoded Google Analytics tracking code from all the pages of your website.
Otherwise, your Google Analytics tracking code will fire twice on your website: one via Google Tag Manager and one via the hardcoded tag on your website.
Following are the key benefits of using Universal Analytics:
#1 Universal Analytics provides many more ways to collect and integrate different types of data than the classic Google Analytics.
#2 Through Universal Analytics you can integrate data across multiple devices and platforms. This is something that is not possible with classic Google Analytics. Consequently, Universal Analytics provides a better understanding of the relationship between online and offline marketing channels that drive conversions than classic Google Analytics.
#3 In Universal Analytics you can create and use your own dimensions and metrics to collect the type of data that the classic Google Analytics does not automatically collect (like phone call data, CRM data, etc).
Adding their own lines of codes between the Google Analytics tracking code.
Now if something goes wrong with the plugin itself or you customised the Google Analytics tracking code in such a way that the plugin no longer communicates with the GA JavaScript then your tracking may stop working.
Your testing could become really hard if you are not familiar with the plugin code and how it is supposed to work with Google Analytics.
Unless you are a ‘ninja’ or ‘guru’ of the Google Analytics development environment stay away from using third-party GA plugins.
Unless you know exactly what you are doing and how it can affect existing website tracking don’t use a GA plugin. Unless you can decode any plugin, your best bet is to stay away from such third-party plugins.
Stick to the standard installation of Google Analytics.
Google Analytics Mistake #15: Using a non-standard implementation of Google Tag Manager
Add one part of the container tag code (the JavaScript part) in the <head>…</head> section of a web page and the other part (the iframe part) in the body section of a web page (immediately after the opening <body> tag:
When you deploy the GTM container tag code in any other way, your Google Analytics set-up may no longer remain a standard implementation.
Following are examples of non-standard implementation of Google Tag Manager:
All of the GTM container tag code added immediately after the opening <body> tag.
GTM container tag code added immediately before the closing </body> tag.
The container code deployed via an external JavaScript file.
GTM container code deployed via another tag management solution
Use of multiple GTM container codes on the same web page.
Use of a third party plugin to install Google Tag Manager on a website.
Now I am not saying that you can not make the non-standard implementation of GA/GTM work for you.
But remember, when you have got a non-standard setup (the one which is not recommended by Google) you could end up creating hard to diagnose tracking issues.
Often a non-standard tracking setup behaves in an unexpected way and if you are not familiar with the GA/GTM development environment then you could make your testing and debugging unnecessarily difficult.
Google Analytics Mistake #16: No goal funnel set up
In order to make your business a success, you should be spending more time and resources in converting existing traffic than in acquiring more traffic.
When you work with the mindset of increasing sales by just sending more traffic to your website, your cost per acquisition tends to be high and your revenue per acquisition tends to be low.
So you may eventually end up making less profit and sometimes even loss.
The best way of converting existing traffic into sales is by mapping the entire conversion process from lead generation ads to post-sales follow up and then looking for the biggest drop-offs from one step to the next.
You do that mapping in Google Analytics through Funnel Visualization reports:
Use this report, to determine the biggest drop-offs from one step of the funnel to the next. These drop-offs can help in explaining which part of the website/ conversion process needs urgent attention.
However, in order to get data in your funnel visualization reports, you would first need to set up funnels in GA.
In Google Analytics, a funnel is a navigation path (series of web pages) which you expect your visitors to follow to achieve website goals.
Through funnels, you can determine where visitors enter and exit the conversion/sales process. You can then determine and eliminate bottlenecks in your conversion process in order to improve the website conversion rate.
Often marketers who attempt to install ecommerce tracking have developed a false belief that they can set up all type of tracking by themselves through GTM.
That they can somehow become independent from the IT/Web developer. They sometimes overestimate their abilities.
Even when you have got adequate knowledge of HTML, DOM and JavaScript, you would still need the help of the client’s web developers/IT.
Without adding server-side code to GTM data layers, you can not implement many of the sophisticated trackings like ‘enhanced ecommerce tracking’.
The best practice is, to always involve your web developer (no matter how confident you feel about your tags setup) during tag planning and deployment, as they understand their development environment better than you ever will.
Google Analytics Mistake #18: Being unaware of duplicate orders/transactions
Duplicate orders/transactions can take place when the order confirmation page (receipt page) can be loaded more than once, by the same user without placing any new order.
With each new page load, the ecommerce data is resent to the GA server.
Within a session, Google Analytics will filter out duplicate transactions.
But if a user comes back later in a different session and revisits the order confirmation page then the transaction data could be sent again to the GA server thus creating duplicate orders.
These duplicate transactions will then show up in your report and inflate your sales data.
The majority of website owners are unaware of this issue. They come to know about this issue, only when they ask an expert to audit their GA/GTM account.
Download this custom report in your GA view and then set the time period of the custom report to the last month.
Now, look at the transactions column. They should all be 1. If you find any value greater than 1, then you have got duplicate transactions issue:
These duplicate transactions could be inflating your sales data and skewing your ecommerce reports. This is what makes this tracking issue so deadly and impossible to ignore.
Google Analytics Mistake #20: Not using custom alerts
It is extremely difficult to manually keep an eye on significant variations in your website traffic or any of your marketing campaigns.
It is not possible to keep track of everything and that too 24 hours a day and 7 days a week. Here is where Google Analytics custom alerts come in handy.
Through Google Analytics custom alerts you can monitor significant variation in your website traffic and marketing campaigns. Whenever such variation occurs you can get an email alert or text message from Google which asks you to take immediate actions.
Custom alerts are generated when traffic reaches a specific threshold that you have specified.
For example, if your website traffic dropped by more than 90% in comparison to the last day, then you can get an alert via email from Google.
Without setting up such a custom alert, you may never know when your website tracking stopped working.
Similarly, if your website sales dropped by more than 90% in comparison to the last day, then most probably either your shopping cart or ecommerce tracking has broken.
You may never know about this issue on time if you are not using custom alerts.
Google Analytics Mistake #21: Not using Google Analytics Intelligence
Analytics Intelligence (AI) is a machine learning algorithm used by Google Analytics which makes it easier to drill down data in GA and quickly get the insight you want:
You can ask any question about your data in plain English (natural language) and the GA machine learning algorithm will try to answer your question.
For example, you can ask Analytics Intelligence (or AI): “How many users did we have last week” and it will try to answer your question.
The AI panel in GA not only lets you ask questions but also displays insight.
In order to generate this insight, the AI regularly scan your Google Analytics data and search for outliers in the time series data.
These outliers are major changes in data trend which can positively or negatively impact your business.
The outliers which can positively impact your business are called ‘opportunities’.
For example,
If AI tells you that your website performs above average on the screen resolution of 1366×768 then you can create an advanced segment ‘with sessions that include: Screen Resolution: 1366×768’ to determine the cause and how this performance can be replicated for all other screen resolutions:
The outliers which can negatively impact your business are called ‘anomalies’.
For example,
If AI tells you that in India, your website has an average page load time of 11.5 seconds and this is slow compared to other top countries then you need to decide whether India is an important market for you.
If it is then you need to ask your developer to decrease page load time further so that the website pages can load faster on slower internet connections:
If you do not use analytics intelligence then you will miss out on all the opportunities and anomalies detected by GA AI.
Google Analytics Mistake #22: Installing ecommerce tracking via a plugin
Many website owners (generally WordPress users) use a plugin to install ecommerce or enhanced ecommerce tracking on their website.
Now if anything goes wrong with the ecommerce tracking (which often do) there is nothing much you can do. That is because you can not edit the plugin code.
The plugin author is not going to change his plugin functionality just to accommodate your specific needs.
Your only option is to wait for the next plugin update and hope it fixes your problem.
Other than that, if you want any type of customisation in your ecommerce reporting then that is not possible via a third-party plugin.
Google Analytics Mistake #23: Ignoring Google Analytics Diagnostic notifications
Google diagnostic is a feature of Google Analytics that makes regular evaluation of your Google Analytics tracking code, account configuration, and data in order to find implementation issues and configuration anomalies.
Once it finds issues, it alerts the GA user through a special message known as a diagnostic notification (also known as ‘Analytics Notifications‘).
These notifications appear as a number over the notification bell in your Google Analytics (GA) view:
These notifications highlight the issues you need to focus on and fix. If you don’t do that then it can negatively impact your website tracking and skew your analytics data.
Google Analytics Mistake #24: Not fixing PayPal self-referral issues
Many businesses use PayPal and other third party payment gateways to accept online payment. But this can create tracking issues in Google Analytics.
A payment gateway is a service through which you can accept credit/debit cards and other forms of electronic payments on your website. PayPal is an example of a payment gateway.
Whenever a customer leaves your website to make payment via a third party payment gateway and later return to your website from the gateway website, Google Analytics often attribute sales to the payment gateway instead to the original traffic source.
This is quite common in the case of PayPal. You can often find PayPal.com being attributed to a lot of sales in Google Analytics:
But PayPal is not a traffic source but a payment gateway, so it can’t generate sales on its own.
This issue skews your sales data and makes it is impossible to determine the real traffic source of your sales.
Google Analytics Mistake #25: Not keeping unfiltered view
An unfiltered view is the one to which no Google Analytics filter has been applied.
You should create and maintain one unfiltered view.
While filters help a lot in segmenting and analyzing the data, they can result in data loss if applied incorrectly. Therefore you should always create and maintain at least one unfiltered view.
Google Analytics Mistake #26: No or incorrect event tracking
In the context of Google Analytics, an event is the user’s interaction/activity with a web page element that is being tracked in Google Analytics.
By default, Google Analytics can not track any event which does not generate pageview when it occurs like: clicking on an external link, viewing a video, downloading a file, scrolling a web page etc.
You can track/capture the various player states of an embedded video on a web page only via video tracking.
Through video tracking, you can determine whether people are actually watching any video on your website and if yes then how much or how little.
In this way, you can determine the effectiveness of your videos in influencing the buying behaviour of your prospective clients.
Scroll tracking is an effective way of measuring how people are consuming your website content.
People who actually read your article or other content on the landing page are most likely to scroll the page and by measuring the percentage of the scroll, you can get a good idea of content consumption.
So if the majority of people do not scroll to the bottom of your articles then something may be wrong with your contents or landing page design.
Articles related to event tracking in Google Analytics:
The keyword referral data tells you which search term was used by a person to visit your website.
For example, if someone visits your website by typing ‘new York city car hire’ on Google, then the keyword referral data is ‘new York city car hire’.
Similarly, if someone visits your website by typing ‘valentine day cards’’ on Google, then the keyword referral data is ‘valentine day cards’.
There are two types of keywords referral data: organic keywords referral data and paid keywords referral data.
Google has been hiding the ‘organic keyword referral data’ since October 2011 by encrypting its organic search data. Google does not hide the ‘paid keyword referral data’.
All web analytics tools (including Google Analytics) can not report on ‘organic keyword referral data’ from Google search engines in their reports.
Google Analytics report ‘not provided’ in place of actual keywords in your organic search traffic reports.
By using the ‘Keyword Hero’ tool you can get most (but not all) of these not provided keywords back in Google Analytics.
Keyword Hero pulls the search data from Google Search Console and then match it with the GA data using some machine learning algorithm.
Following are the advantages of using this tool:
#1 You can see organic keywords data and correlate this data with sales and other conversions in your Google Analytics reports. This will help you in better understanding the performance of your organic search campaigns.
#2 By using organic keywords data you can optimize your landing pages for the keywords which are most likely to result in traffic, sales, leads or some other conversions.
#3 You can develop more content around the organic keywords that have already proven to generate traffic and conversions for your website.
#4 You can once again understand the performance of your branded organic keywords in terms of generating traffic and conversions.
#5 You can once again compare the performance of branded and non branded keywords with each other.
#6 Once you understand the keywords for which your website is ranking really well on Google, you can stop bidding on them in Google Adwords and can greatly reduce your ad spend.
#7 You can discover new keywords for your paid search campaigns. So if an organic keyword is performing really well for your business but your website is not ranking very high for it, you can target that keyword through your paid search ad campaigns.
#8 You can see the search engine ranking position (SERP) of your website on Google for each keyword. This can help you in improving your SERP for profitable keywords and increase sales through organic search.
#9 By using the ‘keyword hero’ tool, you get a competitive advantage as a marketer/advertiser. I mean how many marketers know that they can get back organic keywords data back in Google Analytics? Only a handful, like you and me. The majority think that organic keywords referral data is gone for good.
#10 Keyword hero provides some ready to download keywords dashboards
#11 Keyword hero automatically emails weekly SEO performance reports for your website which includes:
Google Analytics Mistake #28: Not using Custom Channels groups
A channel group is a rule-based grouping of marketing channels.
Custom channels groups are created for two main reasons:
To change the way Google Analytics label and aggregate the incoming traffic for advanced data analysis.
To quickly check the performance of a set of marketing channels or set of traffic sources.
Google Analytics can report the performance of a marketing channel via several traffic sources.
For example, Google Analytics can report traffic from Facebook as:
So if you are not very careful, you may just take the traffic from facebook.com / referral into account while interpreting reports and can draw the conclusion that Facebook sent 965 visits to the website in the last 1 month.
When in fact, Facebook sent 1,009 (965 + 19 + 13 + 10 + 1 +1) visits to the website in the last 1 month.
So if you are taking only facebook.com / referral traffic into account while trying to understand Facebook performance as a marketing channel, you will draw wrong conclusions, you will misinterpret the data.
The traffic from all of these traffic sources is basically Facebook traffic. But Google Analytics is not going to consolidate all of this data and report it to you as Facebook traffic.
This is something you would need to do. So you would need to identify all the traffic sources which belong to Facebook.
Then you need to consolidate the data from different traffic sources into one custom Facebook channel:
Similarly, Google Analytics can report traffic from Google Adwords as:
So if you are not very careful, you may just take the traffic from google /cpc into account while interpreting reports and can draw the conclusion that Google Adwords sent 430,635 visits to the website in the last 1 month.
When in fact, Google Adwords sent 571,060 (430,635 + 133,147 + 7,278) visits to the website in the last 1 month.
Google Analytics is not going to consolidate the traffic data from google / cpc, google / ppc and google / CPC and report it to you as Adwords traffic.
This is something you would need to do. So you would need to identify all the traffic sources which are basically Google Adwords Traffic.
Then you need to consolidate the data from different traffic sources into one custom Google Adwords channel:
That’s how through custom channel groups, you can better understand the performance of various marketing channels.
Google Analytics Mistake #29: Not using a custom campaign
In the context of Google Analytics, a custom campaign is your website URL which contains UTM parameters. Through custom campaigns, you can send detailed information about your marketing campaigns to Google Analytics.
For example,
If you are running various ad campaigns on Facebook, you by default cannot evaluate the performance of each individual Facebook campaign in Google Analytics.
In order to track the performance of each individual Facebook ad campaign in Google Analytics, you would need to add various UTM parameters at the end of the destination URL of each Facebook ad:
Following is an example of a Facebook ad URL that contains UTM parameters (highlighted in bold text):
These UTM parameters have the power to overwrite the original referrer and send that information to GA which cannot be sent otherwise.
Google Analytics treats any traffic that is not direct as referral traffic. So if you are getting traffic from email campaigns, display ads, PPC ads, affiliate marketing etc then they all will be treated as referral traffic.
By default Google Analytics only provide ‘source’ and ‘medium’ information of the referral traffic.
If you want Google Analytics to provide more information about your marketing campaigns that you need to add campaign tracking variables at the end of each destination URLs of your ads.
Note: The process of adding the campaign variables to the end of the destination URL of an ad is known as ‘tagging’. You can tag your ad URLs through Google Campaign URL builder.
Google Analytics Mistake #30: Tagging internal links
An internal link is a URL which when clicked, takes a user from one web page to another web page and both the source and destination web pages are hosted on the same website/primary domain.
For example, a link from a product category page (hosted on your website) to a product detail page (also hosted on your website) is an internal link.
Similarly, a link from one of the web pages of your sub-domain (say blog.abc.com) to a page hosted on your primary domain (abc.com) is an internal link.
On the contrary, an external link is a URL that when clicked, takes a user from one web page to another web page and both the source and destination web pages are hosted on different websites/primary domains.
For example, a link from a Facebook ad to a product detail page hosted on your website is an external link.
Each Google Analytics session can be attributed to only one traffic source (whether system-defined or user-defined) at a time.
So if the value of traffic source changes in the middle of an existing Google Analytics session, it causes the current GA session to end and a new session to start.
Similarly, any change in the value of the following keys triggers a new Google Analytics session:
utm_source
utm_medium
utm_campaign
utm_term
utm_content
gclid
Because of this reason, when you tag an internal link, it could trigger new GA sessions and thus inflate your session count. In short, use UTM parameters to tag only external links.
Google Analytics Mistake #31: Not using test property
In the context of GA, property represents a website or a mobile app. So if you have got one website, you are most likely to use only one GA property.
On the other hand, if you have got two websites then you are going to use two GA properties. Each GA property can be made up of one or more views.
Whenever you change the settings of your live GA property, you change the way your data is collected, processed and reported by Google Analytics.
Following are the various methods through which you can change the settings of your live GA property:
By adding, removing, editing view filters.
By adding, removing, editing goals
By adding/removing products (like Google Adwords, Adsense, Search Console etc)
Every change you made to your GA property setting(s) has the potential to inflate/skew your current analytics data.
The majority of optimizers directly make changes to their ‘live GA property’ before testing them on a different property. Let us call this different property ‘test GA property’ for easy reference.
Let us suppose you implemented new custom dimensions. Now if your custom dimension setup is not correct, you will have to make changes to it.
But while you are making changes, to get your custom dimension set up right, you are also unknowingly skewing your analytics data in the background.
Even if you are using a ‘test view’ (a GA view set up just for testing purposes), you are still skewing your analytics data because custom dimensions are set at the property level and not at the view level.
So using a ‘test view’ is not good enough. You need to use ‘test property’.
Google Analytics Mistake #32: Using too many view filters
Avoid applying too many filters on the same GA view:
This can create serious data sampling issues.
Filters can easily skew your analytics data if you are not very careful. Use custom segments and reporting interface filters wherever possible or create several different filtered views.
Google Analytics Mistake #33: Not excluding query parameters
Exclude query parameters from your view reports.
A query parameter (like the session ID, visitor ID etc) is what appears after the question mark (?) in a URL.
For example in the URL: https://www.abc.com/?sid=234&hn=1 the query parameter is ‘sid=234&hn=1’.
Google Analytics consider one URL with two different query parameters as two different web pages.
For example, the following URLs are considered two different web pages by Google Analytics:
If the query parameter is not changing the content/functionality of a web page then you should exclude it from your Google Analytics reports.
You can do this via your view setting in the Admin panel:
Google Analytics Mistake #34: Mixing up GA tracking code / Property ID
Webmasters/marketers who manage multiple Google Analytics accounts, sometimes accidentally add the Google Analytics Tracking Code (GATC) of a different website. This can very easily skew your analytics data.
You should always double-check that you are using the GATC which is specially meant for your website.
Likewise, if you are using GTM then always double-check that you are using the property ID which is specially meant for your website.
Google Analytics Mistake #35: Not using roll-up reporting
If your company run several websites, sub-domains and/or mobile apps to promote various brands/regional business units and you want to understand the overall performance of your company and also compare the performance of individual brands/ business units to each other then you need to set up rollup reporting in your Google Analytics account.
Roll-up reporting is simply the reporting of data in an aggregated form from multiple digital properties (websites, mobile apps).
For example,
If you have set up separate websites for each country (abc.com, abc.co.uk, abc.com.au,abc.in etc) then through rollup reporting you can aggregate all of your website’s data in one view and see aggregated global performance metrics and/or compare the performance of various country-specific websites to each other.
Rollup reporting helps you in understanding the overall performance of all of your company’s websites and/or mobile apps.
Through roll-up reporting, you can see the total unique visitor reach of all of your websites and/or apps.
In other words, you can determine the total number of unique people you are reaching through your websites, apps and/or marketing campaigns.
Google Analytics Mistake #36: Not using annotations
In order to conduct a very focused and meaningful analysis, you should maintain records of all the changes that significantly affect your data every single day.
These records will help you greatly in interpreting the various spikes in your data trends even months from today.
You would no longer need to remember what event triggered an anomaly and when. Everything is stored at one centralized location in GA.
Annotation is also a great way to create a baseline for measuring website performance in terms of traffic, sales and other conversions.
Google Analytics Mistake #37: Not using Google Analytics API and other automation available
If you manage dozens of Google Analytics accounts and views then you should definitely use the Google Analytics APIs for fast information retrieval.
Otherwise, you will be spending the majority of your time creating and downloading reports instead of doing analysis.
Any tool which helps you in automating reporting, setting up roll up reporting or helps you in automatically importing, exporting GA data or eliminating data sampling issues is worth considering.
Almost all popular shopping carts (like Shopify), provide a mechanism to handle:
cancelled orders
unfulfilled orders
test orders
promotions (promo codes, discounts) and
refund (partial or full).
They then adjust the sales data accordingly to reflect the changes. This is not the case with Google Analytics.
Once a user is served an order confirmation page, a transaction and corresponding sales are recorded by GA.
If the user later cancels the order, demand for refund or the order is not fulfilled for some reason (maybe the credit card was declined) then these changes do not automatically reflect back in GA ecommerce reports.
Similarly, it is common for web developers to place test orders on websites while testing an application/ functionality.
While many developers, eventually delete the test orders from the Shopping cart, they are still recorded and reported by GA.
Test orders can greatly inflate your revenue metrics and skew the entire ecommerce data.
So before you trust your sales data in GA, it is very important that you identify and deduct test orders from your analysis.
Before you trust your sales data in GA, match it with the sales data in your shopping cart. The data is unlikely to match.
But there should not be a large mismatch between GA sales data and shopping cart sales data. Otherwise, that could mean that your GA ecommerce tracking is not working correctly.
Whenever there is a trade-off between GA and shopping cart sales data, trust the shopping cart data.
I have audited hundreds of web analytics accounts. Each account had at least one or two issues that seriously stood in my way of getting optimum results from my analysis. I have put all of these issues into five broad categories:
Directional Issues
Data Collection Issues
Data Integration issues
Data Interpretation Issues
Data Reporting Issues
These are the most common mistakes that kill your analysis, reporting, and conversions.
In order to get optimum results from your analysis of Google Analytics reports you must aim to find and fix as many of these issues as possible. Failing to do so will almost always result in inaccurate analysis, interpretation, and reporting.
1. Directional Issues
These issues are not associated with Google Analytics or any other analytics software you use but are commonly found in analysts themselves and are reflected in the way they set up Google Analytics account, custom segment, conversions segments, filters, and custom reports.
A directional issue is the inability to move in the right direction and at the right time.
It is the inability to determine:
What data needs to be collected and when
What to look at
What should be overlooked and
Where to look at in any analytics reports.
Just because you have got data, does not automatically mean that you should go ahead and analyze it.
The cornerstone of every successful analysis is “moving in the right direction”.
The direction in which your analysis will move will determine the direction in which your marketing campaigns and eventually your company will move to get the highest possible return on investment.
In order to get the right direction, you have to acquire a great understanding of your client’s business, industry, target market, competition, and business objectives.
If you do not have that great understanding before you start analyzing and interpreting analytics reports, you, my friend, are already moving in the wrong direction.
This is the direction that will almost always make you return sub-optimal results for your company.
For example, let’s say your business objective is to reduce the acquisition cost.
Let’s say, you have got 1 million products on your ecommerce store and you sell in 7 countries. Now where you should start?
What should you change on the website? Which key issues you should focus on that can quickly improve your sales and conversions?
The answer is not to first dive into your Google Analytics reports. The answer is to first develop a great understanding of your client’s business and its objectives.
This may take you several weeks or even months. But the payoffs are worth gold.
Any person can learn to use Google Analytics in a few weeks. It is not that hard. But what separates an average analyst from a great analyst is the understanding of the business and its objectives.
With average understanding, you will get average results. With great understanding, you will get great results. It is as simple as that.
Following are a few tips which can help you in getting the right direction for your analytics project:
Determine where you are now.
Identify the problems that need to be addressed first.
Determine the requirements to solve each problem.
Determine the possible barriers to your proposed solutions.
2. You will take cost into account while coming up with a proposal. ‘Cost’ is something which we are generally not bothered about, as a marketer/analyst. No matter how big your company is, no organization has got an unlimited marketing budget and therefore you should not ignore the cost of implementing your recommendations.
3. You will get a good understanding of all possible conversion paths that should be tracked in your analytics reports.
4. You will get a great understanding of all possible macro and micro conversions that should be tracked.
5. You will know exactly what data to collect and where to find it.
6. The biggest benefit that you will get from ‘great understanding’ is that you will know the ‘context’ in which you should analyze and interpret the analytics data. Your probability of accurately interpreting the analytics data will be 100 times better if you know the context beforehand.
Once you have translated your business objectives into measurable goals, you then need to find KPIs to measure the performance of each of these goals.
We have already discussed the most common data collections issues which must be identified and fixed as soon as possible.
3. Data Integration Issues
Data integration is one of the most challenging and difficult issues to resolve esp. for small and medium-sized businesses, as data integration solutions are usually quite expensive.
In the world of Web Analytics 2.0 we rely on several data sources (from Google Analytics, Matamo, Piwik, Kissmetrics, Qualaroo, Facebook Insight, Survey Monkey, phone call data, call centre data to internal tools like CRM etc) to get a complete picture of our marketing campaigns.
But jumping between different analytics tools to get complete insight is time-consuming and is not very practical.
You need to correlate all of your data with business bottomline impacting metrics like revenue, cost, gross profit etc in order to get true insight and in order to do attribution modelling.
In fact, if you are a big organization then it is completely pointless to collect and analyze big data without proper integration.
You need all of the marketing and business data in one place so that you can quickly track various aspects of your marketing campaigns, analyze the overall performance and take timely decisions.
Data integration issues can very easily kill your data analysis and attribution modelling.
Different people interpret the same data differently. It all depends upon the context in which they analyze and interpret the data. If you have a better understanding of the context, your interpretation is going to be more accurate.
That takes us back to resolving ‘Directional Issues’.
If you really want to be good in data interpretation, you must develop an average….good“great” understanding of your business, its objectives and the problem you are trying to solve.
Following are some of the most common data interpretation issues:
1. Not segmenting the data before analyzing it. Data segmentation is the key to accurate interpretation.
2. Poor understanding of the Google Analytics terminology. For example, if you are not sure what “Bounce Rate” is, then how on earth, you can interpret it correctly?
3. Selecting the wrong KPIs to measure the performance of your goals.
4. Relying on a small time frame to make future predictions about marketing campaigns.
5. Relying on a small data set for analysis and interpretation.
11. Too much focus on raw numbers instead of data trends.
12. Attributing conversions/ transactions to wrong marketing channels. This issue alone can break your entire analysis. Therefore you must acquire a great understanding of attribution modelling.
15. Not understanding ‘why’ people do what they do on your website. For example,
why people don’t buy on my website?
why do people buy on my website?
why people don’t share my contents?
The answer to this ‘why’ is not available in your analytics reports. You need to ask questions from your client, conduct customer surveys and do A/B testing, to get these answers.
16. Not using custom reports.
17. Not understanding how and from where the data is collected. For example, if your target market is the UK and the data is collected from compete.com then it is not very useful.
18. Not tracking the various changes that affect your data. Changes in Google Analytics view (adding/removing filters, goals), seasonality, changes in the economy, market conditions etc all affect your data.
If you do not keep a record of these changes (via Google Analytics Annotation, ‘change history’ and through excel spreadsheet) then how you will explain/interpret the various spikes in your data trends, weeks and months from now?
The following articles can help in honing your data interpretation skills:
What separates one analyst from the other is actually the interpretation of analytics data and how quickly he/she can find useful actionable insight from it and/or label the data as useless and move on.
5. Data Reporting Issues
Data reporting is another challenge on its own.
You need to make sure that recipients of your reports interpret the data in the same way you want them to interpret it. If they interpret your reports incorrectly then they may take the wrong business decisions.
Get weekly practical tips on GA4 and/or BigQuery to accurately track and read your analytics data.
My best selling books on Digital Analytics and Conversion Optimization
Maths and Stats for Web Analytics and Conversion Optimization
This expert guide will teach you how to leverage the knowledge of maths and statistics in order to accurately interpret data and take actions, which can quickly improve the bottom-line of your online business.
Master the Essentials of Email Marketing Analytics
This book focuses solely on the ‘analytics’ that power your email marketing optimization program and will help you dramatically reduce your cost per acquisition and increase marketing ROI by tracking the performance of the various KPIs and metrics used for email marketing.
Attribution Modelling in Google Analytics and BeyondSECOND EDITION OUT NOW!
Attribution modelling is the process of determining the most effective marketing channels for investment. This book has been written to help you implement attribution modelling. It will teach you how to leverage the knowledge of attribution modelling in order to allocate marketing budget and understand buying behaviour.
Attribution Modelling in Google Ads and Facebook
This book has been written to help you implement attribution modelling in Google Ads (Google AdWords) and Facebook. It will teach you, how to leverage the knowledge of attribution modelling in order to understand the customer purchasing journey and determine the most effective marketing channels for investment.
About the Author
Himanshu Sharma
Founder, OptimizeSmart.com
Over 15 years of experience in digital analytics and marketing
Author of four best-selling books on digital analytics and conversion optimization
Nominated for Digital Analytics Association Awards for Excellence
Runs one of the most popular blogs in the world on digital analytics
Consultant to countless small and big businesses over the decade
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