Google Analytics Mistakes that kill your Analysis & Conversions

I have audited hundreds of web analytics accounts and profiles. Each account/view 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:

  1. Directional Issues
  2. Data Collection Issues
  3. Data Integration issues
  4. Data Interpretation Issues
  5. 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:

  1. What data needs to be collected and when
  2. What to look at
  3. What should be overlooked and
  4. 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.

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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 few tips which can help you in getting the right direction for your analytics project:

  1. Determine where you are now.
  2. Identify the problems that need to be addressed first.
  3. Determine the requirements to solve each problem.
  4. Determine the possible barriers to your proposed solutions.

I have explained all these strategies in great detail, in this article: Translating Business Objectives into Measurable Goals.

Following are the immediate benefits you will receive once you develop a ‘great’ understanding of your business:

1. You will immediately start talking and thinking like a business owner. You will focus on immediate gains and this will reflect in your recommendations.

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.

Here is a guide that can help you in getting started: Understanding Key Performance Indicators (KPIs) – Complete Guide

To sum up, an analyst/marketer has got directional issues, if he/she:

#1 Does not have a great understanding of the client’s business, target market, competition, and business objectives.

#2 Does not know how to translate business objectives into measurable goals.

#3 Not sure what data to collect, analyze and report.

#4 Does not have well-defined strategies in place to achieve business goals in a timely manner.

#5 Does not have KPIs in place to measure the performance of each goal in a timely manner.

#6 Is not agile enough to quickly solve conversion problems.

2. Data Collection Issues

You need accurate data to do an accurate analysis. Any conclusions drawn based on erroneous data can never produce optimum results.

Following are the most common data collections issues which you must identify and fix as soon as possible:

Google Analytics Mistake #1: Using the old Google Analytics Tracking Code

There are still a lot of organisations out there, which use classic Google Analytics (ga.js).

Universal Analytics (analytics.js or gtag.js) is a new and better version of Google Analytics.

Following are the key benefits of using Universal Analytics:

  • Universal Analytics (UA) provides many more ways to collect and integrate different types of data than Google Analytics (GA).
  • Through UA you can integrate data across multiple devices and platforms. This is something that is not possible with GA. Consequently, UA provides a better understanding of the relationship between online and offline marketing channels that drive sales and conversions than GA.
  • In UA you can create and use your own dimensions and metrics to collect the type of data GA does not automatically collect (like phone call data, CRM data, etc).
  • In UA you can implement Enhanced ecommerce tracking and create user ids. This is not possible in GA.

To get all of these benefits, you need to migrate your analytics account to Universal Analytics.

Related Article: Difference between Google Analytics and Universal Analytics

Google Analytics Mistake #2: Using invalid Google Analytics Tracking Code

Your Google Analytics Tracking Code (GATC) will either not fire or not fire correctly if it is invalid.

Your GATC can become invalid in the following cases:

  • It contains extra spaces or characters like extra comma, bracket or semicolon.
  • It is not formatted correctly. For example, change the formatting of the quotation marks used in your tracking code.
  • It contains an invalid case. For example, in GATC, function names are case sensitive. So if you make a function name like ‘ga’ all uppercase (GA) or proper case (Ga) it will stop working.

Make sure that you copy-paste the GATC directly into the HTML of your web pages from your GA account. This should ensure that the GATC formatting is not changed.

Avoid copy-pasting the GATC from a tool like MS word, as it can add extra spaces or change the quotation marks in your tracking code.

Extra spaces or extra characters like extra comma, bracket or semicolon may stop your GATC from firing on page load.

When you customize your tracking code (to enhance its functionality) by adding a few extra lines of code, make sure that you do not accidentally change the case of any part of the GATC code.

Note: In Google Analytics, URLs are case sensitive. So the webpage index.php is considered a different page than Index.php or INDEX.php

Google Analytics Mistake #3: Using a non-standard implementation of Google Analytics

There is only two recommended way to install Google Analytics on a website:

#1 By directly placing the Google Analytics Tracking Code in the head section of all the web pages of a website.

#2 By using a tag management solution like Google Tag Manager.

When you deploy the Google Analytics tracking code in any other way, your GA set up may no longer remain a standard implementation.

Following are examples of non-standard implementation of Google Analytics:

  • Google Analytics tracking code (GATC) placed outside the head section (<head> …</head>) of a web page.
  • GATC being executed via an external JavaScript file.
  • GATC contains Google Analytics commands which your current GA analytics library does not recognize/recommend.
  • GATC contains invalid formatting (extra comma, extra whitespaces, bracket or semicolon)
  • GATC contains invalid casing (GA function names are case sensitive)
  • GATC deployed via a third party plugin.
  • Using multiple Google Analytics tracking code on the same page.

There are many webmasters who use a third-party plugin to install Google Analytics tracking on their website.

These plugins often modify the original Google Analytics tracking code by:

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 Google Analytics development environment, you know exactly what you are doing and how it can affect existing website tracking and/or you can decode any plugin, your best bet is to stay away from such third-party plugins and stick to the standard installation of Google Analytics.

Google Analytics Mistake #4: Not using Google Tag Manager

GTM is a free tag management solution provided by Google.

Following are the key benefits of using Google Tag Manager:

#1 GTM removes the need for editing the website code over and over again just for adding, removing or editing tags.

Instead, one code is placed on every page on the website, which is the GTM container code. This container code literally acts as a container, as it can store and deploy several marketing and analytics tags.

Through the GTM user interface, you can: add, edit, enable, disable or remove any tag, with just a few button clicks. No need to hard code the website over and over again just for deploying and maintaining various tags.

#2 Through GTM you can test and deploy tags very fast without hardcoding the tags each and every time on your website.

With GTM installed on the website, tags can be: added, edited, tested or removed in a matter of minutes. That means you can move quickly.

For example, if you want to add a survey on your website, say for just one day, just add and publish the survey tag to the website via GTM. Once the day is over, disable the tag. That’s it. No heavy coding, no booking the time with IT, no direct changes to the website code.

#3 GTM makes advanced tracking possible

The biggest advantage of using GTM is that it makes advanced analytics tracking possible for your website. GTM provides many in-built tags and variables through which you can implement advanced tracking in a short amount of time. The same task may take several days or weeks without GTM.

For example, say you want to track clicks on all external links on your website so that you can determine how much traffic the website is sending out to other websites (advertisers, affiliates, etc). If you are using Google Tag Manager, you can complete this task in a matter of minutes. Without using GTM, you will have to add, event tracking code, to each and every external link, which is very time consuming and prone to errors.

Similarly, if you are using Google Tag Manager, you can track clicks on ‘submit’ buttons embedded on pages across your website in a matter of minutes. Without using GTM, you will have to manually add, event tracking code, to each and every submit button on the website, which is very time consuming and prone to errors.

Google Analytics Mistake #5: Double tracking

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 GA pageview tag via GTM then you should remove the hardcoded GATC code from all the pages of your website.

Otherwise, your GATC will fire twice on your website: one via Google Tag Manager and one via the hardcoded tag on your website.

Google Analytics Mistake #6: Using a non-standard implementation of Google Tag Manager

There is only one recommended way to install Google Tag Manager on a website:

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 GA 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.
  • Container code contains invalid formatting and/or invalid casing.
  • GTM container code deployed via another tag management solution
  • Use of multiple 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 #7: Some web pages missing Google Analytics Tracking Code

This is a very common issue and is more likely to happen if you are not using GTM. You need to make sure that all of your web pages have got GATC.

The best way to that is to use a tag auditing tool like tag inspector or use a website crawler like ‘Screaming Frog SEO Spider’.

Google Analytics Mistake #8: Not using goal conversion tracking

Goals measure how well your website fulfils your target objectives.

Your website goals can be something like:

  • which graduate programs are viewed the most
  • how many users contact the student service
  • how many are contacting guidance and admissions
  • how many people sign up for your newsletter etc.

Defining Goals is a fundamental component of any digital analytics measurement plan.

Having properly configured Goals allows Google Analytics to provide you with critical information, such as the number of goal conversions and the goal conversion rate for your website. Without this information, it’s almost impossible to evaluate the effectiveness of your website and marketing campaigns.

Similarly, there is no point in tracking Goal conversions to which no value has been added. A conversion without a goal value (or economic value) is a bogus conversion as it does not add any value to the business bottom line.

Related Article: You are doing Conversion Tracking all wrong. Here is why

Note: You can learn more about setting up goals from here:

Google Analytics Mistake #9: 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 /sales 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

Google Analytics Mistakes

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/sales process in order to improve the website conversion rate.

Google Analytics Mistake #10: Not using ecommerce tracking

If you run an online store where ecommerce transactions take place, you cannot just depend upon the analytics reports provided by your shopping cart.

You need Google Analytics ecommerce tracking set up for your website.

It is only by using Google Analytics ecommerce tracking that you can correlate sales data with website usage data such as sessions, bounce rate, traffic source/medium, landing pages etc. 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.

You would also need to make sure that the ecommerce data you collect is 100% accurate.

Related Article: Complete Guide to Ecommerce Tracking in Google Analytics

Google Analytics Mistake #11: Not using enhanced ecommerce tracking

There are many businesses that still rely on the 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.

If you have installed traditional ecommerce tracking on your website, then you will see the following five ecommerce reports in your Google Analytics view:

  1. Ecommerce Overview
  2. Product Performance
  3. Sales Performance
  4. Transactions
  5. Time to Purchase

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:

  1. Ecommerce Overview
  2. Shopping Behavior Analysis
  3. Checkout Behavior Analysis
  4. Product Performance
  5. Sales Performance
  6. Product List Performance
  7. Internal Promotion
  8. Order Coupon
  9. Product Coupon
  10. 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 user engagement in terms of:

  1. Viewing your internal promotion campaign.
  2. Clicking on internal promotion campaign.
  3. Viewing your products in a product list.
  4. Clicking one of the product links in the product list.
  5. Viewing product detail page.
  6. Adding/removing products from your shopping cart.
  7. Starting, completing and/or abandoning the checkout process.
  8. Asking for a refund.

For example, through the enhanced ecommerce Product Performance report you can track:

  1. Total refund amount for each product
  2. Cart to detail rate (the rate at which users add products to the shopping cart after viewing the product details)
  3. Buy to detail rate (the rate at which users buy products after viewing the product details)
  4. Product list views – number of times a product appeared in a product list.
  5. Product detail views – number of times users viewed the product detail page.
  6. Product adds to cart – number of times a product was added to the shopping cart.
  7. Product removes from cart – number of times a product was removed from the shopping cart.
  8. 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.

Related Article: Beginners Guide to Enhanced Ecommerce tracking in Google Analytics 

Google Analytics Mistake #12: Trying to install ecommerce tracking all by yourself

This is a very big mistake. If you are currently making this mistake then best of luck.

Unless you are a seasoned web developer who understands the GA development environment and also the client’s development environment/database like the back of your hand, you can’t install ecommerce tracking or enhanced ecommerce tracking all by yourself.

Unless you can code server-side language (like PHP) and can also query the database, you can’t install ecommerce or enhanced ecommerce tracking all by yourself. Period.

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 overestimate their abilities.

However, they can not be entirely blamed because Google has been promoting GTM with false promises like ‘GTM makes you independent of web developers’ for years. 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.

That is because, if you are not familiar with the server-side language used by your client and/or the client’s development environment or database, then you will need the help of the client’s IT/web developer, to add server-side code to your data layers or to query their database for you.

Without adding sever side code to GTM data layers, you can not implement many of the sophisticated trackings like ‘enhanced ecommerce tracking’ in GA.

The best practice is, to always involve your web developer / IT (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 #13: 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 the problem with such type of set up is that, 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 and 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, as long as you use a plugin.

You should install ecommerce tracking on your website via GTM without using any plugin.

Google Analytics Mistake #14: Being unaware of duplicate orders/transactions

How confident are you on a scale of 1 to 10 that your website has not got duplicate transactions issues?

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 order.

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 issue so deadly and impossible to ignore.

Google Analytics Mistake #15: Not fixing refund, cancelled and test orders

If your business, issues a lot of refunds, then you need to adjust your sales data accordingly, in Google Analytics, to reflect the refunded sales amount in your ecommerce reports.

There are a couple of methods to do that. One is by making changes to your ecommerce tracking code as explained in great detail, in the article: How to reverse an ecommerce transaction in Google Analytics. The other method is to use the ‘Refund Data Import‘ feature which is explained in great detail in the article: Dealing with refunds in Google Analytics

You can also fix cancelled and test orders in Google Analytics by reversing ecommerce transactions.

To learn more about such reversal, read the followings articles:

#1 How to reverse an ecommerce transaction in Google Analytics.

#2 How to remove/modify Google Analytics ecommerce transaction in one click

Google Analytics Mistake #16: Not filtering out internal traffic

If you are not filtering out internal traffic then there is always a good possibility that you and your staff may be inflating your own website traffic data by visiting your website every day or so.

Internal traffic is the traffic coming from you, your employees, suppliers and other service providers to your website. These people are not your target audience and therefore we don’t need to track them.

Internal traffic can easily inflate your website usage metrics (esp. if you run a low traffic website) and therefore should be filtered out from your Google Analytics reports.

Related Article: How to correctly block internal traffic in Google Analytics

Google Analytics Mistake #17: 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 campaign 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 #18: 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.

Related Article: Complete Guide to Google Analytics Intelligence

Google Analytics Mistake #19: 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.

There are three categories of Google Analytics Notification Messages:

#1 Red Notifications

red notifications

Always pay attention to red notifications and never ignore them as they indicate critical issues with your implementation/configuration set-up. Examples of Red Notifications/critical issues: ‘Missing Tracking Code’, ‘No Hits’, ‘Self Referrals’ etc

#2 Yellow Notifications

yellow notifications

These notifications are not as important as red notifications but you should not ignore them for a long period of time as they can degrade your data quality.

Examples of Yellow Notifications: ‘Missing Ecommerce data’, ‘Goal Conversion Irregularities’, ‘Clicks and Sessions Discrepancies’ etc

#3 Blue Notifications

blue notifications

These notifications denote unused Google Analytics features that may be valuable to use.

These notifications are a gentle reminder of how to get the most out of your Google Analytics.

Examples of Blue notifications: ‘Filter Internal Traffic’, ‘Link Analytics and Search Console’, ‘Configure a Goal Flow’ etc

Related Article: Understanding Google Analytics Notifications and Diagnostic Messages

Google Analytics Mistake #20: 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 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.

Related Article: Tracking true referrals in Google Analytics when using PayPal and other payment gateways

Google Analytics Mistake #21: 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.

Related Article: 10 Google Analytics Views that you must always use

Google Analytics Mistake #22: No cross-domain tracking set up

Without cross-domain tracking set-up, you won’t be able to understand and track a user’s journey which spans across multiple domains.

For example,

If a user landed on your website say via the search term ‘top men shoes’ on Google and then made a purchase on another website say (where both the shopping cart and the order confirmation page are hosted) then without cross-domain tracking set up, Google Analytics will treat the same user as two different users (one user visited and a different user visited and the user session that actually span across the two domains will be counted as two different sessions instead of a single session.

So the GA report of may tell you that a user visited via the search term ‘top men shoes‘ on google but didn’t make a purchase.

The GA report of the website, may tell you that a user visited from and then made a purchase.

So without a cross-domain tracking setup, would end up getting all the credit for conversion instead of the search term ‘top men shoes‘ and google organic search traffic.

Another example,

If a user landed on your website via the search term ‘top men shoes’ on Google, went to checkout on another website, (where the shopping cart is hosted but not the order confirmation page) and then completed the purchase on your website, as the order confirmation page is hosted on your website, in that case, GA may attribute the sales to

Thus, without a cross-domain tracking setup, you may have a hard time determining the original source of your goal conversion and/or ecommerce transaction.

Without a cross-domain tracking setup, most of your conversions could end up being attributed to direct traffic or to the wrong website.

Another advantage of cross-domain tracking is that, when you implement it, you can collect data from multiple websites into a single reporting view.

Related Article: Google Analytics Cross Domain Tracking Explained Like Never Before

Google Analytics Mistake #23: 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 such events only through event tracking or virtual pageviews.

video tracking‘ and ‘scroll tracking‘ are two types of event tracking.

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.

Related Articles:

Google Analytics Mistake #24: Not fixing ‘not-provided’ keywords issue

Not provided keyword is a keyword without ‘keyword referral data’:


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.

What keyword Hero does, is pull 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 contents 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 report for your website which includes:

  • Your daily organic Google Sessions
  • Your Top 10 keywords
  • Your Top 10 mobile keywords
  • Your Top 10 desktop/tablet keywords
  • Your Top 10 organic landing pages
  • Search engine traffic report

Related Articles: 

Google Analytics Mistake #25: 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:

1) To change the way Google Analytics label and aggregate the incoming traffic for advanced data analysis.

2) 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 / 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 / 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.

Related Article: Understanding Channel Grouping in Google Analytics

Google Analytics Mistake #26: 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.

All you will see, by default in GA, is the traffic and sales from dozens of Facebook referrers.

In order to track the performance of each individual Facebook ad campaigns 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 Facebook ad URL which 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.

Related Article: Complete Guide to Custom campaigns (UTM Parameters) in Google Analytics

Google Analytics Mistake #27: 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 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 to a page hosted on your primary domain ( is an internal link.

On the contrary, an external 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 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:

  1. utm_source
  2. utm_medium
  3. utm_campaign
  4. utm_term
  5. utm_content
  6. 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 #28: Not using test property

In the context of GA, a 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:

Every change you made to your GA property setting(s) has the potential to inflate/skew your current analytics data.

Majority of optimizers directly make changes to their ‘live GA property’ before testing them on a different property.

Let us call this different property as ‘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 purpose), 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’.

Related Article: Why you should use multiple properties in Google Analytics

Google Analytics Mistake #29: 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 #30: Overlooking data sampling issues

Google Analytics selects only a subset of data (called 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, analyzing a subset of data (or sample) gives similar results to analyzing all of the data.

But in case of high traffic websites (more than 1/2 million pageviews each month), the selected sample no longer remains a good representative of all of the data.

This produces inaccuracy in your reports and results in data sampling issues.

When Google Analytics is sampling your data badly, you can not rely on the metrics reported by it.

Any marketing decisions based on such reports could also result in huge monetary loss.

To determine and fix your data sampling issues, check out this article: Google Analytics Data Sampling – Complete Guide

Google Analytics Mistake #31: Not excluding query parameters

Exclude query parameters from your view reports.

A query parameter (like session ID, visitor ID etc) is what that appears after the question mark (?) in a URL.

For example in the URL: 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, 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 #32: Using incorrect 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 #33: 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 (,,, 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 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 to through your websites, apps and/or marketing campaigns.

Related Article: Implementing rollup reporting in Google Analytics

Google Analytics Mistake #34: Not using annotations

In order to conduct a very focused and meaningful analysis, you need to 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.

This centralized location is called ‘Annotations’ which you can access under the ‘view’ column in GA Admin area:

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 #35: 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 in 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.

Related Articles: 

Google Analytics Mistake #36: Not comparing GA sales data with shopping cart data

Shopping cart handles sales data much better than Google Analytics

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 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 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.

Related Articles:

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, Compete, 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 analysis and attribution modelling.

Articles on data integration

4. Data Interpretation Issues

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 average….good “great” understanding of your business, its objectives and the problem you are trying to solve.

Other than that you must acquire good knowledge of excel and get hands-on experience in actually analyzing data trends and various charts.

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.

6. Not calculating the correct conversion rate.

7. Too much focus/reliance on conversion rate.

8. Not understanding the ‘average’ metrics.

9. Not understanding the statistical significance issues associated with average metrics.

10. Not understanding the maths and stats behind web analytics.

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 great understanding of attribution modeling.

Check out this article for more details: Google Analytics Attribution Modeling – Beginners Guide

13. Not selecting the right attribution model.

14. Too much reliance on historical data.

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 UK and the data is collected from 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 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?

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 wrong business decisions.

Read this article: Complete guide to Data Reporting and How to become champion in data reporting via Storytelling to learn more about data reporting issues and how to fix them.

Another article worth reading is: How to select the best Excel Charts for your Data Analysis & Reporting

Following are the most common data reporting issues:

#1 Reporting data without solid recommendations.

#2 Reporting a metric all by itself.

#3 Reporting a data trend which is of less than 3 months

#4 Not segmenting the data before presenting it as a trend.

#5 Not adding annotations to your graphs to describe the various peaks and valleys in the data trend.

#6 Not using the right graph/chart to present the data.

#7 Not segmenting KPIs before presenting them

#8 Presenting Internal KPIs to senior management/client.

#9 Not formatting the data in your tables.

#10 Using too many Google Analytics screenshots in your reports.

Next Read: Google Analytics stopped working? Here are 10 ways to fix it.

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About the Author

Himanshu Sharma

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  • Over 15 years of experience in digital analytics and marketing
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  • Runs one of the most popular blogs in the world on digital analytics
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