Google Analytics Mistakes that kill your Analysis & Conversions

Last Updated: August 20, 2022

Table of Contents
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  1. Google Analytics tracking code not installed on all pages of your website
  2. Not filtering out internal traffic
  3. Missing or incorrectly set up goal conversion tracking
  4. Missing or incorrectly set up ecommerce tracking
  5. Missing or incorrectly set up enhanced ecommerce tracking
  6. Missing or incorrectly set up cross-domain tracking
  7. Missing or incorrectly set up phone call tracking
  8. Not fixing data sampling issues
  9. Not using Google Tag Manager
  10. Not using Google Analytics 4
  11. Double tracking
  12. Using the old version of Google Analytics Tracking Code
  13. Using a non-standard implementation of Google Analytics
  14. Using a plugin to install Google Analytics on a website
  15. Using a non-standard implementation of Google Tag Manager
  16. No goal funnel set up
  17. Trying to install ecommerce tracking all by yourself
  18. Being unaware of duplicate orders/transactions
  19. Not fixing refund, cancelled and test orders
  20. Not using custom alerts
  21. Not using Google Analytics Intelligence
  22. Installing ecommerce tracking via a plugin
  23. Ignoring Google Analytics Diagnostic notifications
  24. Not fixing PayPal self-referral issues
  25. Not keeping unfiltered view
  26. No or incorrect event tracking
  27. Not fixing ‘not-provided’ keywords issue
  28. Not using Custom Channels groups
  29. Not using a custom campaign
  30. Tagging internal links
  31. Not using test property
  32. Using too many view filters
  33. Not excluding query parameters
  34. Mixing up GA tracking code / Property ID
  35. Not using roll-up reporting
  36. Not using annotations
  37. Not using Google Analytics API and other automation available
  38. Not comparing Google Analytics sales data with shopping cart data
  39. Directional Issues
  40. Data Collection Issues
  41. Data Integration Issues
  42. Data Interpretation Issues
  43. Data Reporting Issues

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

The following are the most common Google Analytics Mistakes that kill your analysis, reporting and conversions:

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.

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

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.

Related Articles: 

Google Analytics Mistake #3: Missing or incorrectly set up 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.

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.

Further Reading:

  1. Google Analytics Goals and Sales Funnels – Tutorial
  2. How to set up goals in Google Analytics
  3. Google Analytics Conversion Tracking Tutorial
  4. Google Analytics 4 Conversion Tracking Guide – GA4 Goals
  5. Offline Conversion Tracking in Google Analytics – Tutorial
  6. You are doing conversion tracking all wrong. Here is why

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.

You will never be able to correlate ecommerce data with website usage data (sessions, bounce rate, page views, 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.

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.

Further Reading:

  1. Google Analytics Ecommerce Tracking Tutorial
  2. GA4 (Google Analytics 4) Ecommerce Tracking via GTM – Tutorial
  3. Calculate Ecommerce & Goal Conversion Rate in Google Analytics
  4. Why your website conversion rate is destined to remain poor

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.

ecommerce reports google analytics

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 the 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: Enhanced Ecommerce Tracking in Google Analytics – Tutorial 

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.

Without cross-domain tracking set up most of your conversions could end up being attributed to direct traffic or a 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.

Further Reading:

  1. Cross Domain Tracking in Google Analytics – Complete Guide
  2. Cross Domain Tracking in GA4 (Google Analytics 4) Setup Guide
  3. How to check cross-domain tracking in Google Analytics
  4. Cross Domain Tracking with Google Tag Manager (GTM)
  5. Setting up Sales Funnel across websites in Google Analytics

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.

Further Reading: How to Track Phone Calls in Google Analytics – Call Tracking Tutorial

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.

Further Reading:

  1. Google Analytics Sampling Tutorial
  2. Understanding Data Sampling in Google Analytics 4 (GA4)

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:

  1. Google Tag Manager removes the need for editing the website code over and over again.
  2. Through Google Tag Manager you can test and deploy tags very fast.
  3. Google Tag Manager makes advanced analytics tracking possible.
  4. Google Tag Manager makes tag management very efficient.
  5. By using Google Tag Manager, you can improve website speed.

Further Reading:

  1. Five main benefits of using Google Tag Manager
  2. What is the difference between google tag manager and google analytics?
  3. Google Tag Manager Tutorial

Google Analytics Mistake #10: Not using Google Analytics 4

Google Analytics 4 (also known as GA4, ‘Apps and Web’ ) is the latest version of Google Analytics.

Through GA4, you can combine mobile app and website usage data for unified reporting and analysis. GA4 is the fourth version of Google Analytics.

Following are the other three versions of Google Analytics:

Following are the key advantages of using GA4:

  • GA4 provides more robust cross-device and cross-platform tracking than GA3.
  • GA4 provides much more accurate reporting on unique users across platforms than GA3.
  • GA4 provides advanced analysis reports (which were earlier available only to GA 360 users).
  • GA4 provides a free connection to BigQuery.
  • GA4 allows automatic tracking for certain types of events (like scroll tracking, video tracking, exit tracking, site search tracking, etc).

Further Reading:

  1. Google Analytics 4 (GA4) vs Universal Analytics – What is the difference?
  2. Key Benefits of Using Google Analytics 4 (GA4)
  3. Google Analytics 4 training and tutorial

Google Analytics Mistake #11: 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 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.

Further Reading: How to use two Google Analytics codes on one page

Google Analytics Mistake #12: Using the old version of 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:

#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).

#4 In Universal Analytics you can implement Enhanced ecommerce tracking and create user ids. This is something that is not possible in classic Google Analytics.

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

Further Reading:

  1. Universal Analytics vs Google Analytics – What is the difference
  2. Learn to Switch to Universal Analytics from Google Analytics
  3. Difference Between Google Analytics And Google Analytics 360

Google Analytics Mistake #13: Using a non-standard implementation of Google Analytics

There are only two recommended ways 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 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.
  • Incorrectly using multiple Google Analytics tracking code on the same page.

Further Reading:

  1. How to install Google Analytics on your website
  2. How to Install Google Analytics 4 on Shopify
  3. How to Implement Google Analytics With Google Tag Manager
  4. Google Analytics Shopify Tracking via Google Tag Manager
  5. How to install Google Tag Manager on your WordPress Website

Google Analytics Mistake #14: Using a plugin to install Google Analytics on a website

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

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 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

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 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.
  • Container code contains invalid formatting and/or invalid casing.
  • 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

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 process in order to improve the website conversion rate.

Further Reading:

  1. How to set up goals in Google Analytics
  2. Google Analytics Goals and Sales Funnels – Tutorial
  3. Google Analytics 4 Conversion Tracking Guide – GA4 Goals
  4. Understanding Google Analytics Goal Templates
  5. Google Analytics Goals Regular Expressions vs. Equals to vs. Begins with
  6. Google Analytics Goal ID (goal slot id) & Goal Sets Explained
  7. How to use the Funnel Exploration Report in GA4 (Google Analytics 4) – Funnel Analysis
  8. How to fix Goal Conversion Irregularities in Google Analytics

Google Analytics Mistake #17: Trying to install ecommerce tracking all by yourself

In order to install ecommerce tracking or enhanced ecommerce tracking all by yourself you need to know the following:

  • You understand the Google Analytics development environment really well.
  • You understand the client’s development environment and database like the back of your hand.
  • You can code server-side language (like PHP).
  • You can query the database.

Otherwise, you won’t be able to install ecommerce tracking or enhanced ecommerce tracking all by yourself.

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.

You would need this help in order to add server-side code to the data layers or to query their database for you.

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

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 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:

duplicate transactions

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.

Further Reading: Duplicate Transactions (orders) in Google Analytics

Google Analytics Mistake #19: 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.

That way you can 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 transaction in Google Analytics for gtag.js and analytics.js.

The other method is to use the ‘Refund Data Import‘ feature which is explained in great detail in the article: Dealing with Google Analytics Refund – Reverse Transaction

Related Article: How to remove/modify Google Analytics ecommerce transaction in one click

Google Analytics Mistake #20: Not using custom alerts

ga custom alert

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.

Further Reading: Google Analytics Custom Alerts with Examples

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:

Analytics Intelligence google analytics

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’.

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 segmentwith 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:

Anomalies

If you do not use analytics intelligence then you will miss out on all the opportunities and anomalies detected by GA AI.

Further Reading:

  1. Google Analytics Intelligence Tutorial
  2. Understanding Automated Insights in Google Analytics 4 (GA4)

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.

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

Google Analytics Mistake #23: Ignoring Google Analytics Diagnostic notifications

analytics 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:

analytics notifications2

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.

Further Reading: 

  1. Google Analytics Notifications and Alerts Guide
  2. How to Fix Clicks and Sessions Discrepancy in Google Analytics.
  3. How to fix Goal Conversion Irregularities in Google Analytics
  4. How to fix ‘Missing Tracking Code’ in Google Analytics
  5. How to Fix Missing Ecommerce Data in Google Analytics
  6. How to Fix Missing Campaign Parameters in Google Analytics
  7. How to configure a goal flow in Google Analytics
  8. How to fix No Hits in Google Analytics
  9. How to fix self-referrals in Google Analytics
  10. How to Fix Tracking Code Mismatch in Google Analytics
  11. How to fix ‘Destination URLs Not Tagged’ Google Analytics notification
  12. How to fix ‘Incomplete Google AdWords Linking’ in Google Analytics

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:

paypal

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.

Further Reading:

  1. Tracking Google Analytics Paypal Referral and other payment gateways
  2. PayPal.com and the referral exclusion list

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.

Further Reading: 10 Google Analytics Views that you must always use

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 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.

Articles related to event tracking in Google Analytics:

  1. Google Analytics Event Tracking Tutorial
  2. Google Tag Manager Event Tracking Tutorial
  3. Non-Interaction Events in Google Analytics Explained
  4. Sending event data via measurement protocol in Google Analytics
  5. Event tracking through CSS selectors in Google Tag Manager
  6. Google Analytics Virtual Pageviews Tutorial
  7. Tracking Virtual Pageviews in Google Tag Manager
  8. Google Tag Manager YouTube Video Tracking
  9. Google Tag Manager Youtube Video Tracking via YouTube Video Trigger
  10. Scroll Depth Tracking in Google Tag Manager – Tutorial
  11. Implementing Scroll Tracking via Google Tag Manager

Articles related to event tracking in Google Analytics 4:

  1. GA4 (Google Analytics 4) Event Tracking Setup Tutorial
  2. Automatically collected events in Google Analytics 4 (GA4)
  3. What are Enhanced measurement events in Google Analytics 4 (GA4)
  4. How to setup enhanced measurement tracking in GA4 (Google Analytics 4)
  5. Recommended events in Google Analytics 4 (GA4)
  6. How to set up GA4 Custom Events via Google Tag Manager
  7. Events Report in Google Analytics 4 (GA4)
  8. Understanding Event Parameters in Google Analytics 4 (GA4)
  9. How to setup enhanced measurement tracking in GA4 (Google Analytics 4)
  10. How to use Google Analytics 4 Event Builder

Google Analytics Mistake #27: Not fixing ‘not-provided’ keywords issue

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

not-provided-keywords

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:

  • 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

Further Reading:

  1. Google Analytics (not provided) Keywords Analysis – Ultimate Guide
  2. How to unlock not provided keywords in Google Analytics?
  3. How to extract not provided keywords?
  4. How to find not provided keywords in Google Analytics?
  5. Tracking Long Tail Keywords through Google Analytics

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:

  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:

facebook referral traffic

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:

custom facebook channel

Similarly, Google Analytics can report traffic from Google Adwords as:

google ads traffic 1

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:

custom google ads channel

That’s how through custom channel groups, you can better understand the performance of various marketing channels.

Related Article: Default and Custom Channel Grouping in Google Analytics Explained

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. 

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 campaign in Google Analytics, you would need to add various UTM parameters at the end of the destination URL of each Facebook ad:

facebook ad utm

Following is an example of a Facebook ad URL that contains UTM parameters (highlighted in bold text):

https://www.abc.com/book-maths-and-stats/?utm_source=facebook&utm_medium=social&utm_campaign=pdf-book-campaign&utm_content=ad1

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: utm_source, utm_medium, utm_campaign Parameters Tutorial

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:

  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 #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:

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’.

Further Reading:

  1. Why you should use a test property in Google Analytics
  2. Using the GA4 test property

Google Analytics Mistake #32: Using too many view filters

Avoid applying too many filters on the same GA view:

too many view filters

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:

https://www.abc.com/home.php?sid=234&hn=1
https://www.abc.com/home.php?sid=234&hn=132

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:

exclude url query parameter 1

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.

Related Article: Roll up Property in Google Analytics – Tutorial

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.

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

google analytics annotations

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.

Further Reading:

  1. How to use Google Analytics API without any coding
  2. Web Analytics Tool Box from OptimizeSmart

Google Analytics Mistake #38: Not comparing Google Analytics 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 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.

Further Reading:

  1. Shopping Cart Analytics Tutorial
  2. Why Google Analytics and Shopping Cart Sales data don’t match and how to fix it.
  3. Shopping cart design best practices

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:

  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.

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:

  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: KPI Meaning, Examples, Calculation & Dashboard Tutorial

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

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.

Articles on data integration

  1. Google Analytics YouTube Integration & Analysis Tutorial
  2. Power BI Google Analytics Tutorial – Visualize GA Data
  3. How to Track Phone Calls in Google Analytics – Call Tracking Tutorial
  4. Google Analytics for Facebook Tutorial
  5. Learn to Setup Facebook Pixel via Google Tag Manager

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 an 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 a great understanding of attribution modelling.

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 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.

Read this article: How to improve data reporting skills – FREE Training and Mastering Data Reporting via Data Storytelling to learn more about data reporting issues and how to fix them.

Another article worth reading is: Best Types of Charts in Excel for Data Analysis, Presentation and 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 not working? Here are 21 ways to fix it.

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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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