Common Google Universal Analytics Mistakes that kill your Analysis & Conversions

 

I have audited hundreds of web analytics accounts and profiles. And each account/view had at least one or two issues which 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 Universal 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 Universal 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, advanced segment, conversions segments, filters and custom reports.

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 tracked 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 it doesn’t automatically mean that you should go ahead and analyze it or even collect it in the first place. 


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 great understanding of your client’s business, industry, target market, competition and business objectives.

If you don’t 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 which 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.

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 here is not to first dive into your Universal Analytics reports.

The answer here is to first develop a great understanding of your business and its objectives. This may take you several weeks or even months. But the pay offs are worth Gold.

Any person can learn to use Google Analytics in few weeks. But

what separates an average analyst from 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 tips in great detail in the post: Translating Business Objectives into Measurable Goals.

So check it out if you are not sure.

 

Immediate Benefits of “Great” understanding of 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 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:

  • Doesn’t have great understanding of the client’s business, target market, competition and business objectives
  • Doesn’t know how to translate business objectives into measurable goals
  • Not sure what data to collect, analyze and report.
  • Doesn’t have well defined strategies in place to achieve business goals in a timely manner.
  • Doesn’t have KPIs in place to measure the performance of each goal in a timely manner.

 

2. <<Data Collection Issues>>

You need accurate data to do 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 ASAP:

1. Invalid Universal Analytics Tracking Code (UATC) formatting

  • Make sure that you copy-paste the UATC directly into the HTML of your web pages without changing its formatting.

  • Avoid copying the UATC 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 UATC from working.

2. Invalid UATC casing

  • When you customize your tracking code (to enhance its functionality) by adding few extra lines of code make sure that you don’t change the case of the new code.

  • In UATC, 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.


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

 

3. Adding UATC from another account/profile

  • Add the UATC which is specially meant for your account/view.

  • Webmasters who manage multiple analytics accounts and profiles, sometimes accidently add the UATC of a different account or view. This should be avoided at all cost.

 

4. Web pages missing UATC

Make sure all of your web pages have got UATC. You can identify all the pages without UATC by using the custom filters of ‘Screaming Frog SEO Spider’

 

5. No unfiltered view

  • Create and maintain one unfiltered profile/view. An unfiltered view is the one to which no Google Analytics filter has been applied.

  • 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.

 

6. No test view

  • Create and maintain one analytics profile/view just to test your new filters before you apply them to your main view. So if something goes wrong with you filter’s settings, you don’t lose/corrupt your data in the main view.

  • You can learn more about creating view filters from here: https://support.google.com/analytics/answer/1034823?hl=en

 

 

7. Internal traffic is not excluded

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

  • Internal traffic can greatly skew your analytics data and therefore you should exclude it.

  • You can learn more about excluding internal traffic from here: https://support.google.com/analytics/answer/1034840?hl=en&rd=1

 

8. Query parameters in Analytics reports

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: http://www.abc.com/?sid=234&hn=1 the query parameter is ‘sid=234&hn=1’.

Universal Analytics consider one URL with two different query parameters as two different web pages.

For example following URLs are two different web pages for Google:

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

http://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 Universal Analytics reports.

You can do this via your view setting in the Admin panel:

exclude URL query parameters

 

10. Too many view filters

  • Avoid applying too many filters on the same analytics view.

  • Filters can easily skew your analytics data if you are not very careful.

  • Use advanced segments and reporting interface filters wherever possible or create several different filtered views.

 

11. No set up for goals and goals’ value

  1. There is no point setting up a Universal Analytics account if you don’t want to add goals (or conversions) to it.

  2. It is simply pointless to carry out any analysis without setting up the conversion tracking first.

  3. Similarly there is no point tracking conversions to which no value has been added.

  4. A conversion without a goal value (or economic value) is known as the bogus conversion as they don’t add any value to the business bottomline.


Related Post: Analytics Case Study: When your Conversions don’t matter

Note: You can learn more about setting up goals from here: https://support.google.com/analytics/answer/1032415?hl=en&rd=1

 

12. No E-Commerce Data or Incorrect E-Commerce data

  • If you are managing an e-commerce website then you have to set up ecommerce tracking so that you can get ecommerce data (like revenue, sales, average order value, transactions etc) into your analytics reports.

  • Without ecommerce tracking set up you will never get complete picture of the performance of your ecommerce website.

  • Similarly you need to make sure that the ecommerce data you collect is accurate. Your web developer can help you here.

To learn about setting up ecommerce tracking, check this post: Google & Universal Analytics E-Commerce Tracking – Complete Guide

 

13. No set up for Cross Domain Tracking

If your website checkout process occurs on a different domain (quite common in case of affiliate websites) or your web session spans across multiple domains then you need to set up cross domain tracking.

You can learn all about cross domain tracking from here: Google Analytics Cross Domain Tracking – Complete Guide

 

 14. No set up for Event Tracking

In order to track website visitors’ interaction /activity with webpage elements (like flash videos, gadgets, images, links etc) you need to set up event tracking.

You can learn all about event tracking from here: Google and Universal Analytics Event Tracking Tutorial

 

15. No set up for Campaign Tracking

  • Universal 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 Universal Analytics only provide ‘source’ and ‘medium’ information of the referral traffic.

  • If you want Universal 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.

  • 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 URL builder.

 

16.  Overlooking Data Sampling Issues

  • Universal 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 10 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’t 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 post: Google Analytics Data Sampling – Complete Guide

 

 17. Not adjusting your bounce rate

  • One of the best ways to optimize your website conversion rate is to optimize the bounce rate.

  • If majority of people come and leave your website without completing your desired goals (like making a purchase) then you can’t have a good conversion rate.

  • But what if people come and leave your website from the landing page but still complete your desired goals. How you will determine the effectiveness of such bounced visits?

  • That is why you need to adjust your bounce rate. You can learn more about it from the post: Adjusting Bounce Rate in Google and Universal Analytics

 

 18.  Not using Universal Analytics API

  • If you manage hundreds of analytics accounts and profiles then you should use Universal Analytics APIs for fastest information retrieval.

  • Otherwise you will be spending majority of your time in creating and downloading reports instead of doing analysis.

Related Post: Google Universal Analytics Shortcuts: Tricks, Tools & APIs

 

3. <<Data Integration Issues>>

  • Data integration is one of the most challenging and difficult issues to resolve esp. for small and medium size businesses as data integration solutions are very expensive.

  • In the world of Web Analytics 2.0 we rely on several data sources from Google Analytics, Kissmetrics, Qualaroo, Facebook Insight, Compete, Survey Monkey, Phone Calls data, Call center data to internal tools like CRM to get complete picture of our marketing campaigns.

  • But jumping between different analytics tools to get complete insight is time consuming and is not very useful.

  • 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.

  • 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 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.

  • If you read the post Complete Guide to Google Adwords Analytics, you will get a pretty good idea of the skill set required just to integrate Google Adwords with Google Analytics. And here we have not talked about any APIs yet.

 

4. <<Data Interpretation Issues>>

Different people interpret same data differently. It all depends upon the context in which they analyze and interpret the data.

If you have 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 hand 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 you can interpret it correctly?

3. Selecting wrong KPIs to measure the performance of your goals.

4. Relying on small time frame to make future predictions about marketing campaigns.

5. Relying on 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 post for more details: Attribution Modeling in Google Analytics – Ultimate 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 your website? or why people don’t share your contents?  The answer to this ‘why’ is not available in your analytics reports. You need to 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 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, change in economy, market conditions all affect your data.

If you don’t 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.

 

Following  articles can help in honing your data interpretation skills:

  1. Interpreting Average metrics in Google Analytics
  2. Interpreting data trends in Google Analytics
  3. Interpreting Conversion Rate in Google Analytics
  4. Interpreting the true performance of your SEO campaigns

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 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.

If they interpret your reports incorrectly then they may take wrong business decisions.

Check this post: Complete guide to Data Reporting to learn more about data reporting issues and how to fix them.

Related PostHow to select 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.

 

Other Posts you will find usefulGoogle Analytics Account Setup Tool

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