• 1. About this Book
  • 2. 10 Reasons why Ecommerce Businesses Need to Have Google Analytics Set Up Correctly
  • 3. Quick Overview of Your Google Analytics Admin - Before You Set Up Your Ecommerce
  • 4. Setting Up Your “All Web Site Data” View in Google Analytics
  • 5. What the Heck are Parameters... And What do I do with the "Exclude Query Parameters" Field in Google Analytics?
  • 6. Adding Filtered Google Analytics Views Gives You Access to Better Marketing Data
  • 7. Setting up an “Include My Domain” Filter in Google Analytics
  • 8. Using Google Analytics Filters to Rid Yourself of Rage-Inducing Referral Spam
  • 9. Formulating Your IP Address Filter in Google Analytics
  • 10. Ensuring that Your Google Analytics Data is Accurate by Applying Lowercase Filters
  • 11. How to Remove Slashes From The End of your URLs in Google Analytics
  • 12. Fixing the Problem of Parameters in your Ecommerce URLS in Google Analytics
  • 13. Acquiring Your Ecommerce Store’s Unique Parameter List for Google Analytics
  • 14. How to Turn your Ecommerce Parameters into Custom Dimensions in Google Analytics
  • 15. Using your Parameter Custom Dimensions to Discover Ecommerce Opportunities
  • 16. Key Google Analytics Settings You Might Have Overlooked for your Ecommerce Configuration
  • 17. What are the Google Analytics Ecommerce Settings For and How are They Set Up?
  • 18. How to Turn on Ecommerce Tracking in Google Analytics
  • 19. Google Analytics Enhanced Ecommerce on popular Ecommerce Platforms
  • 20. Manually Adding Google Analytics Standard Ecommerce Transaction Tracking Code
  • 21. Manually Adding Google Analytics Enhanced Ecommerce Transaction Tracking Code
  • 22. Implementing Enhanced Ecommerce Features to Collect Game Changing Data For Your Ecommerce Store
  • 23. How Do You Use the Ecommerce Reports Built into Google Analytics?
  • 24. What is the Google Analytics Ecommerce Overview Report and What Should You Use It For?
  • 25. What is the Shopping Behavior Report and What Should You Use It For?
  • 26. The Importance of the Checkout Behavior Report in Google Analytics
  • 27. What is the Product Performance Report Used for in Google Analytics?
  • 28. This post has been deleted
  • 29. How can you see Individual Ecommerce Transactions in Google Analytics?
  • 30. What is the Time to Purchase Report in Google Analytics Used For?
  • 31. Deep-dive your Product Sales with the Google Analytics Product List Report
  • 32. Setting Ecommerce Goals in Google Analytics and Why This is So Important
  • 33. Adding Your Ecommerce Goals to Google Analytics
  • 34. Using Google Analytics Goals to Boost Your Ecommerce Conversion Rate
  • 35. Using the Model Comparison Tool in Google Analytics
  • 36. Segmenting Users - A Powerful Tool for Providing Data Insights
  • 37. Building Segments Using the Shopping Behavior Report
  • 38. How to Use the Segment Builder in Google Analytics
  • 39. How to Build Specific Criteria using Google Analytics' Segment Builder
  • 40. Google Analytics Segment Examples to Enhance Your Ecommerce Sales
  • 41. How to use Segmentation Analysis to Identify Opportunities and Increase Conversion
  • 42. Making the Most of the Demographics of Users When Looking at Ecommerce Data
  • 43. Google Analytics Segmentation Example - Transacted vs Did Not Transact
  • 44. Taking your Segmentation Analysis Further
  • 45. Bonus: Six Reasons Why Your Ecommerce Store Needs Google Tag Manager
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    Six Reasons Why Your Ecommerce Store Needs Google Tag Manager

    Bonus: Six Reasons Why Your Ecommerce Store Needs Google Tag Manager

    In this blog post, I’m going to run you through why your eCommerce Business should also make use of Google Tag Manager alongside Google Analytics. However, I’m not going to go into Google Tag Manager in detail here because it is a extensive topic worthy of its own book. (And I do plan to write one!). So for now I want to just summarise why I believe you should be making the most of Google Tag Manager.

    So here is a list of six reasons why your eCommerce store should definitely be using Google Tag Manager.

    Six Reasons Why Your eCommerce Store Needs Google Tag Manager

    1.    Tracking Events for Goals

    Google Tag Manager makes it really straightforward to track events for Goals, especially those events that are captured when your visitor clicks something, completes a form, or scrolls the page. It’s definitely worthwhile to be able to use Google Tag Manager tracking for this aspect of Google Analytics data.

    2.    Keeps the Tracking Code Clean and Simple

    Google Tag Manager keeps the code away from your website, because all the code and settings are is stored in the cloud. This means that your marketing staff and contractors can track your marketing and web analytics data without having to worry about breaking your website, and at the same time your website developers can update the site without having to worry about breaking the tracking code. You might still need to update Google Tag Manager if your website is updated, but at least you can do so intentionally.

    3.    You can Clean up your eCommerce Data Before you Send it to Google Analytics

    I have seen situations where eCommerce plug-ins have resulted in companies breaking the Google Analytics terms of service. If the plug-in sends broken data such as empty hits, it can increase the total number of hits in the Google Analytics account to more than 10 million hits in a month (this is the limit for the free version of Google Analytics).

    Similarly, I have seen many situations where the plug-in did not correctly structure the data for Enhanced Ecommerce, and so the data then had to be updated and sent through manually. For example, transactions should have the correct transaction value associated, while product-specific actions should have the correct product added to the action.

    4.    You Can Add Shopping Steps and/or Checkout Steps to Google Analytics

    If your eCommerce platform doesn’t send product views by default, such as add to cart, checkout etc, you may be able to add these manually. This will work for the Shopping Behaviour Report even if the specific product is inaccessible. You can also use Google Tag Manager to add separate checkout steps for the Checkout Steps Report.

    5.    You can Send eCommerce events and Tracked Goals to Other Marketing Platforms 

    You can use Google Tag Manager to send your eCommerce data to Facebook, Adwords, YouTube, Google Display Network, LinkedIn, or any other marketing platform of your choice. This will help you optimise your campaigns, and set up micro-remarketing audiences, taking your eCommerce store to the next level.

    6.    You can use Google Tag Manager to filter Personally Identifiable Data from your Parameters

    Storing Personally Identifiable Information in Google Analytics is against the Google Analytics Terms of Service, and may also breach the laws of your country or state. Data protection laws are becoming increasingly stringent, and the recently instigated General Data Protection Regulation in Europe has huge implications for businesses. So it’s important to be aware of this, and ensure that you comply with whatever laws may apply in your particular territory.

    Thankfully, you can use Google Tag Manager to strip out any errant and inappropriate data before it even reaches Google Analytics, which could be hugely important for your eCommerce store.

    So that’s six good reasons why you should be using Google Tag Manager. I highly recommend that you look into this at the first opportunity. For more information on using Google Tag Manager, please subscribe because this is the area in which I specialise.

    Google Analytics Settings Ecommerce Configuration

    Key Google Analytics Settings You Might Have Overlooked for your Ecommerce Configuration

    These settings are often overlooked when setting up Google Analytics but they can make or break your configuration. Lets look at Referral Exclusion, Cross-domain Tracking and Custom Alerts in more detail so that you don’t forget to set them up.

    1. Referral Exclusion

    Given that eCommerce websites sell products, it makes sense that they also have means to collect money. Collecting money often involves sending visitors to other payment gateway websites, such as PayPal, Afterpay etc.

    This has become commonplace now, and PayPal in particular has become a massive and trusted financial services company. But sending your visitors to PayPal and other payment processors can have a significant impact on your analytics.

    One problem with this process is that when the visitor gets sent to another website, the session on your website ends. Then when the visitor comes back to your website after completing the payment details a new session begins. That session is not linked to the previous session, yet it contains the transaction data, and so you then cannot find out the original marketing source of the purchase or any details about the original session.

    When a visitor comes to your website from another website, that’s called a Referral. In this scenario the referral source of the purchase will end up being attributed to PayPal or Afterpay – which is not what you want.

    And also you don’t want to track the customer separately when coming back again, because they only went to your website once, not twice. Capturing the session twice will give you a completely false impression of traffic; it would be yet another source of incorrect data.

    So to fix this issue you have to add the payment gateway to the Referral Exclusion List. Once this is done it will count as just one visit, not two, and the referral source will be the original referrer and not the payment gateway.

    One other thing to note, if the user spends more than 30 minutes at PayPal or any other payment processor, then the standard 30-minute session timeout will kick in, reversing the benefit of adding the external domain to the Referral Exclusion list. If this is the case for you, you will still see PayPal coming in as a referrer even though it has been excluded. To fix this you will need to increase the session timeout settings as well. These are set to 30 minutes by default, but you can increase them up to 4 hours.

    This timeout issue is especially a problem with Afterpay and other payment gateways that require the user to fill in forms.

    Do configure these settings when you set up Google Analytics for your eCommerce store. If you’re generating bad data related to payments, then you’re not getting usable information about your most important traffic… the paying customers!

    2. Cross-domain tracking

    If you have more than one website linked to the same shop, or if your eCommerce platform has a hosted solution for checkout, then you will require cross-domain tracking between the two websites if you want to track the same visitor’s activity on both websites.

    As we discussed in the referral exclusion section above, if a person goes from one website to another website then that normally counts as two separate sessions. But if both websites are your own, or if you have the ability to add tracking codes to them both, then you will want to track a visitor seamlessly between the two websites. This merges both separate sessions into one continued session between the two websites.

    So for example, the Netsuite eCommerce platform has a hosted checkout at netsuite.com. If a user checks out they are redirected to this checkout and so the transaction is not on your own domain. Another example is leadpages.com – you can design leadpages to look like your own website. So if you have a solution that goes from leadpages to your website to a hosted checkout then you need cross-domain tracking between all three websites.

    Cross-domain tracking can be added directly with Google Analytics tracking codes, but is much easier if set up through Google Tag Manager.

    3. Custom Alerts

    I recommend setting up your eCommerce account with a custom alert that tells you if there is a sudden drop of traffic to your website.

    I usually set up an alert that triggers if there is more than 60% drop since the same day of the previous week, although this percentage will need to be adjusted higher if you have a low volume website.

    Google Analytics Custom Alerts

    Make sure your custom alert is set up to be emailed to you so that you can investigate any issues if you do have a sudden drop in traffic.

    Likewise, you might want to set up alerts for sudden increases in traffic so that you can investigate these as well.

    If you haven’t set up all of these configurations in Google Analytics yet, do so as soon as possible as next we’re about to change to a new topic – The Google Analytics Ecommerce Settings.

    How to Build Specific Criteria using Google Analytics’ Segment Builder

    How to Build Specific Criteria using Google Analytics’ Segment Builder

    As I’ve mentioned in previous posts, the segment builder providers you with a huge amount of potential because it can help you to compare different audiences to find insights that can drive your eCommerce strategy. But in order to use the segment builder effectively, you will need to understand some terminology, so that your segments mean what you think they mean.

    Here are a list of terms you will see used in the segment builder, and what they mean:

    Per User / Filter Users: This option enables you to segment based on specific users. This means that if customers visit your website for more than one session you will see all of their sessions in aggregate, even if they didn’t perform any eCommerce actions in some of the sessions.

    So, for example, “all users with a transaction” will show all the sessions by those users, even if the session did not contain a transaction.

    It is also important to note that filtering by users in Google Analytics has a 93-day limit; essentially, equivalent to three months. (You can get around this using other tools alongside Google Analytics).

    Per Session / Filter Sessions: This enables you to segment based on specific sessions (i.e. the specific visit in which the user did the thing that you are interested in). This means that if they visit your website for more than one session you will not see the other sessions.

    So, for example, “all sessions with a transaction” will show only the sessions with a transaction, and not the sessions before or after a transaction if the user came back to your website again.

    This is useful for identifying behaviours that are associated with another specific action, but not so useful if you want to understand your visitors’ behaviours overall. Filtering by sessions does not have a time limit, so you can also use this instead of users, if you need a segment that is longer than 93 days.

    Include / Exclude: This lets you control whether you are including or excluding the condition from your segment. So including the country of Australia would provide you with a segment with only Australians, whereas excluding the country of Australia would give you people from everywhere but Australia.

    Note: Include can have unusual behaviour. If you include A and B then the system will filter down to only data that is both A and B at the same time, not A and B separately. So be careful when you stack conditions with include, as you can end up with some highly misleading data.

    Contains: The word ‘contains’ means ‘does the text contain the text that you have typed?’; i.e. “men’s shoes” contains “shoes” and “kids’ shoes” contains “shoes”.

    Exactly Matches / Equals / Is one of: ‘Exactly matches’, ‘equals’ or the equals symbol ( = ) and ‘is one of’ means that the text or number that you have typed in your condition is exactly the same as the data stored in Google Analytics. In the case of ‘is one of’, it has to exactly match one of a list of items. You can also choose ‘does not exactly match’, ‘is not equal’, or ‘is not one of’ if you want to reverse the condition.

    Starts with, ends with, matches regex: These conditions let you be more specific about the text and narrow it down to specific options, without being forced to narrow down to just one option. For example, all SKUs that start with ‘D’ will show all the SKUs that start with the letter only. Matches Regex lets you be even more specific. Regex is short for Regular Expression. You can read the Google Analytics Regular Expressions here.

    Greater than ( > ), Less than ( < ), Greater than or equal to ( ≥ ), Less than or equal to ( ≤ ): These enable you to select from the data that is stored as numbers. So, for example, all sessions with transactions would require “include sessions where transactions > 0”.

    OR: The condition will be met if A is true, or B is true. This means that the segment will be bigger.

    AND: The condition will be met if A is true, and B is true at the same time. This means that the segment will be smaller.

    Product data: you can specify any product data, whether it be name, category, brand, variant, SKU etc. You can also choose more than one criterion from this section. So, for example, if you have categories of women’s shoes, kids’ shoes, and men’s shoes then you can specify products in categories containing “shoes”, and all three will be captured.

    As you can see, you can be very specific when building segments. It is important to build the segment logic carefully, because once you see the data in an aggregated form it is almost impossible to detect that you have made a mistake when constructing the segment. And this can cause you to infer results that just aren’t true.

    Now that you understand the lingo and how to build your segments, lets have a look at what segments to build.

    What do I do with the “Exclude Query Parameters” Field in Google Analytics?

    What the Heck are Parameters… And What do I do with the “Exclude Query Parameters” Field in Google Analytics?

    In this article, we’re going to take a look at parameters, and examine how collecting them in your Google Analytics can help your eCommerce business. My capturing parameters on the URL you can collect useful information about what your visitors are doing. But parameters can also destroy the functionality of your data, as you will soon learn.

    What are parameters?

    Parameters are pieces of code in the URL of your website that tell your eCommerce store what to display on the page. This is an essential piece of functionality for your store, and is one of the things that can really affect the quality of your Google Analytics data. You will need to read this article and learn about what to do with your parameters if you are to use Google Analytics effectively with an eCommerce store.

    If you go to your website and you carry out some action, let’s say you sort your products by price, or change the sort to relevance, then you should see parameters in the URL. If you take a look at the browser navigation bar in the URL then you will see what I mean. So let’s have a look at the different parts of a URL.

    Let’s take a shoe retailer as an example. This is their domain:

    This is the path. The path is the directory and page name after the domain name.

    Then if there are parameters they will be listed after the page name. A question mark shows that we’re about to start a list of parameters.

    These are the parameters:

    These parameters are created by the code on the website itself. That code is supplied by the eCommerce platform that is being used by your website. That might be Shopify, Magento, Woocommerce, etc. When your users interact with the website, it sends the data to your eCommerce store via these parameters so that the software in the background knows how to display the page for them.

    So when you change which criteria you’re sorting by, or move through different pages in a search list for example, your browser sends that information to the URL as a parameter. Then your eCommerce platform reads the list of parameters and passes it into the back end of the system. This enables your eCommerce system to work out what information to pull out of its database and display to the user.

    The parameters on the URL change based on what each of your visitors are doing. This can be really useful data for your eCommerce store. But…beware! It can also really get in your way at the same time! I will elaborate on this subject in several blog posts about setting up filtering for your parameters.

    Back to Google Analytics and your All Web Site Data View. The “Exclude URL Query Parameters” field, which I told you to skip past in the last article enables you to specify which parameters you do not wish to show in any of the pages of your Google Analytics package. For your All Web Site Data view, you want this to be blank, unless your website contains any parameters that supply Personally Identifiable Information (PII). If it does, you will need them to be filtered out in all views of your Google Analytics account.

    Personally Identifiable Information includes Name, Address and Email Address, Tax File Number and any other publicly identifiable data, or data that you just don’t want to store at all.

    Important note: If your website has PII in its parameters and this is passed to Google Analytics then you may be breaching the Google Analytics Terms of Service. Additionally if the data relates to health or tax information then there may be further breaches to Australian federal laws if the data is passed to Google Analytics. I strongly recommend you audit your data to make sure that you are staying on the right side of the privacy laws.

    If you already know that you have parameters containing Personally Identifiable information, then I suggest adding those parameters to the Exclude Query Parameters field in your All Website Data view right now.

    So that’s the lowdown on what parameters are, and a quick review of which parameters you need to exclude to make sure your eCommerce store is doing the right thing by your customers’ private data. You’ll learn more about the profitable uses for parameters soon, as you continue through this book.

    Shopping Behaviour Report Google Analytics

    What is the Shopping Behavior Report and What Should You Use It For?

    The Shopping Behavior Report is another important aspect of Google Analytics, enabling you to better understand your customers. You can see the access point for the Shopping Behaviour report in the image below:

    If you have Enhanced Ecommerce set up in full then you can tell Google Analytics when your users have performed particular shopping behavior. The Shopping Behavior report provides you with a funnel report, and this will enable you to identify where you lose your customers at each step; assuming a sales sequence of visiting the website, viewing product(s), adding product(s) to the cart, checking out, and then finally purchasing.

    And the funnel aspect of the report shows you what percentage of people are getting to each stage, and at what point people are leaving.

    Funnel Graph

    The funnel graph also provides a wealth of valuable data. One such funnel graph is depicted below:

    Google analytics shopping behavior report funnel graph

    It is important to understand that the blue columns represent the number of sessions that reached that particular activity in the sales funnel, whereas the difference between one blue line and the next shows how many people left at that step. The red arrows pointing to “No Shopping Activity” etc, and percentages in red text, illustrate the absolute and relative abandonment from that step.

    Across the top of the graph above each step the absolute numbers and percentages of sessions that reached that step are also shown. While across the bottom of each step this chart shows the absolute numbers and percentages of sessions that did not continue through to the next step. And the percentages in grey arrows are indicative of the relative percentage of sessions from that step that continued through to the next step.

    The value of this report is that you can quickly identify the major step in your visitors’ shopping activity that is causing the most abandonment, and then focus on improving that area. If you observe that some areas have a much larger abandonment rate than others then that shows a weakness in your user interface or process.

    Ultimately, the average conversion rate for transactions is around 2-3%, so if yours is less, or if you have significant drop-off from any one step, then you have the ability to fix it.

    The steps are as follows:

    • All Sessions: visitor comes to the website, leaves or browses the store.
    • Sessions with Product Views: visitor looks at a specific product.
    • Sessions with Add to Cart: visitor adds a product to the shopping cart.
    • Sessions with Check-Out: visitor views the checkout form.
    • Sessions with Transactions: visitor completes the purchase.

    One point to note here is that Sessions with Product Views refers to detail product views, and not product impressions. So it is possible that the people who abandoned at step 1 did see a list of products, but then chose not to look at any one of them in more detail.

    If you want to use this report, it is important to make sure that your analytics is set up with enhanced eCommerce actions and test them properly. You need to ensure that if someone looks at a product, adds to cart, etc it is actually tracked. If this is not done correctly then you will end up with invalid results, where an entire phase is missing, or the numbers don’t reflect the true behaviour of visitors. I have seen this numerous times; for example, when the eCommerce platform doesn’t send the data for a particular step at the appropriate time.

    I would recommend setting a long period of time in the date picker when using this chart, so that you can get enough accuracy, because this report is best for seeing the bigger picture rather than small details.

    When you hover over parts of the graph you can quickly create an Ecommerce Segment based on the sessions represented by the particular part of the graph that you’re examining. Having said that, it is better to create segments manually because it gives you more control. We’ll talk more about segments and how to use them in the next section of the course. Refer to a blog post instead.

    Criteria Analysis

    Finally, criteria analysis is the second part of the Shopping Behaviour Report. This aspect of the report enables you to see the variation in the numbers when you compare different criteria. You can choose to see numbers and percentages that reach each step, or instead the abandonments at each step. This is useful for seeing how different criteria affect your eCommerce results. Keep in mind that the criteria need to be “session” level criteria to be used by this report.

    The following is an example comparing sessions between new visitors and returning visitors:

    Google analytics shopping behavior report by visitor

    And here is an example comparing abandonments between visitors from different countries:

    Google analytics shopping behavior report by country

    In these reports, you can drill down on the step that you are interested in by sorting by that column. So sort, just click on the column header.

    So that’s a detailed step-by-step guide to the Shopping Behavior report, providing you with everything you need to know about this key eCommerce visualisation in Google Analytics. The Shopping Behaviour Report is a really useful one to get right, just because it is so visual and so it tends to gel well with people at different levels in your organisation. The criteria analysis is very powerful and can help you make sense of your eCommerce results.

    Segmenting Users Data Insights Google Analytics

    Segmenting Users – A Powerful Tool for Providing Data Insights

    This article and the several following in the series will cover the topic of segmentation, as this is undoubtedly one of the key aspects of Google Analytics. Segmentation provides a powerful way to analyze your data in order to discover insights, and you’re not getting anything close to your best out of Google Analytics if you don’t take advantage of it.

    Essentially what segmentation enables you to do is filter your data temporarily based on conditions that you set in advance. This makes it possible for you to see data about particular subgroups of your visitors, and identify demographics within your traffic and clients.

    The reason why segments are such an important way to analyse your data is because Google Analytics presents all of the figures in the different columns as aggregated numbers.

    What that means is that for each different value in the primary (and secondary, if you have one) dimension, the results for each metric will be added up; ie. you get a sum, or an average for the selected timeframe.

    When the numbers are aggregated, you lose insightful details. So for example if women converted at 20% and men converted at 10%, an aggregated group with equal numbers of men and women would show a 15% conversion rate (as shown in the diagram below). This aggregated value lacks the insight that you get when you separate the data into the two separate groups.

    By default, Google Analytics has all users and sessions for the selected timeframe lumped in together as one segment, referred to as the All Users Segment. This segment gives you an overview of your analytics based on every user that came to your website.

    You can segment based on virtually any criteria that interest you, as long as the data is being captured in Google Analytics during the timeframe in question. When you have an eCommerce store, there are eCommerce-specific segments that you can use to split your users up into different groups, in addition to the regular segments that are available to non-eCommerce Google Analytics users.

    Let’s look at the Shopping Behaviour Report as an example. For each step in the sales process we can see an aggregate number of how many sessions reached that step. If we add a segment for Mobile Traffic, and a segment for Tablet and Desktop Traffic, we can now see a lot more detail. For example, we can see in the image below that a first time visitor on a mobile phone has only a 0.83% conversion rate, whereas a returning visitor on a desktop or tablet has a much larger 5.21% conversion rate.

    When we create a segment, we are able to set particular conditions which involves us only seeing the aggregated numbers for sessions, or specific visitors who meet those conditions. The benefit of this is that the numbers are recalculated based on the segment, and therefore you can compare various groups of visitors to one another.

    Some ideas for segments include:

    • people who viewed a particular product;
    • people who arrived via different marketing channels;
    • people on mobile phones vs desktop;
    • people who added to cart vs people who didn’t;
    • people who purchased products vs people who didn’t;
    • people who purchased products vs people who abandoned at checkout;
    • men vs women;
    • different age brackets;
    • people who engaged vs people who didn’t;
    • people who looked at certain types of content vs people who looked at other types of content;
    • people who signed up for email marketing or a coupon vs people who didn’t;
    • people who viewed a particular internal promotion vs people who didn’t;
    • any parameters that have been captured in custom dimensions.

    Basically, any data that has been captured in Google Analytics can be used to create a segment of all the visitors or sessions that meets the criteria you specify. I will show you two different ways to create segments in later blog posts.

    So that’s the 101 on segmentation. I can’t emphasise enough that this is a key aspect of Google Analytics, one that will open up a world of new possibilities for your eCommerce business. That’s why I want to really go into segmentation in quite some depth, please join me by continuing through the next articles in the series.

    The Checkout Behavior Report Google Analytics

    The Importance of the Checkout Behavior Report in Google Analytics

    As we have established by now, there are a wide variety of different eCommerce-specific reports included in Google Analytics. It is valuable to be able to understand the data contained in these reports, which is why I’ve taken the time to walk you through them one-by-one.

    In this post, we’re going to have a quick look at the Checkout Behavior Report. To be honest I don’t tend to use this one all the time, but it is useful for Conversion Rate Optimisation as it lets you walk through how your visitors are interacting with your checkout form. If you can examine and then improve the ways in which customers behave as they are buying your products and / or services you can make some quick wins in store profit.

    The image below shows where the Checkout Behavior Report can be accessed:

    The Checkout Behaviour Report is very similar to the Shopping Behaviour Report, however it uses the custom “Check-out Labelling” steps that you may have set if you have turned on Enhanced Ecommerce Tracking, as shown in the image below:

    Like I said, I don’t tend to use this report all that often as I’m not usually involved in checkout page optimisation projects. However here is an example of what the report looks like. A completed report would have Step 1, Step 2, Step 3 etc, labelled and quantified.

    The intended purpose of going through the steps that I will outline shortly is to identify which step visitors are reaching in the checkout form, and also to determine if there are any sudden drop-offs at a particular checkout step.

    Here is an example of possible allocation of steps:

    • Step 1: View the checkout page
    • Step 2: Complete the name and address fields
    • Step 3: Select a shipping option
    • Step 4: Enter a coupon code
    • Step 5: Add credit card, PayPal or Afterpay details
    • Step 6: Click the purchase button

    Once this process is complete, you could then view the report to see the absolute number and percentage of sessions reaching each step, the absolute number and percentage of sessions abandoning the form at each step, and the relative percentage of people moving from one step to the next.

    While this report is intended to be used with the checkout, there is no reason why you cannot set it up to work with any particular behaviour flow that you wish to track sequentially on your website. The benefit of this, over the Shopping Behaviour Report is that you can track completely custom events. I actually plan to investigate using this report more often as I can see many uses for this report that I haven’t covered here.

    There are a variety of ways that these steps can be implemented. But because the checkout steps are completely custom I recommend setting them up with Google Tag Manager rather than using javascript inline tracking codes.

    The Checkout Behaviour Report is another valuable tool in your Google Analytics toolbox, and one that can be customised to provide interesting insights. As I said, I don’t tend to use this one much myself, but if you use it with success I’d love to know how it is working for you. Are you primarily using it for checkout step optimisation?

    Google Analytics Product Performance Report

    What is the Product Performance Report Used for in Google Analytics?

    The Product Performance Report provides a ton of useful information for your eCommerce store, and you’re definitely going to want to take advantage of it. In this blog post, I will be describing some of the key data that is included in this report, and how you can use it.

    You can find the Product Performance Report in the Ecommerce Conversions menu, as depicted below:

    In this report you choose either the Summary or the Shopping Behaviour report view. They each have different table columns so I’ll describe them separately. You can choose to view your reports by Product Name, Product SKU, Product Category or Product Brand, depending upon which type of details you have supplied in your product tracking code.

    Note: You may not have data for Product Name, Product SKU, Product Category or Product Brand. If you don’t have data it will say (not set) when you select that report view.

    You can also choose to include a secondary dimension to your report, which enables you to analyse your product performance by a criteria of your choosing. To analyse further, you can also click on ‘advanced’, and filter the results based on specific criteria

    Product Performance – Summary Report

    The Summary Report is the reporting view you first see when you open the Product Performance report. It is aimed at helping you to identify the products that are doing the best overall. The Summary Report enables you to discover the following metrics about each product in your product range:

    • Product Revenue: This is the total amount of revenue earned from the specific product during the time period selected.
    • Unique Purchases: This is the total number of transactions involving the specific product during the time period selected.
    • Quantity: This is the quantity of product sold during the time period selected.
    • Price: This is the average price per sale (i.e. revenue divided by quantity) during the time period selected.
    • QTY: This is the average number of items per transaction (i.e. quantity divided by unique purchases) during the time period selected.
    • Product Refund Amount: If you process your refunds into Google Analytics, this column shows you the total revenue refunded during the time period selected. Note: to process your refunds into Google Analytics you will need to write the custom Enhanced Ecommerce Tracking code that processes the refund and sends it to Google Analytics.
    • Cart-to-Detail Rate: This is the percentage of product views that results in the product being added to the shopping cart.
    • Buy-to-Detail Rate: This is the percentage of product views that results in the product being purchased. High Buy-to-Detail Rate items make excellent cross-sells.

    This report can give you a real insight into the way the way that the particular products in your store are performing. This can be food for thought with regard to your future retail and eCommerce strategy.

    Typically you will find that a handful of your products are generating the majority of your revenue, and the rest is the “long tail”. While you probably already know those top few key performers, it is hard to know which of your long tail products to promote. By looking at your Cart-to-Detail Rate and Buy-to-Detail rate you can see which products are likely to sell well if promoted. With the right secondary dimensions in place, you can drill down to the products that have the highest conversions rates and then look at targeting data such as demographics, marketing channels, campaigns and custom dimensions to devise a strategy for pushing high potential products further up the ranks.

    Product Performance – Shopping Behaviour Report

    The Shopping Behaviour Report is the alternate view for the Product Performance report. It enables you to see which products are making their way further down the sales funnel. This is useful if you suspect that some products are more likely to be abandoned throughout the sales process than other products; or that low conversion rates is a product-centric problem rather than a problem of your webpage design.

    Here are the different columns you get:

    • Product List Views: This is the number of times that the product in question has shown up in a list of products during the time period selected. Product list views include navigating to particular shopping categories, search results, “recommended for you” results, or any other list of products shown anywhere on your website, as long as it triggers a Product Impression action when seen by your visitor.
    • Product Detail Views: This is the number of times that the product has been looked at individually during the time period selected.
    • Product Adds to Cart: This is the number of times that the product has been added to a cart during the time period selected.
    • Product Removes From Cart: This is the number of times that the product has been removed from a shopping cart during the time period selected.
    • Product Checkouts: This is the number of times that a particular product has been included in a shopping cart at the time that the visitor went to the checkout step.
    • Unique Purchases: This is equivalent to Unique Purchases in the Summary report.
    • Cart-to-Detail Rate: This is equivalent to Cart-to-Detail Rate in the Summary report.
    • Buy-to-Detail Rate: This is equivalent to Buy-to-Detail Rate in the Summary report.

    You can see an example of the Shopping Behavior Report in the image below:

    In order to have all the columns in this report show data you do need to have eCommerce product events set up. This means that your system tells Google Analytics when someone adds a product to the cart, removes a product from the cart, checks a product out, purchases a product or looks at a single product’s details. If you don’t have all of these set up, it will still show you the numbers for the columns that you do have activated and will show 0 for the columns that aren’t set up.

    So for example, if you see the image above, Product Checkouts is not implemented here, but all the other columns have been implemented. The fields that you can or cannot implement will depend on your eCommerce platform and any plugins that you are using, or whether you are manually implementing eCommerce tracking.

    So I hope that’s a useful insight into the Product Performance Report. Keep this report in mind when you’re considering adding or subtracting products from your product line, or if you’re wanting to promote more products using PPC or SEO.


    Google Analytics Ecommerce Settings

    What are the Google Analytics Ecommerce Settings For and How are They Set Up?

    Feedback from customers has always been necessary for businesses, but in the era of online commerce it can be really hard to know what people are interested in. Essentially shopping via the internet is anonymous until the final transaction. Modern online retailers still need to seek, and pay heed to, the opinions of customers, but these days this is often done by reviewing eCommerce data. So collating, monitoring and understanding this customer feedback in the form of eCommerce data is absolutely central to the successful functioning of your store.

    Not surprisingly there is provision for in-depth eCommerce data in Google Analytics. The eCommerce reports enable you to see data about visitors’ shopping interactions, and to link digital marketing initiatives to transactional data with monetary value attached.

    The eCommerce reports can also give you a rich array of data about promotions and products that visitors to your website have seen, clicked on, added to cart, checked out and transacted upon.

    Here is how it works:

    Essentially, you decide what data you want to collect, then you add eCommerce tracking code to your website to track that data and then that tracking code sends your data to Google Analytics.

    There are two different options for eCommerce data tracking in Google Analytics – Standard Ecommerce and Enhanced Ecommerce.

    When should you use Standard Ecommerce and when should you use Enhanced Ecommerce?

    The decision whether to use Standard or Enhanced Ecommerce settings in Google Analytics depends on two primary criteria. They are:

    • the technology being used;
    • data capture requirements.

    But in general it is better to go with Enhanced Ecommerce.

    Technology

    Firstly, let’s consider the technology being used. If you are using one of the major eCommerce platforms such as Magento, Shopify or WooCommerce, you can reasonably easily hook these systems up with plug-ins that capture enhanced eCommerce data. With Shopify, enhanced eCommerce is built into the platform.

    If you have a custom-built website, or you are using a different eCommerce platform, then you may or may not have Enhanced Ecommerce data available, depending upon what the developer of your website built for you. Some popular platforms like BigCommerce do not currently have Enhanced Ecommerce support.

    To add Enhanced Ecommerce to a website manually involves a lot of development work, and so if your technology doesn’t inherently include Enhanced Ecommerce then this might make your decision for you. However, if you want to go down this route then I will briefly discuss later how to add Enhanced Ecommerce to your website manually.

    Data Capture Requirements

    When it comes to data capture, Standard Ecommerce tracks transactions, whereas Enhanced Ecommerce tracks actions, products and promotions.

    In the case of products, these can be viewed in a list, clicked, viewed in detail, added to a cart, removed from a cart, added to checkout, and transacted upon.

    At each of these stages in Enhanced Ecommerce, the products that are being acted upon are being passed through into data that can be captured. However, with Standard Ecommerce we don’t learn anything about the products until the final transaction takes place.

    This isn’t a problem if all you want to do is track transactions. Tracking product interactions allows you to be more granular with your data, but this may not matter much if you’re not planning to utilise that level of detail.

    In this case, Standard Ecommerce may suit your needs more. Standard Ecommerce will track how much money people are spending when they complete transactions, and that transactional data can then be compared with regard to different pages, traffic sources, specific ads, particular referrals, etc. Standard Ecommerce makes it possible to see how much they’ve spent if they come from one of those sources.

    You would use Standard Ecommerce if you have a fairly basic eCommerce website where ultimately you’re not really planning to use the data generated all of the time, and you don’t require a really detailed understanding of how people are using your website, and your eCommerce platform has already been set up with Standard Ecommerce.

    If you just want to know how much your visitors are spending, and then break this monetary value down by different dimensions, such as a particular traffic source or demographic, then Standard Ecommerce should be fine.

    Enhanced Ecommerce has a lot more features and details, for example enabling you to track detailed product and promotion interactions. This additional functionality will undoubtedly be useful for those eCommerce business seeking more analytical tools.

    An example of the improved feature set of Enhanced Ecommerce is the ability to track when people are viewing or clicking on certain products, or adding them to their cart etc.

    If someone is browsing your website, and they see a product listed for sale, an Enhanced Ecommerce-enabled website that has been setup fully will automatically trigger actions called Product Views or Product Impressions, depending on whether they looked at the product directly, or in a list of products.

    So with Enhanced Ecommerce, when someone adds a product to their cart you can find out what the product is, and likewise when they continue through to checkout, Enhanced Ecommerce makes it possible to view what products they still have in their cart.

    The benefit of using Enhanced Ecommerce is that it enables you to acquire, and then use, detailed information about how visitors are using your website, and understand what kinds of products they are looking at.

    Google Analytics Ecommerce tracking is a framework – your implementation may be different from someone else’s

    Before we move on to talk about how to implement eCommerce data tracking, you need to understand what eCommerce tracking is… and isn’t!

    Google Analytics eCommerce tracking is a framework, which means it gives you a structured yet flexible (is that an oxymoron? 😂) way of inputting data into Google Analytics. This is undoubtedly a benefit, but I should warn you that it can also be a curse!

    Essentially with Enhanced Ecommerce, there isn’t one ‘true way’ to implement Google Analytics eCommerce tracking. Instead, there is a high level of flexibility and customisation that enables you to add eCommerce tracking to any website at all.

    So it doesn’t matter what language your website is written in, which eCommerce platform you use, or what sort of products or services that you sell; you can add as much or as little eCommerce tracking as you like.

    The drawback to this is that if you compare two eCommerce websites to each other, they may be tracking completely different data depending upon how they were initially set up or what plugins they have installed. There may also be anomalies in your Google Analytics data as a result of bugs in your eCommerce tracking code, an incomplete tracking code, or a change to your eCommerce plugin.

    So, for example, let’s say you use WooCommerce and you use a plugin to generate the eCommerce tracking code. If you change your plugin there is a reasonable chance that you will get a different result in your data, just because of the ways the two plugins differ.

    Another example would relate to those eCommerce businesses that rely on custom-built websites. Such operations may have Enhanced Ecommerce tracking turned on, but only use it in order to track transactions, as that is the only custom eCommerce code that has been written. In this scenario, if you then swapped over to a structured platform such as Shopify, you may suddenly get a whole lot of new data that you weren’t expecting, just because they wrote more code into their system.

    It really is the customisation of the eCommerce Tracking Code written by your developer, or your eCommerce platform, that makes the difference between a good setup and a bad one.

    If your Ecommerce Platform has native eCommerce tracking, or a plug-in that you can purchase that hooks right into the eCommerce system, then that makes it easier. But there may still be some tweaking that you would need to do in order to make the data readable on your website.

    It would be so much easier if turning on the eCommerce tracking settings in Google Analytics meant that your eCommerce data would just turn up in the correct format! Sadly this is not the case. But with knowledge of how it all works, and an understanding of what you require from eCommerce tracking, you can make the Google Analytics Ecommerce work to your benefit. Over the next few articles we will discuss how to implement eCommerce tracking for your store.

    How to See Individual Ecommerce Transactions in Google Analytics

    How can you see Individual Ecommerce Transactions in Google Analytics?

    As you are probably learning by now, there are a lot of eCommerce reports included in Google Analytics once you switch on eCommerce reporting. And it certainly makes sense to take full advantage of these if you have an eCommerce store, as they provide valuable information that can help you to earn more profit from your product sales.

    In this article, we’re going to look at the the Sales Performance Report. You can see the location of this report, in the Ecommerce Conversions menu, in the image below:

    The Sales Performance Report enables you to identify the metrics associated with individual transactions that were made in your store. You can view this report by either Transaction ID (i.e. a unique identifier that should match the transaction ID within your eCommerce platform) or Date [of transaction].

    The Transaction ID will be a unique ID that is generated by your eCommerce software at the time of purchase. So if your software is behaving correctly then it should match the ID listed on the receipts of customers. It is important to double-check this at the time of setting up your eCommerce analytics, to make sure your transaction ID being stored is valid. Otherwise you wouldn’t be able to look up a transaction by ID and it can be a bit of a pain to locate based on time.

    This report is useful if you want to drill down into a specific transaction in order to investigate a specific incident related to a transaction, or learn more about what happened in the session in which the transaction was made.

    You can also use this report to identify all the specific transactions that were related to a particular condition, such as all the transactions that were made after a visitor arrived via Organic Search or Social Media for example, or a custom dimension that you may have set such as payments that were made with a coupon vs a credit card. You can see an example of the standard report (with no Secondary dimension set) below:

    Shipping and tax are optional fields at the time of submitting transactions to Google Analytics and so you may find that you have $0 in one or both of these columns. Refund amount is always $0 at the time of a transaction, but you can set up your software to submit a refund to Google Analytics if processing a refund online. If you process your refunds manually you can write a custom script that submits refunds into Google Analytics based on values in a spreadsheet.

    Quantity shows you how many items were included in any specific order. So this can also help you to identify wholesale versus individual retail orders.

    Use the Sales Performance Report if you want to see what is happening at a transactional level or if you want to drill down to specific transactions or dates to identify a problem or resolve a customer enquiry. In general, you won’t need to spend much time in this report, but it is good to know it is available should you need it.