In my experience, many people and organizations lack the knowledge to modify the data they see in Google Analytics. It can be very powerful to manipulate the data for enhancing business insights!

We will first discuss five methods to temporarily manipulate the data in Google Analytics. In addition, five effective ways to permanently modify Google Analytics data are discussed.

Let’s dive right in!

Table of Contents

• Temporary Google Analytics Data Manipulation
  • Segments
  • Report Filters (Standard)
  • Report Filters (Advanced)
  • Campaign Tracking
  • Custom Channel Groupings
• Permanent Google Analytics Data Manipulation
  • Google Analytics On-Page Tracking
  • Google Tag Manager
  • Data Import
  • Google Analytics Filters
  • Default Channel Grouping
• Concluding Thoughts

Temporary Google Analytics Manipulation

Google Analytics allows you to temporarily modify data in several ways.

This can be very useful as different views of your data contribute to greater insights. Actually, it is very hard to optimize your business outcomes without going beyond the obvious surface, like looking at all data in aggregates.

Here are five methods that I have found to be very effective.

1. Segments

Segments belong undoubtedly to the greatest features of Google Analytics. It supports ad-hoc and retroactive data manipulation in extremely effective ways.

Google Analytics allows you to compare four different segments (audiences) within the reporting interface:

Applying segments is one of the most important strategies that every marketer and analyst should understand and utilize.

It helps you to see how different subsets of traffic perform, let’s you spot opportunities for optimization and much, much more.

I recommend reading the articles below to get the most out of Google Analytics segments.

• In-Depth Overview of Different Google Analytics Segments.
• Ultimate Collection of Powerful Google Analytics Segments (100+ Examples).
• How to Leverage Sequence-Based Segments in Google Analytics.
• Deep-Dive into Leveraging Hit-Scoped Segments.
• 10 Differences Between View Filters and Segments.

2. Report Filters (Standard)

Another great feature to manipulate Google Analytics data are the report or table filters.

• Standard report filters allow you to filter data for the first dimension in your report and this can sometimes be limiting.
• Advanced report filters are more powerful as they allow you to filter on all available dimensions and metrics in your report.

Standard report filters are limited, but very easy to work with. An example is shown below:

I have selected all landing pages that begin with “/google”. The caret “^” is one of the regular expressions that is very helpful when working with report filters. Make sure to read this article to learn more about regular expressions in Google Analytics.

But what if you want to look at this set of landing pages and only include those that have a bounce rate higher than 50%? In that case you need to work with the advanced report filters.

3. Report Filters (Advanced)

There is an “advanced” link next to the search field above the selected report.

This is where you can add multiple selection criteria for the report you are looking at.

Now, I have selected an additional condition on bounce rate: higher than 50%.

In short, advanced report filters allow you to further drill down on the data that you need to review.

There is one more trick I like to show you. This relates to adding secondary dimensions to your base report.

You can use an additional filter – after adding the secondary dimension “Default Channel Grouping” – to narrow your data set down to “Organic Search” channel data only.

4. Campaign Tracking

Traffic channel analysis is very important for any business operating online.

Campaign tracking is at the heart of many of the reports in Google Analytics related to channel analysis.

Google Analytics allows you to define five utm parameters for all your campaigns:

• utm_medium
• utm_source
• utm_campaign
• utm_content
• utm_term

This results in a specific way of capturing the data and displaying it in Google Analytics. A custom report is shown below:

In short, you have a great deal of control on how to capture all traffic on your website by manipulating the utm parameters.

5. Custom Channel Groupings

In my experience, Custom Channel Groupings are one of these underused, but very powerful features in Google Analytics.

They allow you to group all your traffic sources in specific buckets for traffic and attribution analysis.

The great thing is that you can set them up and apply the rules retroactively to historical data.

Here is an example of a Custom Channel Grouping I set up for one of my clients:

Specific rules – in this case based on utm parameters – allow for grouping all traffic sources in certain buckets.

It’s much more effective to use this report (or the Default Channel Grouping dimension) for high-level data analysis if compared to source/medium evaluation.

On this topic, I recommend reading the article below. It provides much more background information on how to use Custom Channel Groupings to manipulate Google Analytics traffic source data.

• Top 5 Reasons to Create Custom Channel Groupings in Google Analytics.

Permanent Google Analytics Manipulation

Permanently changing the data that is collected and shown in Google Analytics can be very powerful, but you should be aware of potential risks.

Here are five effective ways to permanently manipulate the data in Google Analytics.

6. Google Analytics On-Page Tracking

The first method you could use is directly modifying your Google Analyics tracking code. There are dozens of reasons why you want to change your “basic” GA tracking: measure across multiple domains, add extra Events for interaction tracking, control the percentage of sessions being measured (because of hit limit) etc.

I want to mention upfront that in most cases this is not the recommended way to go as today Tag Management solutions (e.g. Google Tag Manager) are much more efficient to implement and control your GA tracking.

However, if for whatever reason you still want to implement all scripts hardcoded, please review the support hub here.

Direct links are provided below:

• gtag.js.
• analytics.js.
• AMP HTML.
• App+Web properties (beta).

These links will help you modify the basic GA tracking (on page) to manipulate the measurements coming through in Google Analytics.

7. Google Tag Manager

A much more effective approach for most of you will be to implement Google Tag Manager and manipulate the tracking and data collected via all the options available in GTM (and Data Layers).

Two great starting points to learn more:

• Simo Ahava’s blog.
• Analytics Mania.

Julius Fedorovicius is the GTM expert behind Analytics Mania and he offers great courses (for both beginners as well as intermediates) on GTM.

These blogs and GTM experts will get you on track very quickly. You will learn effective ways on how to manipulate and control your Google Analytics measurements via GTM.

8. Data Import

Data Import lets you join the data generated by your offline business systems with the online data collected by Analytics. This can be a great help for organizing, analyzing and acting upon this unified data view in ways that are better aligned with your specific and unique business needs.

There are different types of data you can upload to Google Analytics:

Hit Data

• Refund Data.

Extended Data

• User Data.
• Campaign Data.
• Geographical Data.
• Content Data.
• Product Data.
• Custom Data.

Summary Data

• Cost Data.

You can set this up at the property level of your Google Analytics account.

Here are three additional resources to learn more about this topic:

• Data Import Step-by-Step Guide For Google Analytics.
• Google Analytics eCommerce: upload product data with Google Sheets.
• Repair Your Data in Google Analytics 360 with Query Time Processing.

Note: the last article is very interesting as it provides tips on how to “repair” your GA data (GA 360 is required).

9. Google Analytics Filters

The last three methods discussed work primarily at the property (= data collection) level, but what if you want to manipulate the data in one or more reporting views?

This is when Google Analytics filters become your best friend!

Five ways to effectively use Google Analytics filters:

• Exclude employee or third-party contractors’ IP addresses from your data.
• Set up a separate test view in Google Analytics.
• Mitigate Google Analytics campaign tracking issues.
• Exclude all technical query parameters at once.
• Collect data from one (your main) target country.

As shown below, filters are stored at the account level and applied at each individual reporting view.

Read the Ultimate Google Analytics Filters Guide to learn all details about why and how to set them up in your account.

Note: I strongly recommend setting up a test view where you experiment with your filters first. One mistake can totally ruin your data in Google Analytics.

10. Default Channel Grouping

In chapter five we have discussed a Google Analytics feature called Custom Channel Groupings. In addition, there is the Default Channel Grouping in Google Analytics. This is one of the features I rely on for most of my clients.

It also supports aggregating traffic sources (e.g. based on source/medium) into high-level buckets.

There are several, important differences if compared to Custom Channel Groupings:

• Default Channel Groupings don’t work retroactively.
• Default Channel Groupings can be applied in different ways and to different reports if compared to Custom Channel Groupings.
• Huge difference is that the Default Channel Grouping can be leveraged via the Google Analytics API where the Custom Channel Grouping can’t.

The potential connection to the Google Analytics API is why I often rely on the Default Channel Grouping. It’s extremely powerful to use the channel dimension when automatically exporting traffic for advanced data analysis or creating dashboards.

Concluding Thoughts

By now you have learned a lot about how to manipulate Google Analytics data to enhance the insights from your data.

There are several other ways to influence the data you see in- and outside of Google Analytics, but these methods are a great starting point.

Be careful with permanently modifying your Google Analytics data if you are rather new to Google Analytics. It’s much safer to work with the other methods mentioned in the beginning of this article if you don’t want to risk making a mess of your data.

Now it’s your turn! What are your thoughts on manipulating Google Analytics data? Do you have any preferred methods to share?

The post 10 Strategies to Effectively Manipulate Google Analytics Data appeared first on Online Metrics.

Why is transaction revenue in Google Analytics not matching our back-end data? Why do I see $2.000 revenue on transaction id “123ABCDEF” instead of $200, the actual revenue amount?

Ok, you will never have a 100% match between revenue measured in Google Analytics and the data in your back-end system. There are 100+ reasons why both numbers will deviate. However, you should do your best to make Google Analytics’ numbers as accurate as possible.

In this blogpost you will learn three things:

• How to set up a custom report for spotting duplicate transaction issues.
• How to automate this analysis via the Google Analytics API.
• How to mitigate these issues via three potential fixes.

First of all, if you haven’t read them yet, you should check out the other articles in the (Enhanced) Ecommerce section.

Table of Contents

• Introduction to Duplicate Transactions
• Custom Report to Spot Issues
• Automate Duplicate Transaction Tracking
• Fix Duplicate Transactions
• Concluding Thoughts

Let’s start with an introduction to duplications transactions first.

Introduction to Duplicate Transactions

Duplicate transactions in Google Analytics refer to a single transaction that was counted two or more times in the selected time period. This overcounting of transactions can greatly skew your Google Analytics data.

It impacts many other metrics as well, some examples below:

• Product revenue
• Quantity of products sold
• Average order value

I have seen hundreds of Google Analytics accounts with Enhanced Ecommerce implemented. You don’t have to worry if the percentage of transaction IDs with duplicate transactions is below 1%. However, you should definitely investigate further if it is substantially higher.

Custom Report to Spot Issues

You can create a custom report via Customisation > Custom Reports > + New Custom Report.

The building blocks of the report (metrics and dimensions) are:

• Metrics: Transactions and Revenue (optional, but helpful for impact analysis).
• Dimensions: Transaction ID.

I recommend choosing a time frame of at least 30 days, but in some cases you might want to go for two or even three full months of data.

Sort on transactions (high to low) if not set on default. And here is the corresponding report:

You would still need to do some manual work for further impact calculations. That’s why I recommend using the Google Analytics API for deeper analysis.

Automate Duplicate Transaction Tracking

You can make a lot of calculations manually, but whenever possible I try to automate those.

Here is an example of an analysis I recently ran across 15 brands that one of my clients is responsible for (all automated):

• Overall, it looks good.
• 12 Out of 15 brands have “percentage of transaction IDs with one order” greater than 95%.
• Only three brands have a lower “score” of 93% (two brands) and 88% (one brand).

In this case I would first focus on the three brands with the lowest score and see whether there are things we can do to improve the measurements in GA.

Now the key question, how can we automate this setup?

Step 1: install and open Supermetrics in Google Sheets.

Visit this URL and scroll to Google Sheets and Supermetrics if you are unfamiliar with this tool.

Step 2: set up your query in Google Sheets.

Here is what you need to select in Supermetrics (first center your mouse on the cell where you want the query to start):

• Data source: Google Analytics.
• Select views: Google Analytics view where you want to pull the data from.
• Select dates: dates to run the query on (e.g. “Last 30 days”).
• Select metrics: Transactions and Transaction revenue (optional).
• Split by / rows: Transaction ID and # rows to fetch (make sure to set high enough to pull all Transaction ID data in Google Sheets).
• Options: try to avoid Google’s data sampling (only available if you are on a paid plan).

This is what the data might look like:

In this case we have 20 transaction IDs with each one transaction, so everything looks good in this case.

Step 3: add another sheet in Google Sheets.

Now you need to add another sheet and make some automated calculations based on the data above.

The sheet above is named “Raw Data 4” and referenced in the formulas below.

This is it! You could do this for multiple GA reporting views if needed. The higher the “% order OK” the better.

You could dive deeper if you have a lot of orders and group transaction IDs by number of transactions. Simply add more COUNTIF formulas to get this done. Also, you could make data visualizations if that works better for you.

Now it’s time to look into potential solutions to mitigate this duplicate transaction issue.

Fix Duplicate Transactions

Here are three ways to mitigate the duplicate transactions issue in Google Analytics.

Option 1
Base the transaction event trigger on someting in your back-end. It should only trigger when the actual transaction takes place. This could be after the user has entered their credit card information and completes payment. And then, the event trigger should only work once and after your system has validated the transaction.

Option 2
Make your “thank you” pages to load only once and only accessible after a visitor has completed the transaction successfully. Keeping transaction hits on the “thank you” page is ok as long as a user can see and visit the page only once. Every new attempt to access/reload this page should redirect them to a different page that contains order detail information the user is probably looking for.

Option 3
The third option is to use the customTask functionality in Google Tag Manager. Here is how it works (in short):

• If the transaction ID in the hit is found in browser storage, this customTask blocks the hit from ever being fired, this preventing the duplicated information from reaching Google Analytics.
• If the transaction ID in the hit is not found in browser storage, the customTask sends the hit to GA normally, but it also stores the transaction ID in the list of transactions that has already been recorded. Thus, it blocks any future hits with this ID from being sent.

Please read Simo Ahava’s blogpost to learn more about implementing this method.

Concluding Thoughts

Tracking transactions incorrectly in Google Analytics can greatly affect your reports and data in Google Analytics.

In this article you have learned to spot potential issues, automate the analysis and how to fix it (three different ways).

As mentioned, transaction and revenue tracking will never be 100% accurate in Google Analytics, but you should strive for the best numbers possible.

Now it’s your turn!

What are your thoughts on duplicate transactions and ways to deal with it in Google Analytics? Happy to hear your feedback!

The post How to Find and Fix Duplicate Transactions in Google Analytics appeared first on Online Metrics.

I have worked with many Ecommerce businesses in the last years. In my experience, “out of stock” issues are quite common among online retailers and can have a big negative impact on sales and brand image.

First, I will start with a quick general introduction and after that we dive deep into the Google Analytics implementation and analysis.

I recommend reading the entire article from start to end, but you can skip the introduction if you really want to.

Note: some of the links on this page are affiliate links. Link to affiliate disclosure.

Table of Contents

• Introduction Out-of-Stock Issues
• Data Requirements Enhanced Ecommerce
• Product-Scoped Custom Dimension
• Product-Level Analysis
• Three Segments for Conversion Analysis
• Out-of-Stock Sessions Analysis
• Ecommerce Data
• Conversion Rate Trends
• Factor and Revenue Impact
• Concluding Thoughts

Let’s dive right in!

Introduction Out-of-Stock Issues

You will be ok if you run a large Ecommerce website and have just a few products out of stock that are not really popular. But, having multiple popular products out of stock can quickly turn into a real problem.

Here are the three most common scenarios:

1. The product is permanently out of stock.
2. The product is temporarily out of stock.
3. The product is expired.

The most common scenario that I encounter is #2: one or more products are temporarily out of stock.

Here is a list of common responses from consumers:

• She switches from Ecommerce platforms to buy the same item from another retailer. And even worse, she switches brands entirely.
• A negative impact on store and brand image especially when a product has been heavily advertised or promoted.
• Loyal customers might substitute a different size or variety of the same product.
• Loyal customers not in dire need will possibly wait to buy the item later when it is back in stock.
• Customers encountering an out-of-stock item before or even after they have added items to their cart may abandon the site altogether, including removing other items that were already in the cart.

Hopefully enough reasons to take this very seriously. Now I will show you how to collect relevant data into Google Analytics!

Data Requirements Enhanced Ecommerce

First of all, you need to get the “basic” Enhanced Ecommerce implementation right.

Review this set of Ecommerce articles to get an in-depth understanding of Google Analytics and Ecommerce.

I recommend splitting the implementation of Enhanced Ecommerce in (at least) two different phases. Also, we want to work backwards when implementing this feature. You can review the image below:

Image courtesy: Google Tag Manager Course for Intermediate Users

Implementing the different items of phase one is required for an in-depth “out of stock” products analysis.

This is because the availability of products is usually shown/measured at the product detail level. Some online retailers also implement this feature on gallery pages (product listing), but in this analysis we will solely focus on product detail pages.

Product-Scoped Custom Dimension

I have written a thorough guide on using Custom Dimensions. Make sure to read it if you are not familiair with them.

Availability (In Stock and Out of Stock) is categorized as a product-level Custom Dimension.

Make sure to follow this rule:

“Product-scoped Custom Dimensions are per-product-per-hit metadata that you need to send with the Enhanced Ecommerce payloads.”

Here is an example of an Enhanced Ecommerce dataLayer.push that contains the Custom Dimension “Availability” in slot #3:

In this case, the visitor is exposed to two different products of which one is in stock and the other out of stock. Embedding the dimensions in this way, will ensure that Google Analytics can pick them up correctly.

There is one more thing you need to set up in the Google Analytics admin section (property level):

Make sure to match the index number and set the scope to “Product”.

Product-Level Analysis

Now it’s time to dive into the first product-level analysis based on this new data collected in Google Analytics.

You will learn how to perform an analysis via Google Sheets and Supermetrics.

—————————————————-

Note:

I recommend setting up two Custom Metrics if you want to perform an analysis inside of Google Analytics.

• Custom Metric 1: captures the product detail view when a product is out of stock. // required
• Custom Metric 2: captures the product detail view when a product is in stock. // optional 

By doing this, you can define a calculated metric that shows out of stock (OOS) percentages for products (name or SKU) directly in Google Analytics.

—————————————————-

Custom Report for Testing

It’s always great to test with certain metrics and dimensions in Google Analytics or via the query builder first.

Here is one example of a report that contains two dimensions and one metric:

• Dimensions: Product SKU and Custom Dimension “Availability”.
• Metric: Product Detail Views.

Report Setup

Report Data

You can create a similar report based on the “Product Name” dimension. By doing this, you already verify that it is possible to combine this set of dimensions and metrics in and outside of Google Analytics.

Google Sheets and Supermetrics

The great thing is that – even without the setup of Custom Metrics – you can make a detailed, automated analysis of OOS percentages at the product name or SKU level in Google Sheets.

There are various ways on how to accomplish this, but I recommend using two tools:

• Google Sheets
• Supermetrics (link to Google Sheets add-on)
• *Google Analytics API // connecting “bridge”

*The Google Analytics API supports you in automatically extracting data from Google Analytics into Google Sheets (via Supermetrics).

Ok, let’s get started with the first step. I will take your through the complete setup at the Product SKU level (you can replicate it on Product Name).

Step 1: install Supermetrics add-on on your computer.

Step 2: launch Supermetrics inside Google Sheets.

Step 3.1: run first query on Product SKU and Product Detail Views.

Here is what you need to select in Supermetrics (first center your mouse on cell A1):

• Data source: Google Analytics.
• Select views: Google Analytics view where you want to pull the data from.
• Select dates: dates to run the query on.
• Select metrics: Product Detail Views
• Split by / rows: Product SKU and # rows to fetch (make sure to set high enough to pull all Product SKU data in Google Sheets).
• Options: try to avoid Google’s data sampling (only available if you are on a paid plan).

Click on “Get Data to Table”. It usually goes pretty fast! Great, you are now done with your first query.

Step 3.2: run second query on Product SKU, Product Detail Views and the related Custom Dimension.

First, to save time, go with your mouse to cell A1. Click on “Duplicate” (under query actions) and go with your mouse to cell D1.

There are only a few changes that you need to make within the query builder:

• Split by / rows: Product SKU and Custom dimension [ID] that corresponds to “Availability” Custom Dimension.
• Filters: Custom dimension [ID] equals “Out of Stock”. // change name if you have named the value differently.

Click on “Get Data to Table”. Great, you have now all data in Google Sheets. It should look somewhat similar to the below:

This sheet serves as a Raw Data Sheet, you could name it as “RAW DATA PRODUCT SKU”.

Step 4: match the two queries so that you can calculate Out of Stock percentages at the product level.

One quick and easy way to get this done is by using the VLOOKUP function.

An example is shown below:

Please review this video below if you need more information on how VLOOKUP works:

Here is an example of a setup to review Out of Stock percentages of different products:

Step 6: get insights from your data and act on it.

You might be able to answer all kinds of questions with this new data:

• How much money are we spending on marketing campaigns to drive visitors to out of stock products?
• Which of our customers (e.g. identified via User ID) wanted products that were out of stock?
• Can we retarget those customers via e-mail when stock is replenished?

There is simply so much value in this data!

Note: you could add product revenue/amount metrics as well, but be careful when interpreting them!

BONUS: automate it!

Ok, this is going beyond the goal of the tutorial, but if you have the (internal) knowledge of Google Sheets and advanced functions, you can definitely automate a lot of things here.

• Periodically and automatically pull the data from Google Analytics.
• Create automated charts for your data set.
• Set up automated custom alerts that inform you when values cross a certain barrier.

Make sure to set up your reporting needs and talk to a technically skilled person to get the job done!

Three Segments for Conversion Analysis

In addition, we can also make an analysis at the aggregated level. I recommend creating the following Google Analytics segments:

1. Sessions with out of stock product views.
2. Sessions with in stock product views.
3. Sessions with in stock and without out of stock product views.

Out of Stock Segment

In Stock Segment

In Stock (no Out of Stock) Segment

Out-of-Stock Sessions Analysis

Let’s dive into an out-of-stock traffic analysis. Again, we can use Google Sheets with Supermetrics.

Sessions (absolute)

Here is what you need to select in Supermetrics:

• Data source: Google Analytics.
• Select views: Google Analytics view where you want to pull the data from.
• Select dates: dates to run the query on.
• Select metrics: Sessions.
• Split by / rows: Year & Month.
• Segment: Out of Stock.
• Options: try to avoid Google’s data sampling (only available if you are on a paid plan).

And here is an overview of sessions (monthly basis):

There is a clear peak in May and June. But, this doesn’t tell the whole story.

What if these months simply have more traffic in general? That’s why we need to go one step further and calculate relative percentages per month.

Sessions (relative)

Add another query to Supermetrics to get the job done (only difference is that the segment includes all sessions with product detail views):

• Data source: Google Analytics.
• Select views: Google Analytics view where you want to pull the data from.
• Select dates: dates to run the query on.
• Select metrics: Sessions.
• Split by / rows: Year & Month.
• Segment: PDP Sessions.
• Options: try to avoid Google’s data sampling (only available if you are on a paid plan).

Now you can use both data sources to calculate percentages and turn them in a chart:

For this website and online retailer, March and April (2018) had relatively a high percentage of sessions where visitors encountered a Product Detail page with a product was out of stock.

Ecommerce Data

How do out of stock and in stock sessions correlate to sales? This is when you need to apply segments in Google Analytics.

Here is an overview of one year’s Ecommerce data:

In this case, the sessions where a visitors encountered an out of stock product, resulted in a substantially higher conversion rate.

There can be several explanations and one of the reasons is that those visitors in general compare more different products before eventually purchasing. They are really “shopping” on the website.

The “in stock (no out of stock)” segment CR% is relatively close to the “in stock” segment.

Here is another overview to give you some context in terms of user behavior:

Clearly a different segment in terms of user behavior!

In the next section I will show the monthly Conversion Rate trends in 2018 for both the “out of stock” and “in stock” segment.

Conversion Rate Trends

I have used the Google Analytics API, Google Sheets and Supermetrics to create the chart below:

• Be aware of potential sampling challenges if you have a high traffic website. The paid version of Supermetrics will mitigate that.
• In 2018 there are large differences in Ecommerce CR% on a monthly basis.
  • Absolute and relative differences are greater for out of stock sessions.
  • Relative differences of CR% (out of stock / in stock) also vary per month.
• In general, there is a correlation between months with low overall percentage of out of stock sessions and Ecommerce CR%.
  • Ecommerce CR% is higher in months with a relatively low amount of out of stock sessions.

Factor and Revenue Impact

In this section you will learn some strategies on how to perform a missed revenue analysis in case of out of stock.

We will look into two types of analysis:

• Product Level Revenue Analysis.
• Aggregated Revenue Analysis.

Product Level Revenue Analysis

At the product level, you can calculate potential lost revenue for certain products based on these factors:

• Total product detail views of a certain product / variant // make sure to capture product views of all variants
• Total product detail views of a certain product with “out of stock” label.
• Price per product/variant.

I recommend working with product SKU instead of product name. The “product name” dimension usually aggregates different product SKUs of which some might be in stock and others out of stock.

You can perform this analysis in Google Analytics (use calculated and custom metrics) or in Google Sheets or different application.

Aggregated Revenue Analysis

But what if your company/client asks you to do an overall transaction/revenue analysis?

There are many ways to address this question and it can get really challenging to come up with reliable data insights.

Let me explain one way of looking at potential missed revenue.

You need to work with factors. The factor shows the relative differences in Ecommerce CR% per month between the out of stock and in stock segment.

Here are some data points based on the chart under “Conversion Rate Trends”:

• It is very common that low factors correlate to periods with high percentages of out of stock sessions.
• In this case, March and May 2018 have the lowest factor and very high percentages out of stock sessions.

Missed Revenue Calculation (Example May 2018)

A few details are shown below:

• Assumption: Out of Stock doesn’t impact In Stock numbers/percentages.
• Factor in May 2018 equals 1.7.
• What if the calculated factor would be 1.8, 1.9, 2.0 etc.?
• Average factor equals 2.0 (based on 2018 dataset).
• Baseline revenue is $700,000. // this is total transaction revenue “out of stock” segment

I have created this chart based on the information above.

• We would see a $40,000.00 increase in revenue when the expected factor would be 0.1 higher in a certain period of time.
• We estimate our transaction revenue loss to be approx. $100,000 if in May 2018 the expected factor would be between 1.9 and 2.0 (instead of 1.7).

Based on these calculations, we can conclude that having products out of stock can dramatically impact your revenue numbers and bottom line.

And, as I already stated in the introduction, there is much more at stake when you are dealing with high percentages of out of stock products!

Concluding Thoughts

Having products out of stock can be a real nightmare. Your revenue and brand image are at stake if you don’t deal with it in a proper way!

Very often, Ecommerce businesses have inventory systems where they can run a query to see which products (SKUs) are out of stock.

Where they are lacking is connecting it to Analytics, customer behaviour and sales. This is where Analytics comes in scope.

In this blogpost I have shared several strategies to measure and analyze out of stock data both at the product as well as aggregated level.

It’s not an easy exercise, but in my opinion definitely worth exploring further!

Now it’s you turn!

Do you currently measure product availability for your company or clients in GA? And what are your thoughts on analyzing this data in Google Analytics?

The post How to Analyze Products Out of Stock in Google Analytics appeared first on Online Metrics.

In the last years I have worked with many different companies to support them with implementing Enhanced Ecommerce.

I can say it’s definitely not an easy implementation and you will probably face many challenges along the way. But, it’s all worth it!

The Google Analytics implementation and configuration audit usually reveals a lot of gaps within the Enhanced Ecommerce setup.

And in this blogpost I will discuss 15 of those issues that I come across regularly.

Quick note that you can find other related articles here.

Table of Contents

• Strategic Issues
• Google Analytics Issues
• Google Tag Manager Issues
• Data Layer Issues
• Debugging Issues

Issue #1: No Time Invested in Background of Enhanced Ecommerce (Strategic)

I strongly recommend to dig through several resources (online) before you try to implement anything new on your website.

This is especially true for modules like Enhanced Ecommerce.

You will have a much higher risk to go wrong if you skip the important resources that are already available and try to do everything on your own.

Here are three resources to check out:

• Enhanced Ecommerce Guide for Google Tag Manager. (Simo Ahava)
• Enhanced Ecommerce (UA) Developers Guide. (Google)
• Top 15 Enhanced Ecommerce Implementation Tips. (Online Metrics)

Issue #2: Not Defining the Scope of Your Project First (Strategic)

The Enhanced Ecommerce module comes with a full set of reports in Google Analytics. You can find it by navigating to Conversion > E-commerce (make sure to first enable it in Google Analytics at the view level).

You will only see meaningful data in all reports if you implement all the Enhanced Ecommerce activities shown below:

• Product Impressions
• Product Clicks
• Product Detail Impressions
• Add / Remove from Cart
• Promotion Impressions
• Promotion Clicks
• Checkout
• Purchases
• Refunds

However, the good thing is that you don’t have to do everything in one time. I actually often recommend splitting the implementation in two or sometimes even more parts.

Here is how I usually go about it (working backwards):

• Phase 1a: purchase and checkout.
• Phase 1b: add / remove from cart and product detail impressions.
• Phase 2: product clicks and impressions.
• Phase 3: rest of actions (if applicable).

Large companies – with enough development resources and knowledge – can implement phase 1a and 1b at the same time.

Note: tracking refunds can be very challenging as refunds are sometimes processed months after the initial purchase (and completely in backend).

Issue #3: Not Being Realistic About Implementation Time (Strategic)

I hope by now you understand that implementing (the entire) Enhanced Ecommerce module is not something to fix in a day.

There might be plugins out there offering some help for your specific store, but still you need some time to carefully plan everything.

Implementation time varies, but you need commitment from developers and the marketing team to get the job done. Be realistic upfront when planning out all activities and better to start collecting data one week later than collecting wrong data in your Google Analytics account.

We will talk about several Google Tag Manager issues soon, but let’s first discuss three Google Analytics configuration mistakes.

Issue #4: Not Enabling Enhanced Ecommerce (GA)

Google Analytics will only show you Enhanced Ecommerce date if you enable the correct settings at the view level.

Here is an example of the Google Demo Store:

Make sure to configure both settings as “ON”.

Issue #5a: Implementing the Wrong Checkout Steps (GA)

You have the option to implement and configure checkout details as part of the Enhanced Ecommerce module.

This requires both work on the GA as well as GTM side. The GA is the easy part, but still often goes wrong.

“In Google Analytics, you only need to define the steps that you have identified and implemented as checkout steps. And never include the purchase.”

Here is a good example from Google:

Two things to keep in mind:

• Don’t implement / configure the cart page or action as a checkout step.
• Never add the purchase to the GA Enhanced Ecommerce configuration.

Issue #5b: Not Implementing Checkout Labels (GA)

This is something you want to avoid:

Always add labels for the checkout steps that you have identified and implemented.

The Google Analytics setup part is easy; many more issues can occur in relation to GTM and the dataLayer implementation (most companies choose that implementation option).

Issue #6: Using the Wrong Universal Analytics Tag (GTM)

Create a Universal Analytics tag and set the Track Type to Transaction.

This is what you should do when you want to implement standard Ecommerce (not recommended).

You should always choose Enhanced Ecommerce, even if you only want to implement the purchase action.

For Enhanced Ecommerce, make sure to send all information either with a pageview or event tag to Analytics. And don’t forget to enable Enhanced Ecommerce features. Here you can review an example:

Issue #7: Incorrectly Applying the Non-Interaction Setting (GTM)

You need to define the “non-interaction” setting for all events that you set up via GTM.

Here is an example of tracking a “product click” action:

This is set up correctly as a “product click” is indeed an interaction that should impact (lower) the bounce rate of a particular page.

Be mindful when using events to convey EE data to Google Analytics.

“Product detail page event: a visitor lands on a product detail page and immediately leaves the website. This event – showing the product detail page – shouldn’t impact bounce rate. Non-interaction should be set to true to accommodate for this.”

Note:

• The advantage of using events to convey EE data is that debugging in Google Analytics is more easy. Also, for most of you if will be easier to apply segmentation to certain Enhanced Ecommerce actions.
• The advantage of using pageviews to convey EE data is that you don’t send extra hit to Google Analytics. Something to consider if you are close to the GA hit limit / not on a paid plan.

Issue #8: Triggering Duplicate Pageviews (GTM)

As mentioned above you can choose to send Enhanced Ecommerce data to Google Analytics via an event or pageview tag.

Maybe you want to limit the hits sent to GA and you decide to use pageviews to convey Enhanced Ecommerce data.

Here is how you can prevent this to happen in your account (two sample tags):

You will generate two pageviews for each product detail page visit if you don’t modify the GTM setup.

Therefore you need to add an exclusion trigger to the GA – Pageview – All Pages tag to prevent it from firing on a product detail page (or any other page within your Enhanced Ecommerce implementation).

The best way is to set it up on the page level so that the Exception Event matches the Trigger Event.

In the example above the duplicate pageview won’t occur as there is an exception set up on “Page View – Product Detail”. It means that only the “GA Ecommerce – Product Detail” tag will fire on PDPs.

Issue #9: Directly Using GTM to Send Product-Level Custom Dimension (GTM)

Product-scoped custom dimensions and metrics are unique in relation to Enhanced Ecommerce.

I have seen several GTM setups where these product-scoped attributes where included in a GTM tag. This won’t work though!

These are extra data points that can and should be added directly into the objects within a products array, example of Simo Ahava below.

In this example, dimension5 is in the object within the products array.

Issue #10: Sending Duplicate Transaction Data (GTM)

I have come across many cases where duplicate transactions are stored in Google Analytics.

An example (custom report) is shown below:

The screenshot above indicates there are multiple transactions captured under the same Transaction ID.

This will negatively impact many data points in Google Analytics. Google Analytics is not really capable to deduplicate transactions by itself.

Two options to solve this issue:

• Make sure the developer activates the tracking code (or dataLayer.push) one time per transaction.
• Implement a GTM solution to prevent duplicate transactions in Google Analytics.

Issue #11: Not following the right EE structure and naming conventions (Data Layer)

Here is how Enhanced Ecommerce is most often implemented:

1. Ecommerce date is pushed to the Data Layer by a developer (e.g. product detail impression, purchase etc.).
2. The Universal Analytics Tag (with Enhanced Ecommerce enabled) sends the data to Analytics (via pageview or event tag).

However, it is crucial to know that the develop needs to send the ecommerce data in the right format and use proper naming conventions.

Three rules for the “dataLayer.push”:

• An ecommerce object that contains data related to a particular funnel step / action you want to track must be present.
• The name of the action is required and should not be modified.
  • E.g. detail, add, purchase.
• Related data to a particular action should be pushed to Google Analytics.

Here is a correct (add to cart) example from Simo Ahava’s blog:

The example includes an ecommerce object, the name of the action (“add” in this case) and related (product) data to that action.

The dataLayer.push does not have to include all fields shown above, some are optional. For example, “variant” is an optional field. Ensure that the developer always uses the right names (attribute) when referring to a particular item. Otherwise, it won’t work.

Issue #12: Not Sending Product-Level Information in the Entire Funnel (Data Layer)

Consistency is really key when it comes to Enhanced Ecommerce.

Here is an example:

“The developer sends product details such as product variant and brand in a Product Detail View. In that case, you will want to send these with all other relevant actions (e.g. Add to Cart, Checkout and Purchase) as well.”

You will miss these data points on pages other than the PDP if you don’t add it to every step of the funnel.

There is very limited automatic persistence or attribution when it comes to Enhanced Ecommerce, find out more here.

Note: this post teaches you how to set up funnel tracking in GA4.

Issue #13: Unclear and Inconsistent Product Category Structure (Data Layer)

Again and as a reminder, consistency is key!

The category in traditional Ecommerce had just one layer where you could store the category information. However, within Enhanced Ecommerce, you can have five layers/levels of product category data.

Required: Send the category with every single product in all funnel steps you want to query against.

Here you can see there are potentially five product category levels you can implement.

The category to which the product belongs (e.g. Apparel). Use / as a delimiter to specify up to 5-levels of hierarchy (e.g. Apparel/Men/Shorts).

• Product Category Level 1: Apparel
• Product Category Level 2: Men
• Product Category Level 3: Shorts

And here is a quick code example:

It can be very useful, since bringing this hierarchy into your product category implementation allows you to analyze product category performance on many different levels.

Issue #14: Mixing Up Checkout and CheckoutOption (Data Layer)

Enhanced Ecommerce allows you to send multiple pieces of information in relation to the checkout:

• Checkout step.
• Checkout option.

Make sure to send the checkout option always after the corresponding checkout Step has been sent. Otherwise it won’t work!

You can use the checkout option hit to send extra information about a specific checkout step.

For example, if checkout step 3 is where the user chooses the payment method, you’ll want to send checkout step 2 when the user first lands on the payment method selection page. Then, after the user clicks or selects the payment method, you can send the checkout option hit with payment details.

Issue #15: Not Running Tests Properly (Debugging)

Many times companies implement Enhanced Ecommerce with all its features in one time and without proper testing.

I highly recommend taking a different approach and careful plan the rollout of Enhanced Ecommerce first.

Also, you really need to test before go-live. Preferably first in a testing environment and after again in the live environment. Fixing bugs in the testing environment first is crucial.

In short, take the time when rolling out Enhanced Ecommerce!

Concluding Thoughts

Enhanced Ecommerce is one of the greatest features of Google Analytics. And as you have seen, it can be a great challenge to implement correctly.

You will have a head start if you apply the learnings from this blogpost and solve the most common Enhanced Ecommerce issues.

Also, make sure to read Simo Ahava’s monster guide on Enhanced Ecommerce to keep you on the right track!

Now it’s your turn! What’s your experience with Enhanced Ecommerce? Make sure to share your tips or concerns in the comment section.

The post [Solved] 15 Most Common Enhanced Ecommerce Issues appeared first on Online Metrics.

No doubt that everyone reading this article has a lot of experience in dealing with product pages.You might be the owner of an Ecommerce shop or maybe you are responsible for Ecommerce Marketing and/or Analytics. At a minimum, you have seen several product pages during your own online shopping journeys.

In my experience, some of these pages are a big help in making a purchase decision. But at the same time, a lot of them need to be optimized in many ways.

Google Analytics can reveal a lot about individual product performance via Enhanced Ecommerce. In addition, you can make an aggregated analysis on the product page level. And that’s exactly where this new post is about!

Table of Contents

• Website Content and Goals
• Product Page Segments
• Session-Level Analysis
• User-Level Analysis
• Concluding Thoughts

First, we will dive into goal setting for different content areas on your site. This serves as a general introduction to the main topic.

And then you will learn in all depth about analyzing product page performance in Google Analytics. You can apply these techniques in other Analytics tools as well.

Website Content and Goals

“Only 1% of the visitors on my homepage buy my product, how can we get this percentage to 2% or higher?”

First of all, you need to be realistic in terms of current performance and what wins are possible to gain (in the short run).

Most importantly is to understand the function of each page. Some pages have a primary role in the purchase process, e.g. the checkout pages. But what about your privacy policy page?

In other words, the goal of a particular page on an Ecommerce site doesn’t have to be converting a visitor into a customer. At least, very often not within the same session as the page was visited.

Two examples are shown below.

Store Locator

• It doesn’t make sense to analyze this page on transaction performance or other related metrics.
• Visitors to this page should be grouped under a non-converting “use case” (and not attributed to the online prospects segment).

Overview or Routing Page

• This page serves as a subcategory overview page (Computer & Office > Apple).
• Main, expected routes are to a sub-subcategory page.
• Again, it wouldn’t make sense to directly/only judge the performance of this page in terms of transactions.
• I would recommend retrieving the percentage of visitors that navigates to a subcategory page of Apple. That could be the primary goal of this page, leading as many visitors as possible to a subcategory page of Apple.

And now let’s look into a product page.

Product Detail Page (PDP)

• No doubt that a lot of elements on this page are part of the purchase decision.
  • Product image.
  • Product price.
  • CTA – Add to Cart.
  • CTA – Add to Wishlist.
  • Quantity selector.
• It makes sense to analyze the page performance in relation to a purchase.
• In addition, I would look into Add to Cart and Add to Wishlist conversions. Add to Cart conversions can be analyzed with Enhanced Ecommerce and the product performance report.
• Further, you can measure Add to Wishlist with event tracking.

In this blogpost we focus on analyzing product pages in relation to the main, purchase event and segmentation across different dimensions.

Product Page Segment

First step is to group all the pages on the website that belong to the “product page” segment.

As you might have guessed, we will focus the analysis on the Google Merchandise Store data.

I recommend reading this post on Google Analytics segments if you need a refresher on segmentation in GA.

In my experience, there are three scenarios when you want to set up a product page (PDP) segment in Google Analytics:

1. The URL structure for PDPs is ideal for defining a segment.
2. The URL structure for PDPs is non-ideal, but it is still possible to define a segment.
3. The URL structure for PDPs makes it very tough or impossible to define a segment on default.

Let’s look into each of these scenarios and the Google Merchandise Store.

1. Ideal URL Structure

Here is an example of an ideal URL structure for defining a PDP segment:

It turns out that every PDP on Conrad’s website has the /p/ identifier at the beginning and ends with the item no.

We can use this information to filter and segment on the PDPs for our analysis.

Learning how regular expressions work is very useful when working with segments. In this case we could use the following RegEx (keep it simple):

• ^/p/ -> captures all pages that start with /p/.

2. Non-Ideal URL Structure

A non-ideal URL structure doesn’t come with one, unique identifier to distinguish product pages from other website pages.

I have encountered websites where I needed to write very complex RegEx statements to capture all product pages at once. And before that I also needed to thoroughly analyze a whole bunch of URLs to demystify the actual structure.

This is something not every marketer or analyst masters.

In doubt about what to do? Make sure to talk to the dev team if you encounter such a situation. Most probably they are skilled to write RegEx statements AND at the same time know a lot about the website structure.

3. Impossible URL Structure

The Google Merchandise Store makes it (almost) impossible to extract the product page URLs with a Regular Expression.

Even complex RegEx statements like, /google\+redesign\/[A-Z]{1,15}\/[A-Z]{1,15}\+.*$, don’t do the job because of similarities in how the product page and category page URLs are built.

It would require a ridiculous long and multiple statements RegEx to get this to work!

However, we don’t have to worry as there is (almost) always a way to tackle challenges.

In this case it’s all about getting the required metadata right.

Solution 1: Content Grouping

Create a content group by tracking code.

You will need dev help here with the implementation. So that they bucket the pages in the right category where one of the groups would be “product pages”.

You can use the content group name to build a segment in Google Analytics.

Read this article if you want to learn more about content grouping.

Solution 2: Custom Dimension

In Google Analytics you can set a custom dimension at the hit, session, user and product level.

At the page level you usually work with hit level custom dimensions.

Here is how we could get this to work for the Google Merhandise Store:

• Configure a custom dimension in Google Analytics and set it at the hit level. E.g. “Page Category”.
• Fetch for each of the pages on the website the “Page Category” value via the GTM dataLayer.
  • https://shop.googlemerchandisestore.com/Google+Redesign/Apparel -> Category
  • https://shop.googlemerchandisestore.com/Google+Redesign/Apparel/Android+Sport+Socks -> PDP

You can use the custom dimension value “PDP” to build a segment in Google Analytics.

Read this article if you want to learn more about custom dimensions.

Solution 3: Use a Funnel Segment

You know what, there is actually another solution without having to modify the implementation! :-)

Prerequisite is that Enhanced Ecommerce is implemented correctly.

Unfortunately, you will see odd data when applying this segment:

Normally, this segment also includes bounced sessions. That’s why I prefer not using it in the analysis as there might be something wrong in the implementation.

Solution 4: Mix of Page and Page Title Dimension

For the Google Merchandise Store, there is another great solution that pops up if you carefully examine the data!

All category pages have in common that the Page Title includes “|” and this is not the case for product pages. We can use that information to define the segment:

Please note that I have defined this segment as a sequential segment.

By doing this, I tell to Google Analytics to treat the Page and Page Title rule within one single hit -> hit-scoped segment!

We can use this segment for our analysis.

In the next chapters we will go deeper into segmentation and how to make a segmented analysis on product pages. The Google Merchandise Store (data Q1 and Q2, 2019) is used.

Note: it’s still a good practice to use content groupings and custom dimensions for setting up and working with certain metadata / categorizations across a wide range of reports in Google Analytics.

Session-Level Analysis

At the session-level we can make different types of analysis.

We will primariy focus on a segmented analysis on “product page sessions”. It means that we include entrances on the PDP, but not solely focus on those in a separate analysis.

The fact that the Google Merchandise Store doesn’t have a lot of direct entries on the PDP is one of the reasons for this choice.

Segment PDP = Page (Session)

Here is (again) the segment that we use:

Audience Analysis

• Roughly 23% of all sessions include a PDP view.
• Engagement (in terms of pages/session and session duration) are higher for this segment compared to all users (expected).
• Bounce rate is 11%; if you look at the distribution of landing pages this is also what we would expect. Not a lot of users enter the website on the PDP.

Device Analysis

• Desktop (overall) is the major traffic source of Google Merchandise Store.
• Relatively, more desktop visitors hit a product page during a session when compared to mobile.
• Tablet is a very small player in terms of traffic to the Google Merchandise Store.

Channel Analysis

• Organic Search is both overall as well as for this particular segment the largest traffic source.
• Referrals bring relatively the most PDP sessions.

Revenue Comparison Analysis

• 68% of revenue and 62% of transactions come from PDP sessions.
• Ecommerce Conversion Rate is higher for PDP sessions (more sales oriented).
• Converted PDP sessions have a higher AOV compared to overall converted sessions.
• Still, 38% of all transactions don’t fall in this segment.

A closer look at the website structure reveals a different route to purchase a product (without visiting a PDP).

This could explain a lower percentage of users interacting with the PDPs on the website before making a purchase. Especially true, since a lot of Google employees are ordering those products and this group of users doesn’t need much information before making a (cheap) purchase.

This is also a plausible reason for the lower AOV we see among all purchases as these also contain quick purchases without hitting a PDP.

Hopefully you already see the true power of segmentation in unlocking behavioral patterns on a site.

User-Level Analysis

I will keep this section short to not overcomplicate the analysis.

Once you understand session-level segmentation and analysis, it’s a great next step to dive into user-level analysis.

Keep in mind that – on default – a user-level analysis is focussed on the client ID which is unique for every browser and device. Someone visiting your site today on desktop and coming back tomorrow on mobile is not recognized as the same user.

That being said, there is another limitation here as Google Analytics caps the maximum time period for a user-level analysis (segmentation) to 93 days.

Here is how the user-level segment for a PDP included session looks like:

The segment definition only changes at the Filter level where Sessions become Users.

And here is the message in Google Analytics:

I will share one analysis for Q1, 2019 and again data from Google Merchandise Store.

Revenue Analysis (User)This is how to interprete the data:

• In Q1 2019, GA measured $12,387 revenue for the Google Merchandise Store with a session-level CR% of 0.12%.
• For PDP visits (sessions) the revenue is $8,303.39 and the session-level CR% equals 0.31%.
• However, if we look at users with a PDP session (and include all their sessions), the session-level CR% is a bit lower (0.24%).
• At the same time, the revenue is a bit higher, but there is not a big difference if compared to the session-level segment.
• In this case we can conclude that a small portion of transactions (21 = 161 – 140) occur through users not hitting a PDP page in converted session B, although they had a PDP hit in session A.

As you already see, applying and understanding user-level segments is a whole different ball game. Something to be discussed in another post in more detail!

Concluding Thoughts

Analyzing product page performance isn’t as simple as you might think.

First of all, you need to demystify a website’s structure and come up with the right definitions for building segments.

Then you need to apply the defined segments to the right set of reports and interprete the data in the context of the website and business situation.

Main point is that segmentation is crucial when you want to derive insights from your data. Getting knowledge and hands-on experience with regular expressions, segments and projects to work on, will get you where you want to be!

A great place to start your journey is the Google Demo Account.

Now it’s your turn! What are your thoughts on making a product page analysis? Happy to hear your thoughts!

The post Expert Guide to Product Page Analysis in Google Analytics appeared first on Online Metrics.

Segments belong to the most powerful features of Google Analytics and a topic I very much enjoy sharing about.

In this blogpost you learn how to leverage sequential segments for creating hit-scoped segments. First, I will give you a quick refresher on segments in general.

Table of Contents

• Introduction to Segments
• How Hit-Scoped Segments Work
• Three Segment Examples
  • #1 Specific Product Added to Cart
  • #2 Click on Email Address on Specific Page
  • #3 PDP Entrances Analysis
• Concluding Thoughts

Introduction to Segments

Most of you will probably know about the segments feature in Google Analytics. It’s a key element in taking your audience understanding and data analysis to a more advanced level.

In the last decade, I have worked with segmentation in great depth and applied many different segment types to both client as well as personal work.

And Google Analytics segments have been a great help in generating insights!

Simply said, a segment is a subset of your Analytics data, based on visitor characteristics or behavior.

Three Ways to Leverage Segments

There are many different ways in how to use segments to your advantage.

Here are three reasons why you should use segments.

1. Segments and Making Business Decisions

Segments can prevent you from making no meaningful or bad business decisions. Aggregated data simple doesn’t tell the complete story; what data-driven business decisions can you make based on an average Conversion Rate of 1.11%?

You need to know a lot about the audience that’s behind this number.

“You shouldn’t try to measure and optimize the digital world on plain aggregates.”

2. Segments and Showing Trends

One of the great ways to leverage segments is by unhiding trends and convincing your boss to take action.

What trend do we see here?

For this website, mobile traffic accounted for 60% of all sessions at the beginning of 2017. We see a clear upward trend in the last two years.

It’s obvious that the mobile experience needs to be fully optimized.

3. Segments and Creating Audiences

Segments are very popular in a unique way.

You can use them as the base of your (Remarketing) audiences.

Creating audiences from segments is very powerful and can really help you and your business.

Three Types of Segments

On the highest level, there are three types of segments:

• User-scoped segment
  • E.g. segment of users who made a purchase in one or more sessions in the selected time period.
• Session-scoped segment
  • E.g. segment of sessions where a purchase occurred.
• Hit-scoped segment
  • E.g. segment of sessions where a specific product was bought.

In the rest of the post we focus on the third group of segments: hit-scoped segments.

How Hit-Scoped Segments Work

Sharing an actual example will help you to understand how hit-scoped segments in Google Analytics work.

Let’s use the Google Merchandise Store in our example.

I have created two segments based on the “remove from cart” action and the most popular product in 2018.

• Ecom Segment (condition)
• Ecom Segment (sequence)

We use two months of data (April and May, 2018) to avoid sampling in Google Analytics.

Ecom Segment (condition)

This segment includes sessions with a “Remove from Cart” and “Nest Hello Doorbell – USA Product” action, without both happening at the same time as a prerequisite. E.g. a session with “PDP Nest Hello Doorbell – USA view” and “Remove from Cart” related to a different product is included in the segment.

Ecom Segment (sequence)

This segment includes sessions with a “Remove from Cart” and “Nest Hello Doorbell – USA Product” action, with both happening at the same time as a prerequisite. E.g. a session with “PDP Nest Hello Doorbell – USA view” and “Remove from Cart” related to other product is only included in the segment if the “Nest Hello Doorbell – USA Product” was removed during the same session.

Custom Report: Removes from Cart

The best way to verify a segment is to create a specific report that relates to it.

Step 1: Create Custom Report

Step 2: Apply both segments.

Step 3: Verify segments and data.

This looks all good to me:

• Both include the same number of “Next Hello Doorbell – USA” Product Removes.
• The number of “Total Product Removes” is far higher for the wider/condition segment.
• The “sequence” segment shows just a few products that were removed from cart in addition to the one on top.

Let’s have a last look at the Audience Overview report.

It’s plausible to have 101 sessions and 108 “Next Hello Doorbell – USA” Product Removes. Potentially, the event could be trigged more than once in a session.

Hope that by now you understand the difference between both segments.

Three Segment Examples

Here are three examples of hit-scoped segment, based on Google Merchandise Store:

1. Specific product added to cart.
2. Click on email address on specific page.
3. PDP entrances analysis.

#1 Specific Product Added to Cart

This segment allows you to isolate the users’ session(s) where the “Nest Hello Doorbell – USA” was added to cart.

As expected, the majority of revenue comes from the product that (at a minimum) was added to the cart.

#2 Click on Email Address on Specific Page

Now we want to isolate users’ sessions with “Email Address” clicks on a specific page, e.g. home:

Let’s create a suitable segment:

And here is the corresponding data:

Great, the numbers for the homepage match with the drill-down we created earlier.

It looks all good as we only see a few other clicks (on other pages) for the sessions where the user clicked on “home email link”.

#3 PDP Entrances Analysis

The website structure of the Google Merchandise Store isn’t very helpful when you want to define a product detail page (PDP) related segment.

Here is an example of a category and product detail page:

Category Page

Product Detail Page

Unfortunately, the “Page” dimension follows for (some of) the category pages the same structure as for the PDP.

BUT, if you closely look at the “Page Title” you can see that the “Page Title” of the category page carries a “|”, where the PDP doesn’t.

This is where we can apply the power of hit-scoped segments to get the sessions that we need.

• Landing page matches RegEx “/google\+redesign\/[A-Z]{1,15}\/[A-Z]{1,15}\+.*$”.
• Page Title does not contain “|”.

Setting up the “landing page” definition for the product detail page (PDP) requires advanced skills of Regular Expressions and demystifying URL structures. Check out this RegEx guide to learn more about them!

And here is the corresponding data to verify our segment:

This looks all good, except one odd thing here. For some pages the Page Title reads “Page Unavailable”. It looks like those pages don’t exist anymore or the Page Title was incorrectly set. Anyway, if needed you can remove those by extending the segment as shown below.

Last thing is to verify our new dataset:

Here we go, “Page Unavailable” has disappeared! :-)

Concluding Thoughts

Without doubts, segments and regular expressions are crucial features and skills to master for any analyst (and marketer).

They allow you to demystify certain trends, make a deep-dive analysis, build audiences and much more.

In this post we have looked into the hit-scoped segments in Google Analytics. You have learned how to create them and how to put them in practice.

Don’t just create segments for the sake of it, although this can be helpful at first.

Make sure to primarily focus on a business question and the data that you need to unlock insights that weren’t visible before.

Good luck on your segmentation journey and let me know how it goes!

What are your thoughts on segmentation and hit-scoped segments in general? Would be great to hear from you!

The post Deep-Dive into Leveraging Hit-Scoped Segments appeared first on Online Metrics.

Visits became sessions, visitors became users… This refers to the external appearance or name of those metrics, but the internal data model related to “scope” has remained the same in the last few years.

In this blogpost we will dive deep into the different scopes, pitfalls and how to apply them correctly in any situation.

Table of Contents

• Metrics and Dimensions
• Scope Definition and Four Types
• User Scope
• Session Scope
• Hit Scope
• Product Scope
• Custom Dimensions and Metrics
• Common Mistakes
• Concluding Thoughts

Let’s start with looking into the building blocks of Google Analytics reports: metrics and dimensions.

Metrics and Dimensions

Metrics and dimensions are the building blocks of Google Analytics reports.

These definitions help you to distinguish between both in Google Analytics:

• A dimension is a characteristic of an object that can be given different values -> a dimension describes data
• A metric is an individual element of a dimension which can be measured as a sum or ratio -> a metric measures data

Here is a quick report from Google Analytics:

This report consists of:

• Two dimensions: medium and country.
• Three metrics: sessions, % new sessions and new users.

Visit the Dimensions & Metrics Explorer to learn more about the standard metrics and dimensions available in Google Analytics.

You can learn a ton here about the background of metrics and dimensions:

• Search on a term that matches with (a) particular dimension(s)/metric(s).
• Expand the entire section and go through all items.
• Expand one particular section to explore underlying metrics and dimensions.

Here is an example of dimension “device category”:

In addition to this, Google Analytics allows you to work with custom metrics and custom dimensions (highly recommended).

I will share more about these custom features later in this article.

Scope Definition and Four Types

Google Analytics collects and presents data in a certain way. This is what scope refers to.

Metrics and dimensions are limited to one scope, so you need to align the scope of a metric with the scope of a dimension.

There are a few things that can happen when you mismatch the scope of a metric and dimension.

Most often it comes down to one of these things:

• The report doesn’t return any data.
• The report returns inaccurate data.

In general, you stay out of trouble when you work with standard reports.

However, you need to be fully aware of scope when working with custom reports, custom tables and segments.

Scope Types

Here are four ways that Google Analytics uses to scope dimensions and metrics:

1. User: aggregation of sessions of the user and all hits (pageviews) collected.
2. Session: aggregation of hits of a user during one session.
3. Hit: captures each hit or pageview during one session.
4. Product: only applies to websites where Enhanced Ecommerce is enabled. Shows product related data.

Let’s dive into each of these in more detail now.

User Scope

User-level data is collected in Google Analytics with the so-called Client ID. Be aware that in many cases the Client ID changes (or is invisible):

• Automatic deletion of cookies on Safari (ITP).
• Clearing cookies.
• Using different browsers.
• Using different devices.
• Using incognito / private browsing.

Cookies are aligned with browsers and not (always) people.

Example:

“You browse the Google Merchandise Store on Wednesday by iPhone, but decide to buy on Desktop a day later.”

In that case Google Analytics will record two users and two sessions.

However, in general you could say that all metrics and dimensions related to the user journey are “scoped” at the user level.

User – dimension examples

• User Type
• Count of Sessions
• Days Since Last Session

User – metric examples

• Users
• New Users
• Number of Sessions per User

Session Scope

Your session relates to one single visit to the website. In this section Google Analytics monitors your hits (events, pageviews, transactions).

This is completely different from the user journey as it only refers to one of the sessions of a user.

Session – dimension examples

• Source
• Medium
• Session Duration (Bucket)

Session – metric examples

• Bounces
• Bounce Rate
• Session Duration

Hit Scope

Any page you explore or any interaction that you trigger during a user journey is registered as a hit.

Hits are made up of pageviews, events and transactions.

Hit – dimension examples

• Page
• Hostname
• Event Action

Hit – metric examples

• Pageview
• Time on Page
• Unique Events

Product Scope

Product scoped dimensions and metrics only apply to websites that have implemented Enhanced Ecommerce. In most cases they refer to the purchase process on your website.

Product – dimension examples

• Product SKU
• Product Category
• Product Variant

Product – metric examples

• Product Detail Views
• Product Revenue
• Product Refunds

Custom Dimensions and Metrics

Until now we have only discussed metrics and dimensions that are readily available in Google Analytics.

The great thing is that you can extend your set of standard metrics and dimensions with a lot of custom ones.

• Google Analytics free version: 20 custom metrics and 20 custom dimensions.
• Google Analytics 360: 200 custom metrics and 200 custom dimensions.

You can enrich your Google Analytics data with a lot of other data points.

I have written two articles on these topics I recommend to check out.

• The Complete Guide to Leverage Custom Metrics in Google Analytics.
• Step-by-Step Guide to Leverage Custom Dimensions in Google Analytics.

It’s very important to consider the scope when implementing each of your custom dimensions.

This list will get you started right away!

Custom metrics come with just two “scopes”:

• Hit-level.
• Product-level.

I hope by now you understand the logic and thoughts behind scope in Google Analytics. Let’s dive into common mistakes when combining metrics and dimensions in Google Analytics.

Common Mistakes

Here are three examples of scope issues I have often come across when consulting clients.

# 1: Page and Sessions

The dimension “page” has a hit level scope combined with the “sessions” metric (session level scope).

When showing sessions by page, it’s actually showing by landing pages.

Page and Sessions

Landing Page and Sessions

It’s a bit confusing as Google Analytics still displays data when combining “Page” and “Sessions”. It would be great if they warn you in the future when combining an invalid set of metrics and dimensions.

# 2: User, Session and Hit Level Segments

This one is probably the most difficult to understand, but very important when working with Google Analytics data.

In Google Analytics you can set up session- and user-level segments. In addition, with a few tweaks you can define a hit-scoped segment as well!

Here are three examples with a quick description beneath each of those.

User: Pro Customers

• Pro Customers segment, set at the user level.
• Apply this segment to one of the reports to analyze Pro Customers’ behaviour across all sessions in the selected time period.

Session: Query Length Segment

• Query Length segment, set at the session level.
• Apply this segment to one of the reports to analyze visitors’ behaviour on AdWords sessions generated by two word queries.

Hit: Enhanced Ecommerce – Add to Cart – Segment

• Add to Cart segment, set at the hit (and session) level.
• Apply this segment to one of the reports to analyze visitors’ behaviour on sessions where Google Sunglasses are added to cart.

Sequential segments allow you to configure hit-level segments within a session or at the user level, incredibly powerful and not often used!

Concluding Thoughts

By now you should have a better understanding of the importance of scope and how you can apply it to your situation and data. And understanding how Google Analytics collects, processes and presents data is a crucial skill.

Misinterpreting or incorrectly configuring scope on your custom dimensions and metrics negatively impacts your reports and analyses.

It wouldn’t be the first time that someone misinterpretes the data and takes the wrong business decision because of scope issues.

Did you already consider scope when working with Google Analytics or is this concept relatively new to you? Happy to hear your comments!

The post Google Analytics Scope: You Don’t Want to Ignore It! appeared first on Online Metrics.

On default, Google Analytics applies the last non-direct click attribution model to the standard reports section. You can use the Model Comparison Tool and Multi-Channel reports to learn a bit more about channel roles, but still your options and data set are rather limited.

What if you want to learn more about original traffic sources?

In this blogpost we will explore the background and actual analysis behind this key question for marketers.

Table of Content

• Pageviews, Sessions and Users
• Segmentation in Google Analytics
• Returning Users Definition
• Returning Users Segment
• Exploring the Data
• Going Beyond
• Limitations
• Concluding Thoughts

Let’s start with the basics before we will dive into the Google Analytics data analysis.

Pageviews, Sessions and Users

In Google Analytics, there are a few rules you need to understand:

• Each user is connected to one or more sessions.
• Each session consists (in 99% of the cases) of one or more pageviews.
• Two common scenarios that lead to a session to expire:
  • 30 Minutes of browser inactivity.
  • On midnight (based on configuration settings).

Further, keep in mind:

• GA’s measurement model is primarily based on cookies and cookies are not shared across different browsers and devices.
• Also, a user (website visitor) can decide to delete her cookies.
• Intelligent Tracking Prevention makes performing a user analysis (on Mobile) more challenging.

These are important limitations to keep in mind when you want to derive the original traffic source either via the existing GA data or via enriching your data with an initial traffic source Custom Dimension.

Last thing, we will focus our analysis on the “users”  metric as we are interested in exploring the original traffic source of a user.

Segmentation in Google Analytics

I can’t stress enough how important segmentation is. In my opinion, segments are a key Google Analytics feature and crucial to understand for everybody working in Digital Marketing.

Read this blogpost if you want to learn more about segments in Google Analytics.

The question – “Where did my returning visitors originally come from?” – can only be answered after applying the right set of segments.

Here is an overview of unsegmented data of the Google Analytics Demo Account (Q2, 2019):

Organic Search is clearly the most important driver of traffic and sales for the Google Analytics Merchandise Store.

A few numbers in relation to Organic Search that we can derive from this report:

• 57% of users.
• 55% of sessions.
• 59% of revenue.
• 65% of transactions.

Returning Users Definition

What is a “returning user” in Google Analytics?

From experience I can say that some terminology used in Google Analytics can be confusing.

A “returning user” is identified by the “Session Count” parameter (_s) in Google Analytics.

In the example above it is my first session and I will be recognised as a new user in Google Analytics.

As long as I revisit the website within 30 minutes my session count remains “1”. However, when I visit the website after 30 minutes, the new ” session count”  value in the cookie becomes “2”, which means in my example “_s=2”.

The definition of a “returning user” is based on that number. The session will be attributed to a “returning user” if “session count” is greater than 1.

Returning Users Segment

Now we have the information that we need to set up the segment for our analysis.

Note: the standard “Returning Users” segment doesn’t work as you can’t use it together with the “session count” dimenion.

Here we go:

• Count of Sessions > 1 -> Returning User.
• 23% Users fall in this segment.
• They are good for 44% of all sessions.

Exploring the Data

Let’s apply this segment to the same report (Q2, 2019):

As expected, Organic Search is still on top, but numbers have changed.

We need to do two more things to focus on the ” original traffic source”:

• Apply “Count of Sessions” as a secondary dimension.
• Use an advanced filter to set “Count of Sessions” to 1.

And here is the report that answers our initial question!

As you can see, there are small deviations (less than 1%) between “user” and “session” numbers. No worries, this doesn’t impact our analysis and findings. You can either use the “user” or “session” dimension.

We work with “users” here and can derive where the “user” came from in their first session. Let’s aggregate these findings with the first “traffic sources” overview.

The “orange” bar displays the traffic source that was responsible for attracting a returning user to the site. You can use this information to find out what works in terms of acquiring visitors to your site.

Social – although percentages are very low – doesn’t seem to bring a lot of returning users for the Demo Store, just one-time visits.

Going Beyond

There are many ways to make this analysis even more useful.

Let’s quickly discuss three ways to extend this analysis.

#1: Segment on Most Loyal Customers

There are many ways to define a loyal customer. In this context it relates to “Count of Sessions” of a particular user.

We could make a similar analysis, but now we only want to include returning users with at least four sessions:

As expected, the number of users that fall in this segment is greatly reduced (6%).

So be mindful and don’t analyze a segment that is simply too small to provide any meaning.

Here is the corresponding data table:

Both the number of users and revenue that falls in this segment are very low.

#2: Make a Revenue Analysis

We already touched upon the revenue metric above, but if you wish you can extend your analysis to include revenue metrics as well and answer questions like:

• Does the revenue share across traffic sources deviate if we look at the “original traffic source” compared to all data?
• Do visitors that return more often to the site actually buy more often or simply need more sessions to convert?
• What is the difference in purchase behaviour of visitors returning a few times to the site compared to those that return 10 times or more?

Make sure to define your business question first before diving into segmentation and data analysis.

#3: Pass the Original Traffic Source into GA

The solution presented in this post doesn’t require you to make any modifications to your Google Analytics tracking implementation.

I came across another way to actually enhance your data with the original traffic source, tracked through GTM with the help of a Custom Dimension.

Keep in mind that you need to be familiar with GTM to get this up and running.

Limitations

Limitations are a fact of life when working on any piece of data analysis.

You can both identify the original traffic source through your current data or by enhancing your data with a Custom Dimension through GTM. But there are certainly limitations you should know about.

1. Cookie Solution isn’t Always Reliable

Keep in mind that returning users and the original traffic source of a user are never 100% accurate (far from that) because of:

• Automatic deletion of cookies on Safari (ITP).
• Clearing cookies.
• Using different browsers.
• Using different devices.
• Using incognito / private browsing.

2. Limitation of 93 Days of Data

Also, a segmented user-level analysis in GA is limited to a period of 93 days.

It means that within Google Analytics, you cannot retrieve the original traffic source more than three months back of a user that visits your site today.

3. Sampling and Data Accuracy

Google Analytics sampling can be a pain when applying (user-level) segments.

Websites with a few thousands visitors a month are usually not affected by sampling.

However, if you work with one of those high-traffic websites, you should certainly take sampling into account when performing an analysis.

4. Retroactive Analysis is not Possible

Last thing to note is that inserting the original traffic source doesn’t provide you with retroactive insights. In other words, this solution works from the moment it is successfully implemented.

However, you can get around this if you decide to work with the data already available in Google Analytics.

Concluding Thoughts

Both deriving the original traffic source through your current data as well as setting it up via a Custom Dimension and GTM can be beneficial.

It can provide you with a lot of additional insights and feed your marketing strategy.

Keep in mind that (especially) with a segmented user-level analysis, there are limitations. Google Analytics is not yet capable of accurately collecting user stats in most cases.

Now it’s your turn!

What are your thoughts on capturing the initial traffic source? Did you already put it into practice on your website?

The post How to Smartly Identify the Original Traffic Source in Google Analytics appeared first on Online Metrics.

Back in 2017, Intelligent Tracking Prevention (ITP) was introduced by Apple. Until earlier this year, most companies didn’t worry so much as ITP was primarily aimed at blocking third-party cookies. However, with version 2.1 and 2.2 being released, the game has definitely changed.

The working of first-party cookies is now impacted as well.

In this blogpost you will learn about the background of ITP and what impact Intelligent Tracking Prevention might have on your Google Analytics data.

Table of Contents

• What is Intelligent Tracking Prevention
• First-Party and Third-Party Cookies
• Intelligent Tracking Prevention (version 1.0 to 2.2)
• Google Analytics Impact Analysis
• Workarounds
• Concluding Thoughts

I don’t recommend doing so, but skip chapter one to three if you are only interested in the practical implications of ITP.

What is Intelligent Tracking Prevention

Intelligent Tracking Prevention (ITP) is a (relatively) new feature introduced by WebKit in 2017. It’s an open-source browser engine that powers Safari’s web browsers and reduces cross-site tracking by further limiting cookies and other website data.

In short, the feature aims to further protect user’s online privacy by changing how Apple Safari’s browsers deal with cookies. That’s at least what they say. ;-)

First-Party and Third-Party Cookies

Let’s get clear on first-party vs third-party cookies first before diving into the latest developments.

First of all both cookies are different from each other in the way they are created and used. They are the same type of files.

First-Party Cookies

First-party cookies are created by the domain the user is visiting, e.g. in my case Online-Metrics.com. These types of cookies are generally considered safe as they help provide a better user experience. Also, they keep the session open which is very important. In that case, the browser can remember important information, e.g. the status of shopping carts, usernames and passwords, and many more.

Third-Party Cookies

Third-party cookies are cookies created by domains other than the domain the user is visiting. So those are not from Online-Metrics.com in this case. There are many different types of these cookies, a few common ones are listed below:

• Retargeting cookies.
• Social Media buttons.
• Chat Popups.

Three Key Differences

1. First-party cookies are set by the publisher’s web server or loaded JavaScript on the site. Third-party cookies can be set by a third-party server by specific code loaded on the website.
2. First-party cookies are only accessible via the domain that initially created it. Third-party cookies are accessible on any website by loading the third-party server’s code.
3. First-party cookies are supported by all browsers, but the user can block or delete those cookies (which might lead to a bad user experience). Third-party cookies are supported by all browsers, but many browsers (including Safari) are blocking the creation of those cookies on default.

ITP 1.0 and 1.1

As mentioned in the introduction, the first version of ITP was released back in June 2017. There was some buzz at that time, but less companies where impacted as the focus was primarily on blocking third-party cookies.

It certainly impacted a wide range of AdTech companies and how they could built user profiles across multiple websites through third-party cookies. However, the majority of companies – also in relation to using Analytics data – were safe. Simply because those solutions rely on first-party cookies.

ITP 2.0

Since the release of ITP 2.0 (June 2018), third-party cookies are fully blocked on default. The 24-hour grace period (still available in ITP 1.0 and 1.1) was scrapped.

It means that websites can no longer leave cookies in the user’s browser, e.g. for retargeting and attribution purposes. Companies accessing their first-party cookies in a third-party context are clearly impacted.

ITP 2.1

In February 2019, ITP 2.1 was released. Most important change in the update was decreasing the first-party cookie expiration time.

Cookies created via the JavaScript document.cookie API (even first-party cookies) are set to expire in seven days, regardless of their existing expiry date.

This can have a big impact on many companies using Marketing Technology tools, including Google Analytics. The first-party cookies are set via the GA JavaScript library and those cookies will expire after seven days, unless the cookie is updated in that period.

The default expiration period of analytics.js is 2 years, so this is huge!

Apple stated earlier this year that the beta releases of iOS 12.2 and Safari 12.1 on macOS High Sierra and Mojave include an updated version 2.1 of Intelligent Tracking Prevention (ITP).

“A user on Safari visiting your website on Day 1 and returning on Day 9 will be seen as a new user.”

ITP 2.2

Now let’s discuss where we currently are, ITP 2.2. Here is the official release article on WebKit.

It was released around May 13 on iOS 12.3.

ITP 2.2 further reduces this cookie life (7 days on ITP 2.1) to just 1 day if the following two conditions are true:

1. A domain classified with cross-site tracking capabilities was responsible for sending the user to the current webpage (large ad networks are most probably classified this way, Google and Facebook included).
2. The final URL of the navigation has a query string and/or a fragment identifier.

“A user on Safari visiting your website on Day 1 and returning on Day 3 will be seen as a new user if both conditions are met.”

Make sure to read all these privacy related blogposts on WebKit if you want to know about all the technical details of ITP. Let’s focus now on the actual impact on your data in Google Analytics.

Google Analytics Impact Analysis

In the last few months I have had an extensive look at ITP and the impact it has on the stats in Google Analytics.

Now we will look into one analysis I recommend replicating on your data. For demonstrating purposes I will use the GA Demo account.

Prerequisites

• Ecommerce website: Ecommerce transaction and/or revenue tracking.
• Non-Ecommerce website: goal tracking for your primary conversion.

In general, always make sure you have an accurate setup of Google Analytics.

Make a note of the release dates of ITP 2.1 and 2.2:

• Release date ITP 2.1 – March 11, 2019 (verified in several GA accounts).
• Release date ITP 2.2 – May 13, 2019 (verified in several GA accounts).

You will need these dates a bit later.

Impact Analysis – ITP 2.1

The Google Demo Store is an Ecommerce site with Enhanced Ecommerce tracking. That’s why I can directly use the “revenue” metric for this impact analysis.

Step 1: Calculate traffic and revenue from Safari users.

You want to make this analysis on your most recent data and aim for a minimum of 100 to 200 transactions within this group.

Even if I use four full weeks of data (June 11 to July 8, 2019), the number of transactions is below 100. It’s ok as this is just for demonstrating purposes.

Navigate to: Audience > Technology > Browser & OS.

Filter on Safari and make a note of the stats:

• Users: 16%
• Sessions: 14%
• Revenue: 79% (!)

This is huge as “revenue” is five times higher as number of “users”.

Step 2.1: Define ITP 2.1 (2.2) segment in Google Analytics.

Here is the segment you need to create:

Keep in mind that you might need to upgrade the “Browser Version” in the future to reflect any changes to ITP and its impact.

Step 2.2: Determine Upgrade Percentage.

Now it’s time to find out what percentage of users have already upgraded to the browser version(s) that are impacted by ITP.

Therefore we need to apply both the default segment as well as the custom one. In this case I use the time period: July 1 to July 7, 2019.

The data shows that 74% of all users and 76% of all sessions are affected by ITP.

• Users (74%): ((1,386 / 1,864)*100%).
• Sessions (76%): ((1,731 / 2,267)*100%).

Step 3: Determine Revenue Percentage after more than 7 days.

• Time Period (prior to release ITP 2.1): 10 January to 10 March, 2019.
• Time Lag report (Conversions > Multi-Channel Funnels > Time Lag).
• Conversion = Transaction.
• Look-back window defaults to 30 days.

Revenue after more than 7 days = Conversion Value = 0.78% + 1.62% + 8.66% = 11%. This is very low as many visitors convert within a very short time frame.

Extending the look-back window to 90 days would increase this percentage to 15%, which is still very low.

For this analysis we will use the highest value (15%), which illustrates maximum impact based on highest look-back window in GA.

Step 4: Calculate impact on revenue.

This is the most important step and you need to be careful to use the right numbers derived in the previous steps.

• Safari browsers are good for 79% of total revenue.
• 74% of this site’s Safari users have upgraded to ITP 2.1. Old devices and legacy iOS versions will prevent this number from getting to 100%.
• 79% (total site revenue) X 74% (ITP 2.1 Safari users) = 58%.
• Thus, 58% of current revenue is coming from ITP 2.1 Safari users.
• 15% of revenue happens after 7 days (Time Lag report).
• 58% (revenue from Safari ITP 2.1 users) X 15% = 9%.
• Approximately 9% of revenue in GA (that is coming in after 7 days in Safari) may not be allocated to the right channels.

These factors determine the eventual impact:

• Percentage of users that have upgraded to a browser version impacted by ITP. // higher value means higher impact
• Percentage of revenue on Safari. // higher value means higher impact
• Percentage of revenue coming in after 7 days in Safari. // higher value means higher impact

Impact Analysis – ITP 2.2

We can do this analysis much quicker as we can re-use the data from steps 1 and 2 described above.

Data from step 1 and 2:

• Safari browsers = 79% of total revenue.
• 74% of this site’s Safari users have upgraded to ITP 2.2.
• Thus, 58% of current revenue is coming from ITP 2.2 Safari users.

Step 3: Determine Revenue Percentage after more than one day.

• Time Period (prior to release ITP 2.1): 10 January to 10 March, 2019.
• Time Lag report (Conversions > Multi-Channel Funnels > Time Lag).
• Conversion = Transaction.
• Look-back window defaults to 30 days.

Revenue after more than one day = Conversion Value = 100% – 74.30% – 8.52% = 17%. This is still relatively low as many visitors convert within a very short time frame (less than a day).

Extending the look-back window to 90 days would increase this percentage to 20%.

For this analysis we will use the highest value (20%), which illustrates maximum impact based on highest look-back window in GA. And, we assume that all sites sending traffic fall under the guidelines of ITP 2.2.

Step 4: Calculate impact on revenue.

This is the most important step and you need to be careful to use the right numbers derived in the previous steps.

• Safari browsers are good for 79% of total revenue.
• 74% of this site’s Safari users have upgraded to ITP 2.2. Old devices and legacy iOS versions will prevent this number from getting to 100%.
• 79% (total site revenue) X 74% (ITP 2.2 Safari users) = 58%.
• Thus, 58% of current revenue is coming from ITP 2.2 Safari users.
• 20% of revenue happens after one day (Time Lag report).
• 58% (revenue from Safari ITP 2.2 users) X 20% = 12%.
• Approximately 12% of revenue in GA (that is coming in after one day in Safari) may not be allocated to the right channels.

Based on both scenarios, the potential misallocation of revenue increases from 9 to 12%. Please keep in mind these are all estimations.

Workarounds

A lot has been said or written about possible workarounds and solutions to counter-effect the implications of ITP.

Ranging from simple client-side solutions to complicated server-side efforts.

• Any easy way of sidestepping ITP is likely not working anymore after the next update.
• Workarounds are both time-consuming and expensive to implement.
• Some suggested solutions – such as 302 redirects to pages without parameters – are detrimental to the company and in this case might negatively impact SEO.

Investing into cookie-less solutions is likely to be more beneficial.

Notes:

• Cookie Saver – not yet tried – seems like a very promising solution.
• TraceDock (company in The Netherlands) is working on solutions mitigating the effects of ITP.
• The Google Analytics evolution into Web and App properties doesn’t provide a solution for ITP (yet).

Concluding Thoughts

This case study illustrates a potential, negative impact on Google’s Demo Store’s data because of large share of Mobile/Safari users and revenue.

This is partly negated because of high percentage of users converting within a short timeframe. Currently – as a rough estimate – 9 to 12% of all revenue might be improperly attributed (wrong channel). Eventually, this might rise to 15% of even higher if Mobile share increases over time. And what about changes to Firefox or within the Google sphere? All not yet fully clear.

Channel performance (outside of Direct) is most probably better than what the Google Analytics data is currently showing. Google could consider lowering channel-specific targets.

Here are, once again, three important factors that determine the eventual impact of ITP (higher values indicate increased impact):

• Percentage of users that have upgraded to a browser version impacted by ITP.
• Percentage of revenue on Safari.
• Percentage of revenue coming in after one or seven days in Safari.

Last thing, there are a ton of other analysis you could potentially make in relation to ITP. I just wanted to share an important one related to revenue and macro conversion impact.

Let’s see what the future brings!

Now it’s your turn. What are your thoughts on Intelligent Tracking Prevention? Did you already analyze the effect on your GA data set and how do you cope with it?

The post The Impact of Intelligent Tracking Prevention on Your Google Analytics Data appeared first on Online Metrics.

After auditing hundreds of Google Analytics setups, I have discovered a lot of reasons why your “pageview count” might be wrong.

Google Analytics pageviews have a tremendous impact on all kind of reports and metrics so you want to get this right.

This new post reveals some of the most common problems with collecting pageview data and how to resolve them.

Let’s dive right in!

1. Not Yet Migrated to the Latest GA Version

Most of you probably use Google Tag Manager to deploy Google Analytics on your site.

The majority of companies are on the latest GA version when they do so, but there is no guarantee.

Last week I stumbled upon this GTM implementation:

In this case you need to upgrade to Universal Analytics as soon as you can.

In short, upgrade to Universal Analytics via Google Tag Manager and your pageview count will become more accurate and reliable.

2. Missing Google Analytics Tracking Code

It wouldn’t be the first time that the Google Analytics tracking code is not implemented on one or more pages of a website.

Some companies work with different templates or deal with an advanced implementation across multiple domains. This is where measurements can easily go wrong.

There are several tools on the market that can reveal untagged pages.

GA Checker is a good, free option if you run a website with a small amount of pages.

On the other hand, Screaming Frog SEO Spider is great (paid) tool as well.

Here are five steps to find a missing tracking code issue via Screaming Frog:

Step 1: download and install Screaming Frog.

Step 2: add your domain and navigate to Configuration >> Custom >> Search.

Step 3: create two filters (either based on UA- (hardcoded) or GTM- (GTM)).

It might take some time as Screaming Frog crawls all your pages.

Step 4: hit the “Start” button.

Step 5: 100% indicator confirms the full crawl is done. Head over to the “Custom” section to review the results.

On the page shown above Google Analytics is implemented, but not via GTM.

I recommend only getting a copy of Screaming Frog if you want to leverage it for SEO checks and optimizations as well.

3. Wrong Location on the Page

How to implement the Google Analytics tracking code depends on the type of implementation that you use.

Let’s look into the Universal Analytics implementation, both via Google Tag Manager and hardcoded.

• Google Tag Manager
  • <script> tag: paste this code as high in the <head> of the page as possible.
  • <noscript>tag: paste this code immediately after the opening <body> tag.
• Google Analytics hardcoded
  • Implement the hardcoded GA script just before the closing </head> tag.

Get this wrong and you risk to inaccurately measure pageviews in Google Analytics.

4. Duplicate Tracking Codes

Also, a common reason for inaccurate pageview measurements is duplicate tracking codes on one or more pages.

There are several tools to debug this issue (Real-Time reporting, Google Tag Assistant). Another way to demystify this issue is by looking at the bounce rate in Google Analytics.

• You will see a very low bounce rate at the aggregated level if you have a site-wide issue.
• It is harder to spot if this issue is only present on a few pages of your website, but you can filter on pages with a substantial amount of pageviews (e.g. > 100) and bounce rate lower than 5 or 10%. This will help finding the issue if present.

Note: the bounce rate might also be impacted because of (automatic) interaction events firing on the first pageview. This causes a huge drop as well.

Here you can learn more about Google Tag Assistant and how to install it on your device.

5. Virtual Pageviews Instead of Events

Events and virtual pageviews are two methods to track certain data points on your website.

In general, you want to use Event Tracking to keep track of interactions on your website.

So don’t use virtual pageviews for scroll tracking, clicks on a banner etc.

Virtual pageviews are great when you want to measure the checkout process on an ecommerce site (and there are no unique urls).

Misusing virtual pageviews can definitely impact the accuracy of your pageview count.

Need some tech help with setting up virtual pageviews? Watch the video below to get started:

6. Wrong Filters in Google Analytics

Google Analytics filters are among the most powerful features. I use them on my own website and for all my clients to enhance the accuracy and usefulness of the data collected.

However, not using them correctly can leave you with inaccurate Google Analytics data.

I recommend starting with segments first if you are rather new to Google Analytics. This as segments cannot impact the data you collect in Google Analytics (filters do!).

When using filters, be especially mindful with “includes” and “excludes”. But also other filters can potentially harm your data. This is why you always want to set up a Raw Data View without any filters or other data modifications. And test them first before applying them to you main (Master) view.

7. Query Parameters

It is very common that a website carries query parameters in the URL.

Let’s look at an example below:

• https://shop.googlemerchandisestore.com/asearch.html?vid=20160512512&key=shirt&keyword=shirt
  • vid = first query parameter
  • key = second query parameter
  • keyword = third query parameter

There are two types of query parameters:

• Technical query parameters (often seen on ecommerce sites like “vid” in the example above).
• Marketing or analysis query parameters (important for analyzing your data).

I recommend removing all the technical query parameters in Google Analytics so that no duplicate URLs are stored in Google Analytics.

You can remove query parameters:

• At the property level (via GTM) // good solution to deal with PII in Google Analytics
• At the view level (with a Google Analytics filter or via the admin view settings)

Simply add all query parameters to the “Exclude URL Query Parameters” field and separate them with a comma.

One more tip, search on the Regular Expression “\? ” in the All Pages report (under Behaviour > Site Content > All Pages):

In the example above almost all query parameters have already been filtered out, but usually this is great start for solving any Google Analytics query parameter issues on your site.

Your pages and pageviews per page will become much more accurate after you finish this exercise.

8. Default Page

The Default Page in Google Analytics is one of the settings where people often make mistakes.

Incorrect use of the default page feature can cause pages to be split up in your data or cause other page related issues in the All Pages report. Find out whether you need to use this feature (most often you don’t need to use it) and be consistent.

Read this article to learn more about this feature.

9. Iframes

Some years ago, iframes were very common, but nowadays they are used less often (phewwwww :-)).

It can be a huge pain to get Google Analytics implemented in an iframe and this can easily lead to measurement issues.

Thoroughly check your implementation if you rely on using iframes on your site. I have seen many examples where two or even more pageviews are fired on one page in the iframe.

Read this article to get started with properly tracking iframes in Google Analytics:

• Tracking Cross-domain Iframes – Upgraded Solution.

10. Ajax Technology

Websites that use Ajax technology can improve the user experience, but at the same time cause inaccurate pageview measurements.

The page content refreshes without an actual reload in your browser. You might want to measure this action with a new pageview, but this doesn’t happen (correctly) on default.

Additional, modified tracking is needed to measure these Ajax events as additional pageviews. With GTM, you might be able to implement this by yourself or with the help of a few tricks from your web developer.

11. Meta Refreshes

Other sites refresh the page without you doing anything. This reload leads to an additional pageview in Google Analytics.

This heavily impacts the total number of pageviews measured, the bounce rate and tons of other stats in your Google Analytics account.

A simple solution for this is to ensure that the Google Analytics tag fires only once per page. In GTM you can set this under the “Tag firing options” shown below:

We have come to the end of the list.

Potentially, there are a ton of more things that can go wrong on the pageview measurement side in Google Analytics.

Hopefully you understand that implementing Google Analytics is not as easy as it seems. You really need to know what you are doing: every website is unique and needs a tailor-made implementation and configuration.

Thoroughly testing your implementation is the key to collecting reliable and actionable data and insights.

What are your thoughts? Happy to hear your take on this!

The post 11 Reasons Why Your Google Analytics Pageview Tracking Might be Inaccurate appeared first on Online Metrics.