Alex is the director of digital marketing for Manitobah Mukluks, Canada’s fastest growing footwear brand, where he helps the luxury winter boot manufacturer accelerate growth profitably via digital marketing. Alex has over 20 years experience in e-commerce and digital marketing.
Below is a list of the Essential Google Audiences the every e-commerce site should create in their Google Ads Account. Even if you don’t plan on using them right away, creating them now will ensure that they can grow so they are ready to be used in the future.
Google’s Built-in Audiences
Before we create our own audiences, we first need to install the Google Ads Dynamic Remarketing Code. This triggers Google to automatically create some built-in Remarketing Audiences:
Shopping cart abandoners
People who added products to the shopping cart in the past 30 days but did not complete the purchase
People who viewed specific product pages on your site in the past 30 days but did not create a shopping cart
People who purchased products from you in the past 30 days
People who visited pages that contain your remarketing tags in the past 30 days
People who converted on your site in the last 180 days. Based on your conversion tracking tag. This is not necessarily people who have purchased from you, but anyone who has triggered a “conversion”. (eg: Phone call from an ad)
People who visited your website in the past 30 days but did not view any specific products
The additional audiences to create follow the same pattern used in Google’s built-in audiences, but with expanded membership durations. The main focus is on Shopping Cart Abandoners, Product Viewers, and Past Buyers.
Shopping Cart Abandoners
Google will have already created a “Shopping cart abandoners” audience with 30 day time window. We will create the following additional audiences for 7, 14, 90, and 180 day durations:
Shopping cart abandoners: xd “x” will be the duration. Eg: Shopping cart abandoners: 7d
Visitors of a page who did not visit another page
URL contains cart
URL contains thank_you
7, 14, 90, 180 days * 30 day duration is already created by Google
Google will have already created a “Past buyers” audience with 30 day time window. We will create these additional audiences for 14, 90, 180, 365, and 520 day durations:
Past buyers xd “x” will be the duration. Eg: Past buyers: 14d
Visitors of a page with specific tags
14, 90, 180, 365, 520 days
Google will have already created a “Product viewers” audience with a 30 day duration. We will create additional audiences with 14, 90, 180, 365, and 520 day durations.
Product viewers: xd “x” will be the duration. Eg: Product Viewers: 14d
Visitors of a page who did not visit another page
URL contains product
URL contains cart
14, 90, 180, 365, 520 days * 30 day duration is already created by Google
The Membership Durations are somewhat arbitrary. You can get more or less granular, and set your own intervals. It is however best to start with something simple and add granularity later on.
A Remarketing Audience needs a minimum of 1,000 members to be eligible to serve. When creating your audience durations, consider how much time it will take to reach 1,000 memebers. eg: How long will it take before your site generates 1,000 abandoned carts, or 1,000 purchases? That will probably be the shortest duration with which you should start.
You should generally add all these lists as “Observations” to all your campaigns.
Once a week (or sometimes daily if we are in a period of high activity or if we have launched a new promotion), first thing in the morning, I look at the past 7 days and run the above formula for each of my campaigns.
If my target ROAS is very close to my actual ROAS, then I don’t bother with the calculation, nor with adjusting budgets.
If the results call for a decrease, then I decrease the budgets.
If the results call for an increase, then I increase budgets, but usually by no more than 10%.
Remember that we are working with a Remarketing campaign. We are targeting customers that are aware of your brand. Customers who have already visited your site. In many cases, customers that have added a product to their cart and are very close to purchase.
Consider this scenario: A long time customer visits your site, quickly finds the product he is looking for and starts the checkout process. In the middle of checkout, he receives an e-mail from a friend. He opens the e-mail and clicks on a link. That link takes him to a news site to read an article. Since he is in your remarketing audience, that news site shows him an ad and records a “View”. The customer finishes reading the article and returns to your site to complete his purchase. Your remarketing campaign has now taken credit for that sale because an ad was displayed to this customer.
If your customer visits any webpage during the purchase process, or if they have another browser tab open, they will likely be exposed to an ad view. That ad view will get View-Through credit for that conversion.
The number of View Through Conversions recorded in a remarketing campaign can be more than ten times that of Click Through Conversions. It is no wonder that remarketing platforms and ad agencies are eager to include View Through Conversions in ROI calculations. If you include View Through Conversions you will see fantastic performance results. This will cause you to double or triple your budget. It’s easy money for them.
Here is what a fictional remarketing campaign with a typical distribution of view vs click conversions might look like:
If we use Click Conversions to calculate performance, we lose $0.17 for every $1.00 in ad spend. We have a negative ROI. The campaign is not profitable. We need to DECREASE our bids and budget.
If we add View-Through Conversions to our calculation, we come to a totally different conclusion: The campaign is now performing fantastically well. We profit $6.50 for every $1.00 in ad spend. That’s a 650% ROI! The campaign is super profitable. We should INCREASE our bids and budgets. This is exactly what your remarketing platform wants you to do, but is exactly the OPPOSITE of what you should do.
The (unfortunate) reality is that the 400 view-through sales above were not a result of your remarketing campaign. The ad display is simply poaching attribution from your other channels.
Note: View Conversion metrics are valuable and useful in many scenarios. However, in the context of a sales driven remarketing campaign they should be excluded.
Cookie Bombing and Fake Impressions
Compounding the problem of View Through Conversions are fraudulent ad impressions and cookie bombing.
Over 50% of Display Ads never actually appear on the user’s screen. They are fraudulently served in a hidden iframe, or are stacked underneath other ads, or are compressed into a 1×1 pixel image, etc… These hidden fraudulent ad impressions still record a “View” in the user’s remarketing cookie. When that user eventually purchases on your site, that invisible fraudulent ad gets credit for the purchase.
If a particular ad network’s website can generate many conversions for advertisers, then those advertisers will want to spend more with that website. Fraudulent sites engage in “cookie bombing” to maximize the number of conversions they can “capture”. They serve as many ads to as many unique users as possible, so that as many users as possible have their cookie. The more users have their cookies, the more conversions will be credited to their sites, and the more ad budget money they will be paid.
To be clear, these sites are not actually serving any visible ads to any users. They are generating fraudulent ad impressions to set cookies on as many users as possible in order to “poach” conversion attribution (and budget) from your other channels. If a user with their cookie ever makes a purchase in the future, that site will get a view-attribution for that purchase, causing more ad budget money to flow to them.
All these hidden impressions distributed to as many users as possible cause View Through Conversion metrics to skyrocket. As mentioned before, View Through Conversions reported by an ad platform can easily be more than 10 times the number of Click Through Conversion reported. It is no surprise that ad platforms want you to believe in and count View Through Conversions.
New Attribution Models: View Throughs Disguised as Click Throughs
The simple way for an ad platform to fool you into counting View Through Conversions is to simply have a single “Conversions” column and lump all conversions in there. The platforms are however getting more sophisticated at transforming view conversions into click conversions, using new attribution models as a guise:
YouTube TrueView Conversion is any conversion that occurs after the user has watched 30 seconds of a video. Even if no click happened. TrueView conversions get counted in the Conversion column in AdWords along with the regular Click Through conversions. This makes it seem like an actual click happened, when none did. What makes this even more confusing and misleading, is that YouTube will still report View Conversions in a separate column. Since there are two separate columns, one tracking View Throughs explicitly, you will assume that the Conversions column must only contain clicks. This is not the case, and if not careful, you will significantly overspend on your YouTube remarketing campaign.
Steelhouse’s “Verified Visit” model counts any View-Through visit to your site within 1 hour of seeing an ad as a “Verified Visit”. Any conversion that happens within 30 days of this “Verified Visit” is counted as a Click Conversion (even though, no click ever occurred). Steelhouse will skirt the View Through question by saying that they only count conversions from “Verified Visits”. This will mislead you to think that they only count Click Through Conversions. But that is incorrect. The stats they report definitely include View conversions, which inflate your ROI, and cause you to overspend.
I spent months believing that SteelHouse’s numbers were based solely on clicks and post-click conversions. SteelHouse’s conduct made it appear to perform better than it actually was performing. Toms made spending allocation decisions and marketing vendor choices based on SteelHouse’s inflated performance.
How to protect yourself from View Through Conversion “fraud”?
Insist that your platform report on Click Through and View Through Conversions in two separate columns. It is OK to include View Conversions in a report if they are in a separate column. It is NOT OK to combine View Conversions with Click Conversions in the same column.
Validate all data with your site analytics. Your analytics platform should report the actual accurate click through conversion data. Always compare the reports your remarketing platform generates with your own analytics data.
Don’t run Conversion focused YouTube remarketing campaigns. There is currently no way to opt-out of the TrueView model, therefor your conversion column will be overstated. Read more about TrueView…
Don’t use Steelhouse. Their Verified Visit model is designed to fool you. They also use their tracking pixel to inject data into your analytics. Scary stuff. Avoid.
Don’t use Criteo. They won’t tell you where your ads are displayed and their network has much more fraudulent sites participating in cookie bombing than anyone else. Avoid.
Fraudulent Automated Clicks
Fake clicks are an evolution of Cookie Bombing, except in this case clicks are auto generated onto the invisible ads, sending invisible visits to your site, and claiming Click credit for your purchases.
How Fake Clicks Work
A legitimate customer visits your site but does not purchase. He is now placed into your remarketing list.
This customer now visits another site on the internet. That site serves up an invisible ad and automatically “clicks” on that ad.
That click causes an invisible visit to your website, by that customer. That customer’s remarketing cookie records a click and a site visit.
Your legitimate customer now has a remarketing cookie set that says he saw an ad, clicked on that ad, and visited your website from that ad.
When that customer comes back to your site to make a purchases, that ad will take credit for the conversion.
There are two issues here: The first is that you’ve paid for a fraudulent click. The second more important issue is that a conversion has been attributed to that fraudulent click. As a result, your remarketing platform appears to be performing better, meaning you will likely increase your budget in order to buy more of those fraudulent clicks.
Obfuscation and Lack of transparency
Anytime the remarketing network is hiding data from you, that’s an opportunity for them to fool you. For example, Criteo refuses to be transparent with the list of publisher sites where you ad appears and where your clicks and conversions come from.
3.6% of Criteo’s ‘users’ generate 25% of its clicks. Such behavior by real human users is highly unlikely. This behavior is indicative of adware, bots, click farms, or other code created by Criteo or its affiliates to generate clicks and drive up Criteo’s click-count numbers.
Insist on detailed and transparent reports on where your ads were served, and where the clicks came from. If certain sites seem to have unusually high click and conversion rates, dig deeper into those sites to see if they are legitimate or not.
Avoid using platforms that lack transparency. Don’t use Criteo for this reason, as they refuse to provide transparent reports of where your ads are served, preventing you from doing a proper audit.
Hijacking Visits and Overwriting Analytics
SteelHouse used “malicious code to make it appear as though an internet user clicked on a SteelHouse-placed advertisement, even though no such click occurred.”
How this works:
A legitimate customer visits your site but does not purchase. He is now placed into your remarketing list.
This customer now visits another site and is shown an ad. The customer DOES NOT click on the ad, but the remarketing cookie records a View.
An hour later, the customer revisits your site from an organic source. The pixel script on your site detects that the customer has a recent ad view recorded.
The pixel script loads an invisible iFrame, in the iFrame it loads the original ad and generates an automatic click and visit to your site.
Your legitimate customer now has a remarketing cookie set that says he saw an ad, clicked on that ad, and visited your website from that ad. Your analytics data also says that this customer came from an ad click, and not from the original organic source.
When that customer comes back to your site to make a purchases, that ad will take credit for the conversion.
Worst still, when you look in your analytics, it will appear that your Remarketing Platform is sending you lots of visitors and conversions, which will cause you to spend more with them. The reality is that these visitors are organic visits that were hijacked by the remarketing pixel.
“SteelHouse’s practice of inserting a code into an internet user’s browser to make a view of an advertisement appear indistinguishable from a click on Decker’s Adobe Analytics system is not, in any way, common or acceptable industry practice. Nor should it be because it is deceptive.”
Keep your analytics private and hidden. The less they know about your analytics platform and setup, the more difficult it will be for them to inject data. Avoid giving them any access, and ideally don’t even tell them what platform you use.
Get a written declaration from the Remarketing Platform that they will not inject data into your analytics, that they will not modify referral data, that they will not generate clicks, nor do anything that will simulate visits to your site or in your analytics.
Don’t use Steelhouse
More steps to protect yourself
Be very skeptical
Be very skeptical of all data provided by the 3rd party platform. By default assume they are lying to you and are trying to take your money. Always rely on your in-house analytics data first (although even this can be messed with via 3rd party pixels) and always try to deeply understand how their platform and algorithms work. Explicitly ask what they are doing about ad fraud.
Beware of secret sauces, black boxes, and magical artificial intelligence powered predictive self optimizing algorithms. If something looks too good to be true, then it probably is.
Never ask the barber if you need a Haircut…
The ultimate selfish goal of any Advertising Platform is to MAXIMIZE your ad spend. You are THEIR customer and they want YOUR money. They want you to allocate as much of your budget to them as possible. They are incentivized to fool you.
Therefore be very skeptical with the data they give you and the stories they tell you. I would explicitly AVOID using either Steelhouse or Criteo due to first hand bad experiences with both.
YouTube Video Campaigns count Conversions very differently from other Campaign types
YouTube Video Campaigns count view-conversions as click-conversions, and include them in the “Conversions” columns of Google Ads.
If you are running a ROI focused YouTube Campaign that is targeting a lower funnel Remarketing Audience, the revenue and conversions reported by Google Ads can be over-inflated by 1000% or more vs what Google Analytics will report.
When running a YouTube Campaigns, always:
Be suspicious and skeptical of the conversion data
Adjust your performance targets. Conversions will be overinflated by a factor of up to 1000%, so adjust your performance accordingly.
Use Google Analytics as your measure of YouTube Campaign performance
Avoid Targeting lower funnel Remarketing Lists with YouTube. Save these lists for campaigns that offer true click-based conversion tracking.
Explicitly exclude low funnel Remarketing Lists from your targeting. This will ensure the attribution poaching is limited.
YouTube Conversion Tracking Explained
Standard Campaign Behaviour
For most campaign types, a conversion is only recorded in the “Conversions” column when someone clicks on your ad and then proceeds to make a purchase. This is called a Click-Through Conversion. If there are no clicks on your ad, no conversion is recorded in the Conversions column.
This is the normal expected behaviour.
YouTube Campaign Behaviour
For YouTube campaigns, clicks behave normally: If someone clicks on your ad and then makes a purchase, that purchase is recorded as a conversion as expected. So far so good.
However, if someone DOES NOT CLICK on your ad, but watches your entire ad, and then later purchases, that purchase will be recorded as a conversion and attributed to YouTube.
As per Google:
A ‘view’ is counted when someone watches 30 seconds (or the whole ad if it’s shorter than 30 seconds) or clicks on a part of the ad. A ‘view’ that leads to a conversion is counted in the ‘Conversions’ column.
Essentially, these are View-Through Conversions masquerading as Click-Through Conversions. The ad was never clicked, yet a conversion was recorded anyway. Considering that many ads are only 5 seconds long, most ad views are likely being treated as clicks.
This is NOT EXPECTED behaviour!
Attribution Poaching is when one Channel tries to take credit for a sale that was either going to happen anyway, or for a sale that was actually caused by another Channel.
Consider this scenario…
You create a YouTube campaign targeting your Shopping Cart Abandoners with a 5 second video ad.
One of your potential customers visits your site, and ads a product to their Shopping Cart. By adding to the Shopping Cart, they are now in your Shopping Cart Abandoners audience, and will be actively targeted by your YouTube Campaign.
They continue to browse your site, but they are interrupted by an e-mail or text from a friend with a link to a funny YouTube video. They click on the link and watch the video. While watching the video, they are forced to watch your 5s video ad (which they ignore). Since they watched y0ur entire ad, Google considers that a “click”.
They then eventually return to your site, to complete the purchase. Or maybe they complete the purchase 3 days later after receiving your Cart Abandonment e-mail.
In either case, your YouTube Campaign will claim credit for the Purchase, and will count it as a Conversion even thought that customer never clicked on your ad.
Why is Google Doing This?
A video view is much more like an impression than like a click, so why are these conversions being lumped in with click-through conversions?
The simple cynical answer is that Goolge makes more more money this way. By counting view-through conversions, the YouTube campaigns will appear to perform much better than they actually do with just click-through conversions (as much as 1000% better). If advertisers think that YouTube is performing 10X better, then they will allocated 10X more budget. The end result is Google makes 10X more money from YouTube.
A “Video View” is more like an Impressions than a Click
Video-View-Conversions should clearly be lumped into the View-Through Conversion column. If Google wants to explicitly report on Video-View-Conversions separately, then they should create another column type. Don’t lump them in with click-through conversions.
The problem with all this is that suddenly the “Conversions” column behaves differently in one campaign type vs another. Suddenly the clean click-through conversion data is being polluted with View-Through conversion data. This makes YouTube campaigns appear to perform much better than they should. Which will lead you to incorrectly increase spend.
To make the matter worse…
Many people have long click-through conversion windows. Often 30 days or even longer. This essentially allows a YouTube to claim credit for a conversion that happens 30 days after a video view.
Many video ads are short – only 5s long. Most of these short ads are probably viewed in their entirety, meaning that they are all being counted as clicks. Often the user is forced to watch the entire 5s ad – again a click. This leads to more incorrectly attributed click-conversions.
Often people target video ads using remarketing lists. Often the lowest hanging fruits for video campaigns are Remarketing Audiences. This shows ads to people who know your brand, who have recently visited your site, who may be subscribed to your newsletter, and who may in fact be currently actively shopping on your site. They are all likely to buy from you regardless of the the video ad, but the video ad will take credit for all their purchases.
This could also affect your Display and Discovery Campaigns
This issue could also affect your regular display campaigns and possibly your Discovery campaigns if they are serving ads on the YouTube network.
My recommendation for Display Campaigns is to try and exclude all YouTube placements: Go to: Campaign Settings > Additional Settings > Content Exclusions and select all of the following for exclusion:
Live streaming YouTube video
This should only be an issue if you use Google Ads Native Conversion tracking. If you import your conversions directly from Google Analytics, then this should not be an issue (as Analytics only attributes conversions to the last click)
If you are running a pure brand awareness campaign, this is probably less of a concern for you.
If you are an Ad Agency getting paid as a % of ad spend, then YouTube campaigns can make you a lot of money.
Once you’ve installed all your code, it’s time to run through your main pages (collection, product, cart, and purchase pages) with Google Tag Assistant installed to make sure there are no errors.
Next Steps Configure your Remarketing Audiences
Now that your store is collecting dynamic remarketing data, the next step is to properly organize and segment your visitors into Purchasers, Cart Abandoners and Product Viewers. This is covered in the next post about Google Ads Remarketing Audiences.
The deeper a user is in your sales funnel, the more likely he is to buy. A shopping cart abandoner is more likely to buy than a product browser who is more likely to buy than someone who briefly visited your homepage.
The more recent the user’s visit, the more likely he is to buy. A user who visited your site yesterday is more likely to buy than a user who visited your site 10 days ago.
The more likely a user is to buy, the more you want to bid on that user.
Create one campaign per major market you are targeting, and give them a descriptive name:
USA: Display Remarketing
Canada: Display Remarketing
Generally, but not always, you will want a separate campaign for every unique currency and language you are targeting.
Core Ad Groups
1. Cart Abandoners
This ad group will target cart abandoners: Visitors who added a product to their cart but never purchased. Dynamic Product ads perform particularly well with cart abandonment, as your visitors are shown the exact products that they added to their cart.
Cart Abandoners – 7 day
Cart Abandoners – 14 day
Cart Abandoners – 30 day
Cart Abandoners – 90 day
Cart Abandoners – 180 day
2. Product Viewers
This ad group will target users who visited a product details page but who never added a product to their cart.
Product Viewers – 14 day
Product Viewers – 30 day
Product Viewers – 90 day
Product Viewers – 180 day
Product Viewers – 365 day
Product Viewers – 520 day
3. Past Buyers
This ad group will show ads to people who have previously purchased. This is a good place to push micro conversions such as joining a loyalty program, joining a community, new product launches or related product up-selling.
Past Buyers 14 days
Past Buyers 30 days
Past Buyers 90 days
Past Buyers 365 days
Past Buyers 520 days
Exclude Mobile App Placements
Exclude placements where users are unlikely to interact with your ad, or where they may accidentally click your ad, such as in mobile apps and games.
To exclude mobile apps, go to your ad group and then select:
Placements > Exclusions tab > Exclude placements
App Categories > Expand All App Categories, and exclude all app categories individually
To exclude YouTube placements, go to: Campaign Settings > Additional Settings > Content Exclusions and select all of the following for exclusion:
Live streaming YouTube video
Exclude GMail Placements
Gmail “clicks” don’t necessarily result in a visit to your site, and usually only represent the expansion of your ad. This can lead to attribution poaching, in particular if you have a newsletter that you send our regularly.
To exclude gmail placements, go to Placements > Exclusions and exclude mail.google.com
A word about View Through Conversions
View Through Conversions are conversions where a display ad appeared on the screen, was NOT clicked, but the user ended up purchasing on your site sometime later. In general I recommend that everyone IGNORE View Through Conversions, in particular in remarketing campaigns.
What usually happens, is that an ad is displayed on screen, the visitor may not even see it, but clicks instead on a cart-abandonment e-mail and makes the purchase. AdWords will credit that conversion to the view through.
The one exception is for “brand unaware” customers. These are customers that have never visited your website before. If such a customer sees you ad, and purchases, then the odds are better that it was a result of your ad.
You spend $1,000 on a new Ad Campaign that generates $10,000 in revenue. “That’s a great ROI” you tell yourself, “Let’s double the spend!“.
When you double the budget to $2,000, your Campaign only generates $12,500 in total sales and not the $20,000 you were expecting. Why?
Cost of Sales
Ad Campaign #1
Ad Campaign #2
Increasing spend by $1,000 only resulted in $2,500 in additional revenue: An incremental cost of sales of 40% and an incremental ROAS of 250%
Why doesn’t it scale?
By scale I mean that your ROI should be linear: If the first $1,000 generates $10,000 in sales, then the next $1,000 should also generate $10,000 in sales.
The issue is that rarely is your campaign performance evenly distributed. If your drill down deeper into your initial $1000 Campaign, you might see the spend broken down into something like this:
Majority of the sales are being generated by a small subset of the overall campaign. A classic 80/20 scenario. The performance of the Branded ad set is subsidizing the cost of the unbranded ad set.
The performance of the Branded subset is subsidizing the cost of the Unbranded subset. 80% of your sales are coming from only 20% of the spend, while 80% of your spend is going towards an underperforming segment.
When we try to double the budget to $2,000, here is how the budget gets allocated:
When budget is doubled, most of the spend goes towards the underperforming segment, resulting in disappointing incremental sales.
With this new data in mind, we should probably:
Decrease budget on the Unbranded segment
Increase budget on the Branded segment
However, it is probably the case that your performing segment is already receiving 100% reach/impressions. So spending more is usually not possible. (In particular for Google Search Ads targeting your branded term: How much you can spend is a function of how many people are searching for your brand. Once you reach everyone, spending more can’t get you more people).
If you want to increase your spend, the only place to do so is in the underperforming unbranded campaign. But at least you’ll have a better expectation of the results.
Avoid making budget decisions on aggregate data. Always try to segment and dig a little deeper.
Don’t let underperforming segments ride the coat tails of your top performers. Look for 80/20 campaign and ad group performance and analyze those individually.
For Google Search Ads, always separate your Branded search terms and Unbranded search terms into separate campaigns.
If you are using Google Merchant Center to with Shopify then you have likely ran into an issue where Merchant Center will give you a warning that there is insufficient match of micro-data information and that automatic item updates are no longer being performed.
This is generally due to incorrect or incomplete micro-data on your product page.
All the required edits should be limited to your Product.liquid file. You need to define a Product itemscope which will have properties such as Product Url, Product Image, Product Title, and Product Description.
Nested within the Product will be an Offer itemscope that will contain the product variant’s price, currency, condition, and availability.
What complicates things is that most Shopify Themes will have at least a partial implementation of micro-data, and are perhaps only missing a few items, or perhaps don’t fully support variants.
4. Now you need to find the place in your product.liquid file where you display your price. Find a wrapping div tag and add the Offers itemscope attributes to the tag. The Offers itemscope MUST nested within the Product itemscope div tag.
WARNING! This code is no longer maintained. Although the code in this post should still work, please use at your own risk. I recommend using the Shopify Facebook Marketing App to sync your product catalog.
Below is a free customizable DIY solution to create a Facebook Product Feed in Shopify.
This is an advanced topic and assumes you have the required understanding of HTML/XML/Liquid, the Shopify Store Admin and Facebook Business Manager.
Enter the feed collection url you copied in step 3 above. Leave the username & password blank. Choose a time for your daily upload to occur (early morning is usually a good time). Choose your currency.
Click Start Upload and wait for the feed to be fetched and processed.
Fix errors: If there are errors, go back, fix them, re-fetch, and keep doing so until the feed is error free. Sometimes it is necessary to delete and re-create your catalog in Facebook for some changes to appear.
5. Prevent the Facebook Feed from Showing on your Store
Depending on how your store is setup, you may need to add some code to prevent your Facebook feed collection from showing up on your store. The exact way to do this may depend on your theme, but generally you will want to have an “unless” statement within the loop that displays your collections:
By default, Shopify sends transactions to Google Analytics with a unique product title for each product variant. This causes the Product Performance Report to be split on the variant level instead of at the product level as it was intended.
If, for example. you are selling winter boots, and someone buys a size 10 in Black, Shopify will send the product name as “Winter Boots – 10 / Black” instead of just “Winter Boots“. This is a bug as as the variant details are already included in the Google Analytics “Product Variant” column.
The Solution: GA Custom Data Import
The solution to this problem is to overwrite the Shopify data using the Google Analytics Custom Data Import tool.
1: Export your Product Data
First we need to export all our product data – we can accomplish this by creating a custom Collection Template that generates a CSV report instead of the standard HTML.
a. Create a new Collection Template
Call the new collection template csv-ga-product-feed and paste the following code:
b. Create a new Collection based on your csv-ga-product-feed Template
Select the products you want to include in this feed (probably all your products). These will be the products whose values will be overwritten in Google Analytics. Call your collection “Google Analytics Product Data Import” or something similar and save it.
c. Download your Product Feed
View your new collection in your store (eg: store.myshopify.com/collections/google-analytics-product-data-import)
View source in your browser and save as HTML
Rename the file with a CSV extension (eg: google-analytics-product-data-import.csv)
2: Setup and Import the data into Google Analytics
WARNING! You can really mess up your Google Analytics data if things go wrong. I highly recommend that you duplicate or backup your Google Analytics view and do a trial run before working with your live data. Once you upload this new data and overwrite there is no UNDO!
a. Setup the Data Feed
Go to Google Analytics > Admin > Account > Property > Data Import
Click the red “+ NEW DATA SET” button
Select “Product Data”
Give your Data Import a name: “Product Name Override”
Select the Google Analytics Views you want this import to affect
Setup your Data Set Schema: Product SKU is the mandatory key, but select Product and Product Variant as the additional fields.
Overwrite Hit Data: Choose Yes (but read my warning above)
Click Save and Done
b. Upload your data feed
Click on “manage uploads” beside your new Data Feed definition
Click the blue UPLOAD button
Choose your CSV file and click UPLOAD again
And now wait for the upload an update to be complete
3: Verify your new data
The data upload will only affect data from this date forward. So your old data will not be fixed. But your future data will be nice and clean… Until you add new products to your store, in which case you will have to repeat this process.
You will need to wait at least a day before you start seeing the new data coming in. If you add new product SKUs to your store, you will also need to regenerate and reupload a new file in order for the new product data to be fixed.