MARKETING
Analytics: What Sources are Stealing Your Paid Revenue Attribution?
“Where should we attribute revenue?” is a question every digital marketer has asked themselves at some point. In two different accounts, we found that referral and affiliate websites were receiving revenue credit for paid-initiated traffic. These two clients both use Analytics revenue for the account’s target ROAS goals rather than the platforms. At times, we had to pull back our paid spend when Analytics dipped under the target goal. After further analysis, I wondered if we should reconsider the attribution approach.
In one account, we noticed a 90% increase in revenue attributed to referral websites month-over-month. This caught my attention because this was a significant increase.
I investigated what sources were contributing to the increase in referral traffic. Sometimes, platforms or banks appear under referrals. Sometimes, payment processing websites appear under Referrals, such as PayPal or to Affirm. These types of sources are not responsible for driving the traffic and are common referral exclusions. This means Analytics would ignore these sessions and give the credit to the previous interaction.
Analytics Attributing Revenue to Referral Sources
If we drill down into the referral websites, we can see that many of the websites receiving revenue credit are coupon websites.
Many times, the coupons do not even work on these websites, but Analytics will still credit the purchase to these domains if the user clicks the link that directs them back to the website.
If we look at the Paid-Initiated paths that end with referrals, the coupon websites received $39,062 in revenue.
If we look specifically at Referral-initiated paths, out of the $87,729 in revenue, the coupon websites were only responsible for initiating 6 visits with $4,701 in revenue.
After a discussion with the client, they believed these coupon websites were not valuable and often did not have valid coupons. So, we made the decision to exclude these coupon websites to avoid having them interfere with our marketing objectives.
Analytics Attributing Revenue to Other Advertising
In the second account, we noticed it became increasingly difficult to hit their target ROAS goals during the summer. However, since this immediately followed the distribution of the first round of stimulus checks, we believed this attributed to the spike in revenue. They also had some larger coupon promotions on their website during the spring months.
As you can see in this month-over-month trend, ROAS dropped below 300% starting in June. The value the platforms were reporting revenue $250k higher than what Analytics was reporting.
A few months ago, Analytics had revenue bucketed as other, and it was often the last interaction in Analytics. This channel was responsible for 14% of the revenue in Analytics for 8 months.
All of the revenue under Other Advertising was being attributed to a CJ Affiliates source. This started around the same time the account began to struggle meeting its ROAS goals.
Analytics Paid-Initiated Traffic and Other Advertising
In the Multi-Channel Funnels in the Top Conversion paths, it shows that $1.4M out of the $2.5M of paid-initiated traffic was attributed to Other Advertising (CJ Affiliates). Most of this revenue would have been attributed to Paid Search if the affiliate source was not present.
Note: This report is filtered to conversion types as transactions only. It is also filtered for traffic that begins with Paid Search and ends with Other Advertising.
During one conversation with the client, we all agreed while the affiliates may be contributing to the revenue, but the question was exactly how much. If we change up the filters to show traffic that Begins with Other Advertising, we can see this channel is only responsible for driving traffic that resulted in 281 transactions and $47,268 in revenue.
So, in this case, while we could say the affiliate program is assisting in the searchers making the purchase, it does not appear to be the primary channel driving searchers to the website. So, Analytics attributing 100% of the revenue in MCF is greatly devaluing Paid Search traffic’s role.
Google Analytics – Last Click Attribution
Another important consideration is how Analytics is reporting conversions and revenue. By default, Analytics is set up to give the last non-direct visit 100% of the conversions or revenue credit. One issue with this model is the user journey is complex; assigning all the credit to the “last touchpoint” may undervalue other sources.
In the paid-initiated traffic, we can see they visit the coupon or affiliate websites right before making a purchase, and then the revenue is attributed to the coupon websites. Sometimes we see the searcher visit multiple coupon websites in the same session. Also, we can see that some revenue was attributed to the Affirm payment option for when searchers prefer to make payments over time.
For example, if a person clicks to your website from a Paid ad, then returns as a coupon ‘referral’ or ‘other advertising’ traffic to convert, Analytics will report 1 transaction for Referral or Other Advertising. The Multi-Channel Funnels report will show 1 conversion with the path paid search > referral. Paid search will get 1 assisted conversion.
In the Last Interaction attribution model, the last touchpoint—in this case, the Referral channel—would receive 100% of the credit for the sale.
In the following scenarios, the final touchpoint will get 100% of the credit in Analytics’ Last Click model unless it is Direct and then it gives credit to the previous source.
In the scenarios above, the last click may be the reason you purchased, but it is not the reason you were interested in the first place. It may be worth considering upgrading your attribution model to something that gives more credit to other touchpoints along the journey. This type of attribution makes it difficult to assign credit where credit is due.
Let’s say we updated it to a Position-Based model. The attribution credit would look something like this with 40% attributed to the first and last touchpoints and 20% divided to anything in between.
In Google Ads, most of us have moved away from the Last Click attribution model. This article From Last Click to Position-Based: An Attribution Test does a great job of discussing how changing the model impacted Google Ads campaigns. If your account has enough clicks and conversions, then the Data Driven Model will be an available option.
Model Comparison Tool
You can use the Model Comparison Tool in Analytics or it is called the Model Comparison Explorer in Analytics 360. They can be found under Conversions > Multi-Channel Funnels > Model Comparison Tool.
In the above scenario, the channels that would benefit the most are Paid Search, Organic Search, and Social Network. This data shows us that these paths may begin the journey more often and the Last Click model is not giving them the credit they may be entitled to receive.
You can also use the Attribution Beta in Analytics to explore the difference in the models without changing the settings.
Analytics Attribution Revenue to Referral Spam Coupon Websites
In this case, we see a large portion of the revenue is being attributed to coupon websites. These websites dominate the search results when you look for coupons for many brands. Oftentimes these coupons do not work, but searchers will try to get a promotion. You can see some ads are offering discounts for Macy’s here.
One option might be to switch to another attribution model in Analytics. If the Data-Driven Attribution model is available this might be the best option. Your account would need to meet specific criteria for this option to be available. Another option would be to switch to Position-Based for conversions that involve multiple touchpoints.
Another option might be to create a special coupon page for your website that is not easily found on your website. Then you can set up a Brand Coupon ad group and target these discount terms to bring searchers back to your website with a valid coupon. While some people may continue checking out without a coupon, others may choose to abandon their cart.
Conclusion
It may be time to really think about how we are attribution revenue in Analytics. The searcher’s journey can often be complex. Is the Last Click approach attributing too much revenue to sources that are less valuable? Are these referral sources devaluing your marketing efforts? Even if you decide you are not ready to rethink the attribution model in Analytics, it would be worth the time to deep dive into the list of referral sources getting credit for revenue. Maybe some of these referral sources could be excluded to give you a better vantage point of what is contributing the most.
MARKETING
YouTube Ad Specs, Sizes, and Examples [2024 Update]
Introduction
With billions of users each month, YouTube is the world’s second largest search engine and top website for video content. This makes it a great place for advertising. To succeed, advertisers need to follow the correct YouTube ad specifications. These rules help your ad reach more viewers, increasing the chance of gaining new customers and boosting brand awareness.
Types of YouTube Ads
Video Ads
- Description: These play before, during, or after a YouTube video on computers or mobile devices.
- Types:
- In-stream ads: Can be skippable or non-skippable.
- Bumper ads: Non-skippable, short ads that play before, during, or after a video.
Display Ads
- Description: These appear in different spots on YouTube and usually use text or static images.
- Note: YouTube does not support display image ads directly on its app, but these can be targeted to YouTube.com through Google Display Network (GDN).
Companion Banners
- Description: Appears to the right of the YouTube player on desktop.
- Requirement: Must be purchased alongside In-stream ads, Bumper ads, or In-feed ads.
In-feed Ads
- Description: Resemble videos with images, headlines, and text. They link to a public or unlisted YouTube video.
Outstream Ads
- Description: Mobile-only video ads that play outside of YouTube, on websites and apps within the Google video partner network.
Masthead Ads
- Description: Premium, high-visibility banner ads displayed at the top of the YouTube homepage for both desktop and mobile users.
YouTube Ad Specs by Type
Skippable In-stream Video Ads
- Placement: Before, during, or after a YouTube video.
- Resolution:
- Horizontal: 1920 x 1080px
- Vertical: 1080 x 1920px
- Square: 1080 x 1080px
- Aspect Ratio:
- Horizontal: 16:9
- Vertical: 9:16
- Square: 1:1
- Length:
- Awareness: 15-20 seconds
- Consideration: 2-3 minutes
- Action: 15-20 seconds
Non-skippable In-stream Video Ads
- Description: Must be watched completely before the main video.
- Length: 15 seconds (or 20 seconds in certain markets).
- Resolution:
- Horizontal: 1920 x 1080px
- Vertical: 1080 x 1920px
- Square: 1080 x 1080px
- Aspect Ratio:
- Horizontal: 16:9
- Vertical: 9:16
- Square: 1:1
Bumper Ads
- Length: Maximum 6 seconds.
- File Format: MP4, Quicktime, AVI, ASF, Windows Media, or MPEG.
- Resolution:
- Horizontal: 640 x 360px
- Vertical: 480 x 360px
In-feed Ads
- Description: Show alongside YouTube content, like search results or the Home feed.
- Resolution:
- Horizontal: 1920 x 1080px
- Vertical: 1080 x 1920px
- Square: 1080 x 1080px
- Aspect Ratio:
- Horizontal: 16:9
- Square: 1:1
- Length:
- Awareness: 15-20 seconds
- Consideration: 2-3 minutes
- Headline/Description:
- Headline: Up to 2 lines, 40 characters per line
- Description: Up to 2 lines, 35 characters per line
Display Ads
- Description: Static images or animated media that appear on YouTube next to video suggestions, in search results, or on the homepage.
- Image Size: 300×60 pixels.
- File Type: GIF, JPG, PNG.
- File Size: Max 150KB.
- Max Animation Length: 30 seconds.
Outstream Ads
- Description: Mobile-only video ads that appear on websites and apps within the Google video partner network, not on YouTube itself.
- Logo Specs:
- Square: 1:1 (200 x 200px).
- File Type: JPG, GIF, PNG.
- Max Size: 200KB.
Masthead Ads
- Description: High-visibility ads at the top of the YouTube homepage.
- Resolution: 1920 x 1080 or higher.
- File Type: JPG or PNG (without transparency).
Conclusion
YouTube offers a variety of ad formats to reach audiences effectively in 2024. Whether you want to build brand awareness, drive conversions, or target specific demographics, YouTube provides a dynamic platform for your advertising needs. Always follow Google’s advertising policies and the technical ad specs to ensure your ads perform their best. Ready to start using YouTube ads? Contact us today to get started!
MARKETING
Why We Are Always ‘Clicking to Buy’, According to Psychologists
Amazon pillows.
MARKETING
A deeper dive into data, personalization and Copilots
Salesforce launched a collection of new, generative AI-related products at Connections in Chicago this week. They included new Einstein Copilots for marketers and merchants and Einstein Personalization.
To better understand, not only the potential impact of the new products, but the evolving Salesforce architecture, we sat down with Bobby Jania, CMO, Marketing Cloud.
Dig deeper: Salesforce piles on the Einstein Copilots
Salesforce’s evolving architecture
It’s hard to deny that Salesforce likes coming up with new names for platforms and products (what happened to Customer 360?) and this can sometimes make the observer wonder if something is brand new, or old but with a brand new name. In particular, what exactly is Einstein 1 and how is it related to Salesforce Data Cloud?
“Data Cloud is built on the Einstein 1 platform,” Jania explained. “The Einstein 1 platform is our entire Salesforce platform and that includes products like Sales Cloud, Service Cloud — that it includes the original idea of Salesforce not just being in the cloud, but being multi-tenancy.”
Data Cloud — not an acquisition, of course — was built natively on that platform. It was the first product built on Hyperforce, Salesforce’s new cloud infrastructure architecture. “Since Data Cloud was on what we now call the Einstein 1 platform from Day One, it has always natively connected to, and been able to read anything in Sales Cloud, Service Cloud [and so on]. On top of that, we can now bring in, not only structured but unstructured data.”
That’s a significant progression from the position, several years ago, when Salesforce had stitched together a platform around various acquisitions (ExactTarget, for example) that didn’t necessarily talk to each other.
“At times, what we would do is have a kind of behind-the-scenes flow where data from one product could be moved into another product,” said Jania, “but in many of those cases the data would then be in both, whereas now the data is in Data Cloud. Tableau will run natively off Data Cloud; Commerce Cloud, Service Cloud, Marketing Cloud — they’re all going to the same operational customer profile.” They’re not copying the data from Data Cloud, Jania confirmed.
Another thing to know is tit’s possible for Salesforce customers to import their own datasets into Data Cloud. “We wanted to create a federated data model,” said Jania. “If you’re using Snowflake, for example, we more or less virtually sit on your data lake. The value we add is that we will look at all your data and help you form these operational customer profiles.”
Let’s learn more about Einstein Copilot
“Copilot means that I have an assistant with me in the tool where I need to be working that contextually knows what I am trying to do and helps me at every step of the process,” Jania said.
For marketers, this might begin with a campaign brief developed with Copilot’s assistance, the identification of an audience based on the brief, and then the development of email or other content. “What’s really cool is the idea of Einstein Studio where our customers will create actions [for Copilot] that we hadn’t even thought about.”
Here’s a key insight (back to nomenclature). We reported on Copilot for markets, Copilot for merchants, Copilot for shoppers. It turns out, however, that there is just one Copilot, Einstein Copilot, and these are use cases. “There’s just one Copilot, we just add these for a little clarity; we’re going to talk about marketing use cases, about shoppers’ use cases. These are actions for the marketing use cases we built out of the box; you can build your own.”
It’s surely going to take a little time for marketers to learn to work easily with Copilot. “There’s always time for adoption,” Jania agreed. “What is directly connected with this is, this is my ninth Connections and this one has the most hands-on training that I’ve seen since 2014 — and a lot of that is getting people using Data Cloud, using these tools rather than just being given a demo.”
What’s new about Einstein Personalization
Salesforce Einstein has been around since 2016 and many of the use cases seem to have involved personalization in various forms. What’s new?
“Einstein Personalization is a real-time decision engine and it’s going to choose next-best-action, next-best-offer. What is new is that it’s a service now that runs natively on top of Data Cloud.” A lot of real-time decision engines need their own set of data that might actually be a subset of data. “Einstein Personalization is going to look holistically at a customer and recommend a next-best-action that could be natively surfaced in Service Cloud, Sales Cloud or Marketing Cloud.”
Finally, trust
One feature of the presentations at Connections was the reassurance that, although public LLMs like ChatGPT could be selected for application to customer data, none of that data would be retained by the LLMs. Is this just a matter of written agreements? No, not just that, said Jania.
“In the Einstein Trust Layer, all of the data, when it connects to an LLM, runs through our gateway. If there was a prompt that had personally identifiable information — a credit card number, an email address — at a mimum, all that is stripped out. The LLMs do not store the output; we store the output for auditing back in Salesforce. Any output that comes back through our gateway is logged in our system; it runs through a toxicity model; and only at the end do we put PII data back into the answer. There are real pieces beyond a handshake that this data is safe.”
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