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Case Study: How the Cookie Monster Ate 22% of Our Visibility

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You Can Go Your Own Way: How to Get Things Done When You’re the Only SEO

The author’s views are entirely his or her own (excluding the unlikely event of hypnosis) and may not always reflect the views of Moz.

Last year, the team at Homeday — one of the leading property tech companies in Germany — made the decision to migrate to a new content management system (CMS). The goals of the migration were, among other things, increased page speed and creating a state-of-the-art, future-proof website with all the necessary features. One of the main motivators for the migration was to enable content editors to work more freely in creating pages without the help of developers. 

After evaluating several CMS options, we decided on Contentful for its modern technology stack, with a superior experience for both editors and developers. From a technical viewpoint, Contentful, as a headless CMS, allows us to choose which rendering strategy we want to use. 

We’re currently carrying out the migration in several stages, or waves, to reduce the risk of problems that have a large-scale negative impact. During the first wave, we encountered an issue with our cookie consent, which led to a visibility loss of almost 22% within five days. In this article I’ll describe the problems we were facing during this first migration wave and how we resolved them.

Setting up the first test-wave 

For the first test-wave we chose 10 SEO pages with high traffic but low conversion rates. We established an infrastructure for reporting and monitoring those 10 pages: 

  • Rank-tracking for most relevant keywords 

  • SEO dashboard (DataStudio, Moz Pro,  SEMRush, Search Console, Google Analytics)

  • Regular crawls 

After a comprehensive planning and testing phase, we migrated the first 10 SEO pages to the new CMS in December 2021. Although several challenges occurred during the testing phase (increased loading times, bigger HTML Document Object Model, etc.) we decided to go live as we didn’t see big blocker and we wanted to migrate the first testwave before christmas. 

First performance review

Very excited about achieving the first step of the migration, we took a look at the performance of the migrated pages on the next day. 

What we saw next really didn’t please us. 

Overnight, the visibility of tracked keywords for the migrated pages reduced from 62.35% to 53.59% — we lost 8.76% of visibility in one day

As a result of this steep drop in rankings, we conducted another extensive round of testing. Among other things we tested for coverage/ indexing issues, if all meta tags were included, structured data, internal links, page speed and mobile friendliness.

Second performance review

All the articles had a cache date after the migration and the content was fully indexed and being read by Google. Moreover, we could exclude several migration risk factors (change of URLs, content, meta tags, layout, etc.) as sources of error, as there hasn’t been any changes.

Visibility of our tracked keywords suffered another drop to 40.60% over the next few days, making it a total drop of almost 22% within five days. This was also clearly shown in comparison to the competition of the tracked keywords (here “estimated traffic”), but the visibility looked analogous. 

As other migration risk factors plus Google updates had been excluded as sources of errors, it definitely had to be a technical issue. Too much JavaScript, low Core Web Vitals scores, or a larger, more complex Document Object Model (DOM) could all be potential causes. The DOM represents a page as objects and nodes so that programming languages like JavaScript can interact with the page and change for example style, structure and content.

Following the cookie crumbs

We had to identify issues as quickly as possible and do quick bug-fixing and minimize more negative effects and traffic drops. We finally got the first real hint of which technical reason could be the cause when one of our tools showed us that the number of pages with high external linking, as well as the number of pages with maximum content size, went up. It is important that pages don’t exceed the maximum content size as pages with a very large amount of body content may not be fully indexed. Regarding the high external linking it is important that all external links are trustworthy and relevant for users. It was suspicious that the number of external links went up just like this.

Increase of URLs with high external linking (more than 10)
Increase of URLs which exceed the specified maximum content size (51.200 bytes)

Both metrics were disproportionately high compared to the number of pages we migrated. But why?

After checking which external links had been added to the migrated pages, we saw that Google was reading and indexing the cookie consent form for all migrated pages. We performed a site search, checking for the content of the cookie consent, and saw our theory confirmed: 

A site search confirmed that the cookie consent was indexed by Google

This led to several problems: 

  1. There was tons of duplicated content created for each page due to indexing the cookie consent form. 

  2. The content size of the migrated pages drastically increased. This is a problem as pages with a very large amount of body content may not be fully indexed. 

  3. The number of external outgoing links drastically increased. 

  4. Our snippets suddenly showed a date on the SERPs. This would suggest a blog or news article, while most articles on Homeday are evergreen content. In addition, due to the date appearing, the meta description was cut off. 

But why was this happening? According to our service provider, Cookiebot, search engine crawlers access websites simulating a full consent. Hence, they gain access to all content and copy from the cookie consent banners are not indexed by the crawler. 

So why wasn’t this the case for the migrated pages? We crawled and rendered the pages with different user agents, but still couldn’t find a trace of the Cookiebot in the source code. 

Investigating Google DOMs and searching for a solution

The migrated pages are rendered with dynamic data that comes from Contentful and plugins. The plugins contain just JavaScript code, and sometimes they come from a partner. One of these plugins was the cookie manager partner, which fetches the cookie consent HTML from outside our code base. That is why we didn’t find a trace of the cookie consent HTML code in the HTML source files in the first place. We did see a larger DOM but traced that back to Nuxt’s default, more complex, larger DOM. Nuxt is a JavaScript framework that we work with.

To validate that Google was reading the copy from the cookie consent banner, we used the URL inspection tool of Google Search Console. We compared the DOM of a migrated page with the DOM of a non-migrated page. Within the DOM of a migrated page, we finally found the cookie consent content:

Within the DOM of a migrated page we found the cookie consent content

Something else that got our attention were the JavaScript files loaded on our old pages versus the files loaded on our migrated pages. Our website has two scripts for the cookie consent banner, provided by a 3rd party: one to show the banner and grab the consent (uc) and one that imports the banner content (cd).

  • The only script loaded on our old pages was uc.js, which is responsible for the cookie consent banner. It is the one script we need in every page to handle user consent. It displays the cookie consent banner without indexing the content and saves the user’s decision (if they agree or disagree to the usage of cookies).

  • For the migrated pages, aside from uc.js, there was also a cd.js file loading. If we have a page, where we want to show more information about our cookies to the user and index the cookie data, then we have to use the cd.js. We thought that both files are dependent on each other, which is not correct. The uc.js can run alone. The cd.js file was the reason why the content of the cookie banner got rendered and indexed.

It took a while to find it because we thought the second file was just a pre-requirement for the first one. We determined that simply removing the loaded cd.js file would be the solution.

Performance review after implementing the solution

The day we deleted the file, our keyword visibility was at 41.70%, which was still 21% lower than pre-migration. 

However, the day after deleting the file, our visibility increased to 50.77%, and the next day it was almost back to normal at 60.11%. The estimated traffic behaved similarly. What a relief! 

Quickly after implementing the solution, the organic traffic went back to pre-migration levels

Conclusion

I can imagine that many SEOs have dealt with tiny issues like this. It seems trivial, but led to a significant drop in visibility and traffic during the migration. This is why I suggest migrating in waves and blocking enough time for investigating technical errors before and after the migration. Moreover, keeping a close look at the site’s performance within the weeks after the migration is crucial. These are definitely my key takeaways from this migration wave. We just completed the second migration wave in the beginning of May 2022 and I can state that so far no major bugs appeared. We’ll have two more waves and complete the migration hopefully successfully by the end of June 2022.

The performance of the migrated pages is almost back to normal now, and we will continue with the next wave. 

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YouTube Ad Specs, Sizes, and Examples [2024 Update]

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YouTube Ad Specs, Sizes, and Examples

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!

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Why We Are Always ‘Clicking to Buy’, According to Psychologists

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Why We Are Always 'Clicking to Buy', According to Psychologists

Amazon pillows.

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A deeper dive into data, personalization and Copilots

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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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