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How Helpful Was the Helpful Content Update?

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How Helpful Was the Helpful Content Update?

On August 25, Google started rolling out the Helpful Content Update, an ongoing effort to reward sites with “people-first” (i.e. not written specifically for SEO) content. MozCast measured rankings flux peaking at 92°F on August 26, which sounds relatively high, but this is what the two weeks on either side of the update looked like:

The dotted blue line shows the 30-day average for the period prior to the start of the update, which came in at 87°F. Ranking flux actually peaked on August 23 above any day of the update rollout. To make matters worse, we had to remove August 8-9 from the 30-day average, because Google’s data center outage completely disrupted search results.

Let me sum up: it’s a mess. I like to think I’m pretty good at handling messes, but this is like trying to find one particular drop of water in two weeks of rain during a summer-long storm. If you like messes, read on, but for the rest of you, I’ll tell you this — I found no clear evidence that this first iteration of the Helpful Content Update moved the needle for most sites.

Averages, lies, and damned lies

Given the extended rollout, I attempted to look at the difference in visibility for individual domains for the 14 days before and after the start of the rollout (which helps smooth out single-day outliers and keeps the days of the week consistent across both sides). One “loser” that immediately popped up was Conch-House.com, with nearly a 50% visibility loss in our data set. I admit, I even got a little judgmental about the hyphen in the domain name. Then, I looked at the daily data:

1663811105 672 How Helpful Was the Helpful Content Update

The averages don’t tell even half of this story. Whatever happened to Conch-House.com, they were completely knocked out of the rankings for 20 out of the 28 days analyzed. Note that the MozCast data set is limited, but our much larger STAT data set showed a similar pattern, with Conch-House.com ranking for up to 14,000 keywords on one day during this period.

What happened? I have no idea, but it quite definitely, almost certainly, very probably maybe was not the Helpful Content Update.

Confirmed content coincidence

Here’s an example I got pretty excited about. WhiteHouse.gov saw a +54% total visibility gain across the two time periods. The keyword set was pretty small so, once again, I dug into the daily numbers:

1663811105 698 How Helpful Was the Helpful Content Update

Looks great, right? There’s a clear spike on August 25 (although it fades a bit), and while the spike wasn’t as large, I was able to confirm this against a larger data set. If I was smart, I would’ve stopped the analysis right here. My friends, I was not smart.

One of the challenges of the Helpful Content Update is that Google has explicitly stated that helpful (or unhelpful) contact will impact rankings across a domain:

Any content — not just unhelpful content — on sites determined to have relatively high amounts of unhelpful content overall is less likely to perform well in Search …


Even so, it’s interesting to dig into specific pieces of content that improved or declined. In this case, WhiteHouse.gov clearly saw gains for one particular page:

1663811106 994 How Helpful Was the Helpful Content Update

This brief was published on August 24, immediately followed by a storm of media attention driving people to the official details. The timing was entirely a coincidence.

Is it helpful content (regardless of your take on the issue)? Almost certainly. Could WhiteHouse.gov be rewarded for producing it? Entirely possibly. Was this increase in visibility due to the Helpful Content Update? Probably not.

Is this blog post helpful content?

Hey, I tried. I’ve probably lost three nights of sleep over the past three weeks thanks to the Helpful Content Update. The truth is that extended rollouts mean extended SERP churn. Google search results are real-time phenomena, and the web is always changing. In this case, there was no clear spike (at least, no clear spike relative to recent history) and every once-promising example I found ultimately came up short.

Does that mean the update was a dud? No, I think this is the beginning of something important, and reports of niche impacts in sites with clear quality issues may very well be accurate (and, certainly, some have been reported by reputable SEOs whom I know and respect). The most viable explanation I can come up with is that this was a first step in rolling out a “helpfulness” factor, but that factor is going to take time to iterate on, ramp up, and fully build into the core algorithm.

One mystery that remains is why Google chose to pre-announce this update. Historically, for updates like Mobile-friendly or HTTPS, pre-announcements were Google’s way of influencing us to make changes (and, frankly, it worked), but this announcement arrived only a week before the update began, and after Google stated they had updated the relevant data. In other words, there was no time between the pre-announcement and the rollout to fix anything.

Ultimately, Google is sending us a signal about their future direction, and we should take that signal seriously. Look at the HTTPS update — when it first rolled out in 2014, we saw very little rankings flux and only about 8% of page-one organic results in MozCast were HTTPS URLs. Over time, Google turned up the volume and Chrome began to warn about non-HTTPS sites. In 2017, 50% of page-one organic results in MozCast were HTTPS. Today, in late 2022, it’s 99%.

The Helpful Content Update probably isn’t going to change the game overnight, but the game will change, and we would all do well to start learning the new rules.

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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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