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Commercial vs. Functional vs. Emotional: A Case Study on Page Title SEO Testing

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Commercial vs. Functional vs. Emotional: A Case Study on Page Title SEO Testing

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.

My dad used to tell me that the one thing you invest in for your car is the tires. I had a habit of asking the garage for the cheapest tires they had, but my dad would say “that rubber is the only thing between you and the road”. He had a point, and today I invest in those tires to get me to my destination safer.

There’s a similar trap that search marketers and SEOs can easily fall into. In our fast-paced day-to-day lives, we can often underestimate the power of copy, even though, like my tires and the road, it’s the only thing between our business and our customers. Much like my tires, if you don’t invest in it, you’re in for a bad time.

To that end, I’ve used SEO Testing to trial different copy types in product page titles, and want to share the results of that test.

The hypothesis

Customers are more likely to click organic search engine results featuring content that is commercially focused, using language like “free” or “best value”.

Every good test starts with a hypothesis. It’s nothing more than an idea that I want to test and learn from. While there’s an outcome I expect, the data is all I really care about. That’s where SEO Testing comes in.

The test

The test itself had some simple steps. I was updating page titles across a range of mobile phone product pages, so that these would appear in the SERPs in front of our customers. To measure success, the primary KPI was CTR, observed in Google Search Console.

The test would run across all phones on the Three website for six weeks. The control CTR data was collected from the six weeks prior to updating the page titles.

Instead of simply changing page titles to commercial content, I decided to hedge my bets a little and cover the spread with some additional test parameters. If commercial copy didn’t work, what copy did connect with our customers the best?

In addition to a bucket of page titles focused on commercial content, I also added two “backup buckets” for functional and emotional copy.

I used the new SEO Testing Group Test functionality to create three groups:

  • Commercial content page titles

  • Functional content page titles

  • Emotional content page titles

Commercial content focused on appealing to the financial aspects of a purchase decision. Functional copy stuck to the facts and just simply said what you would be finding on the page you clicked through to. Emotional took a softer and “fluffier” approach.

Here are some examples of the content we used:

  • Commercial: iPhone 12 Pro Max | Buy Now At Our Best Ever Price | Three

  • Functional: Samsung Galaxy A02s | A Powerful Entry Level Phone | Three

  • Emotional: iPhone 11 | Get The iPhone You Always Wanted | Three

The four pillars of the test: Control content, commercial focus, functional focus, and emotional focus.

Google being Google

Just as this test was ending, Google started to use their AI-power to rewrite page titles, steering away from using the provided page titles less and less. Fortunately, this test was finishing at the same time Google was rolling this functionality out, and to the best of my knowledge, the test was not impacted by the update. I was running the test in the Irish market, which had seen very few page title re-writes at the time.

Regardless, at the core of this test is consumer psychology. Even if Google never pulls in another page title that I write for the rest of my days, the reason people clicked, or didn’t click, on content during the test matters. It’s a data-based example of how your potential customers respond to the words you put on your page, and why it’s important you invest in them — just like your tires.

The results

You shouldn’t run a test and then check it every day. Just hit start and do your best to forget about it.

I ignored my own advice and regularly checked the data.

In the early stages the hypothesis held up, but after a few more days a clear trend emerged. What did I learn here? The start date of the test isn’t necessarily the date the page titles change. It takes time for Google to crawl and re-index the new content.

After a few more days, the trends started to change completely and by the end of the six-week test period, the hypothesis failed. And that’s okay. In fact, that’s exciting, especially because contingency was baked into the test.

The results of the test: commercial +1 percent, functional +9 percent, emotional -31 percent.

Customers responded best to the simple functional copy group, evident through a 9% increase in CTR for this group. Customers also emphatically rejected copy with a softer, emotional focus, the clearest outcome of the test with a 31% reduction in CTR (which is, for me, the most interesting result).

If I had just run the commercial group, I would have been left with very few learnings thanks to a paltry 1% increase in CTR.

It’s an important side note to include that this test was being carried out after a CMS migration, which led to automated page titles being generated and pulled into Google. It was an unfortunate by-product of an otherwise successful migration that took some time to resolve. Organic CTR did drop by approximately 21% on monitored product pages for a period of time immediately after the migration, due to the automatically generated page titles appearing spammy.

Google SERP snippet for iPhone.

So, this test was more than a test, it was also a fix.

But that meant the control copy feeding into Google was automatically generated and uniform. Despite this, emotional copy led to a further 31% drop in click through rate. I was shocked by this finding. It meant that the automatically generated page titles that needed a fix and already led to a drop, were performing better than the emotional page title content.

The key takeaway

This test taught me a lot, but I want to focus on the most transferable elements instead of the vertical-specific.

Content matters. Whether it’s a landing page, a page title or a search ad, the words you choose will be read by someone at some stage, and impact their decision-making. We so often focus on sales conversion, that we forget the micro-conversions along the way that turn a searcher into a customer.

Test everything. I could have just trusted my gut, said focus on the sales language, and been done. But instead, I opted to test a few ideas out at once to find what worked in the real world, not just what I felt or thought would work.

Check your tires. Just a friendly reminder that it’s worth checking your tires and investing in good ones.


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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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