MARKETING
Is Bing Carving Away at Google’s Search Dominance?
Ever since Microsoft announced in February that Edge and Bing would be powered by ChatGPT, observers have wondered if Google’s search dominance would hold up. If other sites like Bing could provide a superior AI-driven experience, the thought goes, maybe, just maybe, Google’s lead in the realm of connecting the US population with information would start to slip away.
Looking at how two sources of interest in Google compared to Bing have trended over the last few months, we see competing stories for how the AI revolution has moved the needle.
StatCounter Shows Google’s Dominance Has Only Grown
One source of US search engine market share by platform is StatCounter, which bases its calculations on every page view referred by a search engine to the more than 1.5 million sites its tracking code is installed on globally. No source is perfect and all should be taken as directional indicators, but StatCounter is a widely-used source for this kind of information and its search engine market share numbers in particular have long looked in line with expectations.
Looking at how things have shifted since Bing’s announcement, Google’s share went up from 88.13% in January to 89.1% in May. Over the same time frame, Bing’s share actually fell from 6.67% to 6.36%.
As such, this is an early indicator that Google may actually be slightly expanding its dominance in the US search market, despite Bing’s best efforts to make hay out of the AI news cycle. However, looking over at a source of Google’s own making, there are more positive signs that Bing’s moves might be attracting new users.
Google Trends Showing Microsoft Bing Interest Growing Relative to Google Search
Google Trends provides relative search interest over time, and while hard values for volume aren’t provided, we can get a sense for not only how popular a search term or topic is, but also how popular an entire website like Microsoft’s Bing or Google’s search engine is at a given time compared to other options by comparing the normalized values provided. These values are always whole numbers, and thus aren’t but so precise in terms of providing users the ability to quantify such comparisons, but can certainly be used directionally.
Comparing Microsoft Bing and Google Search website interest over the last twelve months, the gap in interest between Bing and Google has gotten a bit smaller since the February announcement. One way to visualize this is to divide Bing’s interest by Google’s to provide a simpler relative interest than what’s provided directly in Google Trends. If Google’s relative interest for a given day was 80 and Bing’s was 5, this would yield a result of Bing producing 6.25% as much interest as Google.
The chart below shows that Bing’s relative interest grew substantially in the middle of February after its big announcement. Interest has since waned relative to Google search, but remains meaningfully above where it was prior to the ChatGPT announcement most weeks.
Conclusion
Any source showing relative demand for Bing and Google is bound to come with its fair share of caveats, and marketers are going to have to think critically about the types of analyses shared over the coming months when it comes to how much stock to put in particular data sets. Looking at StatCounter and Google Trends, we find conflicting stories, with one showing Bing search engine market share slightly down and the other showing Microsoft Bing interest slightly up.
Microsoft itself has been modest in its public statements regarding the impact of its ChatGPT integration, stating that its accomplishment of crossing 100M DAU announced in March was aided ‘with a little bit of a boost from the million+ new Bing preview users.’ One million users is a nice bump, but pretty small in the grand scheme of things. Small gains are still appealing for Microsoft, with its CVP of Finance noting that just one point of share gain in the search advertising market is a $2 billion revenue opportunity.
Google is also quickly maneuvering to integrate generative AI into its own search experience, and touted that its tools have relied on AI and machine learning for years. The arms race will only continue to heat up as it becomes clearer what users actually want moving forward.
Regardless of whether you put more stock into the StatCounter or Google Trends data presented here, the reality is that neither source is a strong indicator that the game has meaningfully changed so far, and that’s not that surprising. According to a recent Pew Research report, just 14% of Americans have used ChatGPT for entertainment, to learn something new, or for their work, and the engagement of average users with AI is still in its infancy. As such, we shouldn’t expect current indicators to reveal much about the future potential for disruption, and it will take time and adoption for the true form of this powerful technology to take shape.
For marketers, the key will be keeping tabs on this progression while not getting too caught up in the immediate hype. As the saying goes, many are likely to overestimate the impact in the short run but underestimate it in the long run. Make sure you’re playing the long game as you evaluate how the search game is evolving.
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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