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Bard and ChatGPT will ultimately make the search experience better

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Bard and ChatGPT will ultimately make the search experience better

The use of large language models like those developed by ChatGPT and Google are going to impact traditional search. There’s no doubt about that — and the changes are imminent.

Some commentators see trouble looming. Chris Penn of TrustInsight recently told us: “(I)f unbranded search is the lion’s share of your search traffic, particularly your converting search traffic, you should be very concerned. That’s where the large language models will be intercepting your traffic and not giving anything to you or giving very little to you.”

Others are more sanguine. We spoke with Brent Ramos, product director for search at Adswerve. “We’re at the precipice of this new frontier for search which will ultimately be better. I’m not taking a pessimistic view at all; I’m very much looking forward to it.”

An established Google partner

Adswerve is a long-standing Google partner, providing services related to Google products to agencies, analysts, marketers and publishers.

“We have a large portfolio of agencies and a very large portfolio of direct marketers across all the products, whether that’s Analytics or Cloud or the media size of the house,” said Ramos. “My focus has been primarily on the search side of things, but of course I’ve touched the GCP side of things.” Adswerve also has an Adobe Analytics practice.

Changing the search paradigm

Repeatedly emphasizing that it’s early days for conversational search and that we don’t yet know what it’s going to look like further down the road, Ramos is optimistic about a new, more immersive and interactive search experience.

“I think it’s definitely going to change the paradigm of how we understand search today, because as soon as you throw in Bard, let’s say [Google’s generative AI], and it pushes down all the organic ranks, and you have the chat AI upfront, all of a sudden people are going to converge and convert in that experience, and it’s too early to know what it’s going to look like.”

In other words, and as expected, many users of Google search will look first at the answer to their query generated by Bard; they won’t necessarily scroll down to look at links, or even at footnotes that show where Bard found its information.

Dig deeper: Does ChatGPT pose an existential threat to marketers?

The implications for paid search

“What I anticipate is that there will be a new paradigm of what that will mean for paid results, so rather than have a host of links to click through in an index format, we’ll see new formats come up. I think the definition of conversions will change, and the experience of the paid ecosystem will change, but it’s not going to go away.”

The far from resolved question, of course, is what the new experience will ultimately look like. But we are on an irreversible journey, Ramos believes.

“It’s not going to go away,” he said. “Technology will very rarely recede. So we know it’s going to become a new paradigm and move search into this new realm. On the long-game horizon, we can expect to see new ways of conversion, new ways of formatting, SERP is going to get a lot busier, website conversions might decline.”

While some are concerned that, if users can get all of the information they need directly from the AI, the index of links, including paid links, will become irrelevant. Ramos insists that this isn’t something new. “We’ve seen that in social, right? People are converting more within the social channel itself rather than landing onto the actual page, especially for ecommerce.”

A living, breathing conversation

That does mean, however, that there will need to be opportunities within the AI content for people to convert, and Ramos doesn’t pretend he yet knows what that will look like. “Maybe it’s no longer pay-per-click; it’s pay-per-interaction,” he said. “Rather than getting this repository of links to sift through as humans, we’ll actually get this really rich, semantic conversation presented to us. The index or repository we’re accustomed to today will shift into a living and breathing conversation.”

The bottom line, for Ramos, is that whatever it looks like, it’s going to be better. “It will eventually be better over all. To me it’s like back when Google and the internet first started coming out — the big industry of the yellow pages, and publishers were like, ‘What are we going to do?’ And then of course it was ultimately a really good thing.”

The importance of interconnectivity

Even traditional search doesn’t just generate lists of links, Ramos observed. “SERP is one thing, but search also powers things like local listings, maps, ecommerce buy-buttons and all these other things that are interconnected with it and that are crucially embedded in the ecosystem.”

From knowledge panels to videos to dictionary definitions and alternative search suggestions: “All these things that are tangential to search are also wrapped up in that connectivity, so I think that’s the bigger picture people should be trying to understand.”

Even so, he admits: “That’s where I imagine it leading, but this is all speculation at this point because this all very fresh.”

Bumps in the road?

Not only do we not yet know how we’re going to get to this rich, interconnected conversation, but we are already seeing teething problems — from truly disturbing behavior from the AI to outright error.

“There’s definitely going to be a bumpy ascent,” Ramos admitted. “The silver lining is that we know it can be done; it’s more a matter of how fast we can architect against it, and it takes a lot of human capital and power to do so.”

What is he telling his clients at this stage in the journey? “The guidance is to understand holistically — and without your own biases about AI — and accept innovation.”

Ramos sees the competition between Google’s Bard and Bing’s ChatGPT-based generative AI as a positive. “We want to see competition in the marketplace — a marketplace that’s rich with innovation — so on both sides of the house I think it’s a good thing, and they should be pushing each other.”


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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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