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Marketing operations and technology shouldn’t ignore the web

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Marketing operations and technology shouldn't ignore the web

During the past few years, after I transitioned formally and more fully to the marketing operations and technology space, I’ve continued to feel a bit of a misfit. At least through my anecdotal observations, most folks in this community have specialized in marketing automation and adjacent subspecialties, and there is nothing wrong with this. However, as a person who gained experience working mainly with content management systems (CMSs), and administering similar systems like form builders and community platforms, those are the people to whom I can more closely relate.

Recently Darrell Alfonso, a marketing operations thought leader and MarTech contributor, asked on LinkedIn what people think is the most underrated platform or system in martech. While he had a limited number of slots for a LinkedIn post-poll, he didn’t mention web systems. Although I began drafting this column before he posted that poll, I feel that the community doesn’t focus on web systems as much as it should.

While the marketing operations and technology community doesn’t ignore web systems, it could benefit from focusing on them more.  Such systems aren’t just for the creative folks with copywriting, wordsmithing, and video-making skills.  They are integral parts of martech stacks that, with vision, commitment and orchestration, can empower bold strategies using components throughout the stack for impactful and effective multi-channel campaigns.

Websites are far more than just places to host content, solicit personal information, and collect data.  In many cases, they’re prominent parts of customer journeys.  For instance, when systems like DAMs, CDPs, and DSPs are properly set up, customers can see consistent messaging and imagery across channels.  When the customer is on a sporting goods e-commerce site, their site behavior can indicate their propensity to buy (specific sport or activity, cold vs. hot weather, male vs. female, apparel vs. equipment, etc.). Orchestration ensures that the online ads, the emails, paid social media posts, and the imagery and messaging they encounter during website visits and return visits, can all focus on what they expressed interest while surfing (get it?) through the site.

More control than other settings

Notably, marketing teams have far more control over setting up and presenting websites than they do over other channels. Ad networks (both digital and analog), social media networks, trade shows, and other channels place significant constraints on how organizations can present themselves. 

Granted, browser and device providers, in addition to regulators, do place constraints on websites, but marketers have far more leeway than in many other channels.  So, why not take advantage of this?

The power of experimentation

Insights from one channel can certainly apply to other channels.  Since there is this considerable leeway in using the web channel, it makes a great place to try things out.  For instance, testing – user, A/B, and multivariate – is not only an excellent way to conduct conversion rate optimization, it is a good place to test hypotheses that can help guide tactics for multiple channels.

Such hypotheses can involve messaging and imagery.  Different testing tactics can help identify important insights.  For instance, user testing can help marketers get into the head of customers; this tactic can include interviews and having testers speak their thoughts aloud while their sessions are recorded.  While it’s impractical to conduct user testing at a large enough scale to quickly draw actionable conclusions, it can help generate experimentation ideas for A/B and multivariate tests.  It is easier to present and examine how large groups react to various possibilities for content, UI elements and site flow via such tests. In turn, the data from these experiments can apply across multiple channels, not just the web.

A multi-channel view

In addition to testing, the community can consider how well the web channel can fit within multi-channel orchestration. For instance, if marketers notice that a certain audience segment responds well to some targeting, there are a variety of tactics to consider.

These can range from imagery that better reflects the segment (having people in images and videos who look like the segment members), to special deals or pricing and terms (organizational partners, public servants, and campaigns). With some orchestration using systems like analytics, CRMs, CDPs, DSPs, and DAMs, the web aspects of the customer journey can match the experience with the other channels.

Read next: Does your organization need a headless or hybrid CMS?

Don’t forget the web

The marketing operations and technology community does an excellent job talking about marketing automation, analytics, online marketing, and CDPs. Still, there’s a lot of value in considering these channels in concert with the web channel. Marketing operations and technology span the entire customer journey, and leaving the web portions mainly to our creative colleagues doesn’t serve anyone well. And remember, our creative colleagues are valuable collaborators in our more traditional channels. So, the aim is to partner, not commandeer.


Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.


About The Author

Marketing operations and technology shouldnt ignore the web
Steve Petersen is a marketing technology operations manager at Zuora. He spent nearly 8.5 years at Western Governors University holding many martech related roles with the last being marketing technology manager. Prior to WGU, he worked as a strategist at the Washington, DC digital shop The Brick Factory where he worked closely with trade associations, non-profits, major brands, and advocacy campaigns. Petersen holds a Master of Information Management from the University of Maryland and a Bachelor of Arts in International Relations from Brigham Young University. He’s also a Certified ScrumMaster. Petersen lives in the Salt Lake City, UT area.


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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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