MARKETING
The One Thing Content Marketers Love and Hate More Than Writing
Writing is a little like working out for me – I don’t love it, but I love having written.
I feel the same about technology. I don’t love enterprise content technology, but I enjoy having “technologied.” (Is that a thing?)
I know a lot of content marketers feel the same way – about writing and content tech. The ambivalence content marketers feel toward technology arises because so many of us work with a tech stack that wasn’t designed for content’s specific needs.
As I (too often) find myself telling clients, “That marketing tool can do that. But it’s not built to do that.”
Most people in #Content have a love-hate relationship with technology – because they’re using tools that weren’t built for their needs, says @Robert_Rose via @CMIContent. Click To Tweet
Why martech isn’t the (only) answer
Technology to improve the operations of marketing and communications isn’t new. In the 1980s, database marketing systems helped store customer information and enable pre-digital marketing efforts like mass mailings. I remember helping my mom figure out how to do a mail merge with Lotus 1-2-3 and WordPerfect. (If you haven’t had to hit Alt-F3 to “reveal codes,” have you ever really written?)
It’s easy to forget that the explosion of marketing technology in the early 2000s was born out of the drive to automate sales. (There’s a reason for the company name Salesforce.)
The marketing technology stack evolved out of the growing complexity and importance of digital marketing and the evolving sophistication of the technology enabling it.
Salesforce automation, email marketing, analytics, and digital content management became core to the not well-defined martech stack – a set of software intended to make marketing operations and processes more scalable, effective, and measurable.
There are myriad definitions of categories that belong in the martech stack. Scott Brinker’s famous MarTech 5000 chart documents more than 8,000 solutions across a half dozen categories.
The typical enterprise marketing stack might include tech for:
- Marketing resource management
- Content management
- Email marketing
- Marketing automation
- Social media publishing
- Customer relationship management
- Web analytics
But don’t @ me about the 14 other categories I missed. That’s the point. The marketing stack is a bit like digital marketing: ill-defined and anybody’s guess.
Why content needs its own stack
Content operations need different technology now that content has emerged as a distinct function from sales automation and CRM (just as digital marketing did).
Content strategy (when it exists) typically lives somewhere in the marketing and communications teams. But there are content professionals in many other parts of the business. Technical writers, content strategists, writers, multimedia experts, even sales and other executives all have a role in content. (I often say it’s easier to count who doesn’t create content these days.)
This dispersion across functional groups creates tension as content becomes a more strategic and complex function in the organization. Content professionals have to balance:
- Unique governance structures
- Scalability
- Workflow
- Content creation
- Content management
- Activation
- Measurement
- Repurposing
Unfortunately, the tools at their disposal have been chosen and implemented as part of a classic martech strategy. It’s not wrong, per se. But it’s designed to optimize marketing operations, not content operations.
The tools at most #Content teams’ disposal were chosen to support marketing operations, not #ContentOperations, says @Robert_Rose via @CMIContent. Click To Tweet
Content tech is the athleisure wear of technology
So, where does that leave us?
Organizations need to adapt the classic martech solutions to include technologies designed to optimize content operations.
Some of the unique attributes of the content technology stack might include tools built for:
- Editorial content and workflow collaboration. An entire content process happens before production tools put it into final form. Specialized technology exists to help with ideation, collaboration, intake, calendaring, workflow, and measurement of the actual content creation process.
- Content and asset management solutions. What is raw content, and what is an asset? These questions have unique answers given the context of a business’ content strategy. But you need different technologies for each. And I don’t mean you should shoehorn content or assets into whatever the company uses to manage the corporate website. The days of thinking that one enterprise content management solution can (or should) rule them all are over. The tech needed to support artificial intelligence-driven, personalized mini content experiences differs from that required for static corporate websites.
- Content operations optimization. Content operations differs from marketing operations. The challenge of managing multiple resources, freelance teams, media management, and content projects is different.
- Creation and enforcement of guidelines and standards. Pervasive technology that integrates into all the different tools (CMS systems, Microsoft Word, Google Docs, etc.) can help content creators adhere to content and editorial guidelines, standards, and playbooks. Technologies exist that can help content become more standardized by suggesting SEO changes, enforcing brand and editorial style guides, etc.
Developing, managing, and optimizing a content operation in business is a pressing concern. And it’s not going away.
The convergence of content marketing, content strategy, and content operations makes me think of the recent growth of athleisurewear in fashion. Both are now so pervasive they’re not trends any longer. They’re just how people work (and dress).
As you evolve your content strategy, ask for the technologies that can make your work and processes a heck of a lot more comfortable.
Ask for #Content technologies that make your work a lot more comfortable, says @Robert_Rose via @CMIContent. Click To Tweet
The upgrade will (to tweak a phrase from Lululemon marketing) enable you to move with confidence and comfort.
Get Robert’s take on content marketing industry news in just three minutes:
Cover image by Joseph Kalinowski/Content Marketing Institute
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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