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Optimizely content has always been headless

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Optimizely content has always been headless



The idea of “separating content and presentation” is sacrosanct in content management. It’s considered one of the core benefits of using a content management system at all. It’s as close to a universal principle as we have in this industry.

Peer-reviewed white papers have been written about this. The one linked here even traces this concept back to Aristotle’s ideas of logo and lexis – the content of a speech and the style of rhetoric used to deliver it – proving this idea has literally been with us since the dawn of civilization.

The implied benefit is that your content can stay “pure,” meaning free of any applied presentation. Your content is stored as pure data (in the back end), completely ignorant of how it might be presented.

When you want to deliver content to a channel, you apply some presentation, rendering, or template, and transform it into a form that’s easy to consume in that channel:

  • Content bound for a web site gets turned into HTML via some templating language (Razor is common in .NET)
  • Content bound for print gets turned into a PDF, via XSL-FO or PrinceXML
  • Content bound for SMS might get truncated and concatenated with a URL

The possible channels are endless, and still growing.

The most common use of a traditional CMS for the last couple decades has been to manage and maintain a website (think WordPress). The idea of content is universal and enduring, but for a long while, websites were pretty much all anyone did. We all talked a big game about “content re-use” and “multi-channel publishing,” but for many years, the web was the only thing we were concerned with.

Lots of “web CMSs” (WCM or WCMS) popped up to meet this need in the late 90s and early 2000s. They qualified their names with “web” or “W” to differentiate themselves from the enterprise content management systems (ECM, commonly use for internal company content) and document management systems (an outdated term) that dominated the market prior to that time.

Some systems embraced this role with lots of web-specific functionality. They provided pre-built HTML widgets and built-in web navigation management systems and all sorts of things that were very specific to publishing HTML. Lots of them baked these features in so deep that they became inextricable from the web channel. They didn’t manage “pure” content anymore – all stored content was saturated with web-specific features, so it was less useful elsewhere.

Optimizely never did this.

Since its founding, and to this day, Optimizely manages pure content. It makes no assumptions about what you want to do with that content. Optimizely has always believed in the separation of concerns. Optimizely’s repository can be said to be “abstracted” away from web delivery.

The relationship between Optimizely content and HTML published to the web is loose. In an Optimizely instance, content is pure until literally the last moment it goes out the door. Only at that point does Optimizely apply a template to turn it into HTML, and this process can be easily side-stepped.

This was true long before we had a Content Delivery API, or any explicit headless CMS functionality. Almost a decade ago, I was working on Optimizely projects that would be called “headless” today, long before that word was on anyone’s lips or feature checklists.

(Some nostalgia: back in the Web Forms days, we translated Optimizely content into XML from Generic Handlers. This was before MVC, before WebAPI, and even before JSON got heavy adoption. Good times.)

So, if you don’t want to render HTML…don’t. If not HTML, how would you render the content? Well, that’s up to you and your requirements. Build your own front-end framework if you’d like, the sky’s the limit.

Of course, layered on top of this content foundation are lots of tools to make it easy to create and deliver a website. In a sense, our core content management features are “wrapped” in website management tools. You can work with your content at the pure level, or take advantage of things like URL management, page composition, and other things that only make sense on the web.

To prove this, I wrote some code. I’ve made it available on GitHub here:

Managing Non-Web Content in Optimizely

This is some sample code showing how to use Optimizely’s API to manage content that might never see the inside of a browser. There’s a README file that explains the technical specifics. It’s anti-climactic, by design. It looks a lot like the code used to manage all other Optimizely content. This is not a coincidence.

From the README:

Optimizely content is based on a hierarchy of classes. The traditional “web” content is actually built on a foundation of more pure content representations.

These objects will never appear in Edit Mode, nor will they have URL – and they shouldn’t, because they’re not web content.

They’re just…well, content.

Not only are these objects easily manipulated with Optimizely’s API, we offer a UI to edit them: Content Manager. It’s quite simple to configure Content Manager to show you a simple grid or table view of this non-web content, and allow you to create, edit, and delete objects. Additionally, this content is fully available over our Content Delivery API.

Put another way: there’s a fully featured headless CMS living inside a more fully-featured web CMS. If you want the best of both worlds, here you go: use the web tools when you want, and when you need something more generic, well, we’ve always done that.

As I note in the README:

This is not a new feature. It’s not even new code. This functionality has been in the product for years.

In Optimizely CMS, not all content is web content. The fact that we use content to generate a website is almost incidental.

It’s your content. You decide what you want to do with it.



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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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