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How To Reinvent Your Content Discovery Strategy in the Age of AI

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How To Reinvent Your Content Discovery Strategy in the Age of AI

ChatGPT, Bing, and Google’s Bard grab the headlines. The evolving use of AI-generative content tools prompts fear, excitement, and chaos among marketers.

It’s clear why. You need help. Forty-six percent of marketers say one person (or group) is in charge of their organization’s content calendar. Who wouldn’t want to taste the AI apple?

More importantly, you need to get your brand’s content where your audience is. Even with its limitations, machine learning has changed how many people search. Google has long used AI to deliver the right answers so searchers don’t have to click for more information. AI content generators, like ChatGPT, also attract a share of searchers who prefer those tools for more detailed answers (that don’t require a click.)

How do you adapt to get your content discovered by your targeted audience in this AI-entrenched world? Take a pause to reflect and strategize.

How do you get your #content discovered by your targeted audience in this #AI-entrenched world, asks @ahaval via @CMIContent. Click To Tweet

AI brings limitations

Start the update for your content discovery strategy by understanding the downside of AI tools. Consider these three factors:

1. Machines can’t understand intent

Social media and search algorithms are improving at offering readers the content they want. But understanding user intent remains a work in progress and always will.

Let’s say someone searches for “jaguars.” Do they want information about the animal, the Jacksonville, Fla., football team, or the British car manufacturer? Google wouldn’t know, and it wouldn’t ask them to clarify. It would take its best guess, and the searcher would likely need to refine their search at least once.

A machine can’t know definitively what a reader wants, so people must refine their searches. That data feeds into the AI tool to improve the algorithm, but it will never be perfect.

2. Nuance is lost on AI

A machine struggles to understand nuance. It communicates complex topics in a black-and-white way. As the “father of modern linguistics,” Noam Chomsky explains:

[Machine-learning programs’] deepest flaw is the absence of the most critical capacity of any intelligence: to say not only what is the case, what was the case and what will be the case — that’s description and prediction — but also what is not the case and what could and could not be the case.

Machine-learning programs neglect to communicate with nuance. They “learn” from the online information, accurate or not, and fail to view complex situations from multiple lenses.

3. AI spreads misinformation and bias

Chatbots like ChatGPT and Bard can’t decipher what’s accurate information and what’s fake news. Bard made headlines when it got a fact wrong about the James Webb Space Telescope in its first demo. The AI tool made a mistake because it scraped and spat out misinterpreted news, extending the life cycle of false information.

Bard made the mistake of saying the telescope took the first pictures of a planet outside of this solar system in this tweet:

At the same time, machine-learning programs are “trained” by people, so human bias is a real concern. For example, someone trained a computer model created to identify melanomas with clinical images. Unfortunately, 95% of the images in the training data set depicted white skin, which begs the question, “Would the computer model miss or over-diagnose skin cancers in patients of color?”

AI-created content can be wrong, biased, and misused. Therefore, it needs to be fact-checked. You can better address the challenges in your content discovery strategy by understanding them.

How to succeed in today’s search landscape

As companies like BuzzFeed and many others flood the internet with AI-created content, you can stay on higher ground by following these five steps:

1. Optimize your content for discovery

Algorithms personalize the user experience more than ever through the platform’s discovery page (i.e., TikTok’s for-you page). So make your posts “discoverable” on social media and Google Discover to help your followers and others in your target audience see your content.

One of our clients, Amanda Todorovich, executive director of content marketing at Cleveland Clinic, shares how to do that on social media, including:

  • Use creative, algorithm-friendly formats (such as video).
  • Catch the scroller’s attention with visuals and hooks.
  • Deliver quick and actionable value to readers.

 

Of course, you don’t need to stop there. Encourage readers to engage with your social posts by adding calls to action and tagging the content to match their intent. To improve your chances of Google Discover featuring your content, follow its content policies and recommendations.

2. Prioritize your audience

Pull your data to answer two questions about your target audience:

  • Where do they hang out online?
  • How, and most importantly, where do they search for information?

Dig into your brand’s Google Analytics, social follower demographics, CRM data, and insights from your sales team. Let the data drive your strategy and determine where the leads come from. Then, focus more of your efforts on those channels.

Use your data to find out where your target audience searches for information online, says @ahaval via @CMIContent. Click To Tweet

TIP: Search is multifaceted. Yes, your audience can find your content on the page of a search engine. But they could just as easily find your content on social media channels.

3. Create with journalistic integrity

The potential for misinformation to multiply in an AI world makes readers’ trust hard to earn. Be a reliable source of information by:

  • Supporting your claims with research and subject matter experts.
  • Citing vetted primary sources.
  • Adding the publishing date to your articles.
  • Approaching complex topics through multiple lenses.
  • Covering topics with appropriate depth, including counterarguments and avoiding generalizations.

4. Plan content that can be repurposed

Staying competitive (and discoverable) with chatbot-based content mills requires multiple forms of content. Repurposing is the solution.

As you create long-form content, consider subtopics that can spin into other forms. Scrape the best moments or insights and bring them to life in a new way:

  • Spin podcasts into YouTube Shorts or Reels.
  • Turn blog articles into newsletters.
  • Transform research findings into social media posts.

5. Stay true to your brand

Stand out in the crowd of content by honing your brand’s voice. Don’t sound like a robot — your readers will notice if every sentence has three clauses. Don’t sound like your competition, either. They’re all spinning content about the same topic. Instead, write about issues that truly matter to your customers in a way that they want to consume them.

Overcome the content discovery challenge

Finally, remember that chatbots and algorithms have a long way to go before they can:

  • Understand user intent.
  • Communicate with nuance.
  • Decipher what’s true or false.
  • Check biases.

So lean into what makes your brand sparkle, and you’ll add value to the content marketplace and continue to build trust with your audience. Be consistent in how you show up online, and you’ll become recognizable in a roiling sea of sameness.

All tools mentioned in the article are identified by the author. If you have a tool to suggest, please feel free to add it in the comments.

Want more content marketing tips, insights, and examples? Subscribe to workday or weekly emails from CMI.

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Cover image by Joseph Kalinowski/Content Marketing Institute



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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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