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8 tips for using AI to create the high-quality content your audience deserves

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8 tips for using AI to create the high-quality content your audience deserves


If the relentless appetite of modern audiences isn’t keeping you up at night, you’re probably not a content creator. It’s a reflection of the times we live in: hyperconnectivity coupled with digital platforms that are unapologetically designed to addict their user base. Audiences expect more… and more… and more… by the day, by the minute, sometimes even by the second. It’s a challenge of exponential velocity—a challenge that weighs heavily on today’s marketers to produce fresh, engaging content at rates they never dreamt possible.

And so, the verdict is clear: content marketers who don’t take advantage of AI-generated content tools to maintain velocity will trail the wake of those that do. But the content marketers who know how to use AI to enhance their content creation efforts—not just in quantity but also in quality—will be the ultimate victors. What’s the point of a loaded editorial calendar if it lacks the quality your audience deserves?

In this article, we’ll delve into 10 ways that AI content generators can help expedite your content creation processes while also meeting standards that will engage and resonate with your target audience.

1. Audience research

Understanding your audience is a necessary first step for crafting content that truly resonates, and AI can help provide information about specific target personas—not just basic demographics, but also psychographic information on their behaviors and preferences.

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Break down your target audience into individual personas and ask your AI content generator questions that add more color around who they are and what they care about. You can even use this as a jumping point for deeper research. For example:

  • What’s their average age?
  • What do they do in their day-to-day role?
  • What are their biggest challenges and pain points?
  • What do they care about most?
  • What do they want to achieve?
  • What is their success measured against?
  • What are their preferred content formats?
  • Where do they go to find helpful guidance and information?
  • What are some common tools they regularly use?
  • What are their values, beliefs, hobbies, or habits?

2. Keyword research for SEO

Now that you have a better understanding of your target personas, AI can go a long way to help improve search rankings. AI content generators can analyze large amounts of data from search engines, social media, and online platforms to generate relevant and targeted keyword suggestions. They can also identify long-tail keywords, search intent, semantic variations, contextually related terms, and keyword clusters to help you optimize content for a broader range of search queries.

To take things one step further, AI content generators can perform competitive analysis to identify keywords that competitors are ranking for. Understanding the keywords that drive traffic to competing websites will help you identify gaps in your own content strategy and discover new keyword opportunities.

3. Trending topics for content ideation

Content marketing thrives on relevance, and keeping up with trending topics can be a time-consuming process. By analyzing data from diverse digital sources—be it social media platforms, blogs, news sites, or forums—AI can help identify emerging patterns and topics that are gaining traction. This real-time analysis helps keep you updated on what your audience is currently interested in to swiftly adapt your content strategy.

But it doesn’t stop there. AI can also provide preliminary drafts or suggest content angles on these trending topics. With the ability to analyze previously successful content structures and the specific interests of a brand’s audience, AI can recommend approaches that are more likely to resonate.

4. Background research on not-so-familiar topics

They say write what you know but what if you don’t know it all? Content marketers are often tasked with writing about a diverse array of topics, some of which they may not be intimately familiar with—but you no longer have to spend hours or even days trawling through multiple sources for foundational understanding. Use AI as your digital research assist—an efficient means to bridge the knowledge gap. Within moments, you’ll be presented with a comprehensive overview of any topic, highlighting key facets, historical context, contemporary relevance, and the latest data or insights associated with it.

But beyond just data retrieval, AI content generators can structure and present this information in a user-friendly manner. By analyzing the kind of content that resonates with readers in a given niche or industry, the AI can tailor its outputs, ensuring that even the densest of topics are made accessible and engaging. This not only ensures that your pieces are informed and accurate but also structured in a way that captures and retains reader interest.

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5. Editorial calendar planning

Catering to diverse audience preferences across various formats—be it blogs, podcasts, videos, infographics, or social media posts—is crucial. And planning a comprehensive editorial calendar that spans these formats requires meticulous strategy, foresight, and a deep understanding of audience behavior. AI can understand patterns in audience engagement, preferences, and content consumption habits across different mediums. Based on these insights, an it can recommend a structured editorial calendar that strategically intersperses different content formats. For example, if the AI identifies that video content sees peak engagement on weekends, while long-form articles are consumed more during mid-week, it can schedule accordingly, ensuring that content is not just high-quality, but also timely.

An AI-driven editorial calendar doesn’t just allocate formats based on broad trends, it tailors content topics for each format based on what has historically resonated best with target audiences. By analyzing metrics like shares, comments, and dwell time, the AI can discern which topics are best suited for video discussions, which ones make compelling infographics, and which are ripe for deep-dives in blog format. This intricate meshing of topic with format ensures that the content isn’t just diverse but is also optimized for maximum engagement.

6. Content outlines

A robust content outline serves as the blueprint that guides overall flow, ensures comprehensiveness, and ensures all key points are addressed. Use AI to help you produce outlines based on any given topic, for any given content format. For varied marketing assets, whether they’re whitepapers, articles, or video scripts, AI content generators can customize the outlines based on the specific nature and intent of the asset. Fore example: If you’re planning a webinar, AI can help suggest a format that balances visuals with key talking points. If you’re planning a detailed user guide, it might recommend an in-depth, sectioned approach with introductions, body content, and conclusions. Now you’ll have a tailor-made, strategic scaffold that ensures consistency across all produced content.

7. Content generation

Now we get to the good part: the actual generation of the actual content. The six tips before this are all building blocks that allow you to knock it out of the park when asking your AI tool to generate content. Because you get what you give, the secret to getting a great first draft from your content generator is to brief it how you’d personally like to be briefed. The more information, the better. Here’s a helpful start:

  • Put the bot in your shoes: Prompt your AI by having them live a day in your life. This will generate content that’s more specific for what you’re trying to achieve. For example: “You’re a content marketer at Optimizely writing an article about a new product feature.”
  • State the desired format: If you’re writing for a particular content format, mention it. “Write a listicle on the benefits of organic SEO.”
  • Set the tone: If you want the content to carry a certain mood or tone, specify it. For instance, “Write a humorous guide on common email marketing blunders.”
  • Define the audience: Make sure to specify the target audience. For examples “Write an introduction to content marketing for small business owners.”
  • Limit your length: For more concise answers or to get a summary, indicate a desired word count or ask for a brief explanation.
  • Ask open-ended questions: To extract more detailed and expansive answers, frame your prompts as open-ended questions.
  • Provide context: The more background or context you give, the better the AI can tailor its response. If you’re continuing from a previous topic or if there’s a unique context to your request, mention it.
  • Ask for multiple angles: If you’re unsure about the exact angle you want, you can ask the AI to provide multiple takes on a topic. For example: “Provide three different introductions for a blog post on digital marketing trends in 2023.”
  • Review and refine: AI is a tool, and its outputs will always need human touch-ups. Once you get the content, review it for alignment with brand voice, accuracy, and relevance. The output can be an excellent starting point that you can then refine to perfection.

8. Content refinement

Existing marketing content can sometimes lose its relevance or impact as market dynamics, audience preferences, and keyword trends evolve. AI can help analyze a piece of content and cross-reference it with current keyword trends, search intents, and audience behavior patterns. For example, an older blog post might have solid foundational information but may lack recent keywords that have gained prominence. Your AI content generator is also great at identifying gaps or outdated information. It can recommend additions or modifications, breathing new life into your content arsenal.


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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.”

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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.”

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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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