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5 AI Tools to Transform Your Podcast Production & Marketing Process

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5 AI Tools to Transform Your Podcast Production & Marketing Process

After years of speculating about whether the buzz around AI was justified, by now, most of us as marketers and entrepreneurs have accepted that the promise of AI-enabled tools is real.

It’s clear that AI technology will impact (and perhaps radically transform) almost every aspect of our work and lives.

Podcasting is no exception.

And while there are certainly challenges AI will present to us as podcasters, content creators, and marketers, there are also some incredible benefits.

Specifically, those benefits revolve around the ways in which AI will make our jobs easier when it comes to creating, repurposing, and marketing our shows to get them in front of our ideal listeners, and ultimately, clients and customers.

So in this article, we’ll look at 5 AI podcasting tools you can start using today to create a better show and reach more people for less money and in less time.

1. AI-Generated Podcast Show Notes

If you’re like most podcast hosts, writing show notes for each episode is likely one of your least favorite parts of the production process.

Fortunately, a growing number of AI-writing assistants like Capsho have emerged to help take this time-intensive (not to mention boring) task off your hands.

All you have to do is upload your finished audio file and Capsho will automatically write your episode’s show notes, suggest a title, create a transcript, pull out quotes, generate social captions, and even repurpose your episode into a blog post, newsletter, and LinkedIn article.

Keep in mind you’ll still need to do some manual tweaking to polish off the AI-generated, but for many hosts, Capsho, or similar tools like Swell or Podcast Marketing.ai can get you 90% of the way there.

2. AI-Generated Podcast Promotional Assets

One of the best ways to drive awareness of your show is by repurposing it into short-form video.

And while manually combing through your episodes in search of the perfect clip can be a painstaking process, once again, AI has come to the rescue.

If you’re already recording video for your podcast, Vidyo is a nifty tool that automatically identifies, pulls, and captions compelling clips from any video file you upload.

In addition to a number of high-quality, customizable templates, Vidyo also has a nifty feature that automatically cuts between speakers, helping keep your viewers engaged on longer clips.

If you don’t record video for your podcast, not to worry.

Dubb is another podcast repurposing tool that generates AI-created background animations & transcripts from your uploaded audio files to create unique and engaging videos instantly.

3. AI Podcast Post-Production

So far, we’ve explored AI tools to help you repurpose your finished episode recordings. But there are a number of tools designed to make creating a high-quality show easier in the first place.

Descript has been on the scene the longest and has a whole suite of editing features including AI-generated transcript creation, audio clean-up, automatic filler-word removal, and more.

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But while Descript certainly has a powerful feature set, it’s far from the only option.

Perhaps the most intriguing new tool is Adobe Podcast.

While it’s currently still in beta, Adobe has released a couple of features for free, including their jaw-dropping Enhance Speech tool, which uses AI to transform almost any recording into an NPR-quality finished product (seriously).

With tools like Enhance Speech, the barriers to entry in terms of recording gear, software, and technical know-how is rapidly dropping, meaning one less layer of friction between your ideas and your audience.

4. AI Voice Generation

One of the most fascinating applications of AI when it comes to podcasting is AI voice generation.

Maybe you’re a writer who would love to turn your blog posts into podcast episodes but doesn’t have the time, for example.

No problem.

With Listnr, you can simply upload your text, choose your voice (currently over 70 languages available with dozens of accent variables in each), and generate an audio version of your article in minutes.

But say you want to make things a bit more personal.

In addition to its post-production and editing tools, Descript’s Overdub feature allows you to train an AI voice model on your own voice.

Once the model has been trained, you can use Overdub to do everything from typing in a replacement word for one you flubbed during the recording to generating new custom audio content based on text.

5. AI Video Alteration

Our final entry into the list might not apply to every podcaster, but it’s no doubt one of the coolest AI tools I’ve come across to date, especially if you record video for your podcast.

When it comes to production quality, there’s no denying that eye contact with the camera is one of the most important factors to consider.

For solo videos, eye contact allows for a more personal, intimate experience for your viewers. For interviews, that depth of connection extends to your guest as well, leading to better interviews, and a better experience for both them and your audience.

The problem (as we’ve all experienced on countless awkward Zoom calls) is that you can’t look at the camera and your guest’s video (or your script) at the same time.

To solve this, most serious video podcasters and YouTubers eventually opt to invest in bulky, often expensive teleprompters.

With NVIDIA’s new Eye Contact tool (a part of their Broadcast app), however, teleprompters might soon become obsolete.

The tool uses AI to seamlessly edit your video feed in real time so your eyes always remain locked on the camera, even while you’re taking notes or reading from a script.

Honestly, it’s kind of mind-blowing.

Unfortunately, Broadcast is only available for Windows right now, though I have no doubt we’ll be seeing more tools like this emerge for other operating systems soon enough.

How Will AI Impact Your Production Workflow?

How Will AI Impact Your Production Workflow?

The AI arms race is well underway, both in terms of the development of new technologies and applications, as well as the adoption of those tools by small businesses, marketers, and creators looking to get a leg up on the competition.

While there are certainly still kinks to be ironed out, it’s clear that AI-enabled tools have emerged as viable options to help us create better content faster and cheaper than we could just a couple of years ago.

If you’re like me, this has you excited.

It means we can spend less time on the menial admin work that doesn’t make use of our unique talents and perspective, and more time on the creative, ideas-driven work that only we can do.

And it’s that work that will ultimately allow us to move our businesses forward.


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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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