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How Planning To Fail Can Succeed [Rose-Colored Glasses]

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How Planning To Fail Can Succeed [Rose-Colored Glasses]

An interesting question came up recently in a marketing group I follow on social media: “What content should we create?”

The first few comments on the post were what you might suspect. Some people encouraged the poster to interview people who fit their personas to find out what they struggle with. Others talked about getting over writer’s block. A few suggested they list every question their potential customers might have and write posts about that.

The original poster responded by acknowledging the value of these responses but clarified the question. They weren’t asking what they should write that would resonate with their target audiences. They were looking for content ideas that would drive the most reactions. Full stop.

They wanted to create controversy, provocation, and a level of virality. The theory: Do something that makes a lot of noise and inspires a boatload of people to react, then the right people will pay attention to your other content that focuses on the things you do.

Predictably, the tone of the discussion shifted into a fiery debate of the flawed notion (if not ethics) of that theory. Let’s save that discussion for a different day.

But it got me thinking. Is there a case where it makes sense to deliberately put out content you don’t like, agree with, or approve of – with the explicit goal of failing?

My answer is yes.

Is there a case where you should publish #content with the explicit goal of failing? Yes, says @Robert_Rose via @CMIContent. Click To Tweet

Why intentionally failing can be a good thing

We all know failing can be a productive result. There are entire books written on how people tend to learn more from failures than successes.

But this concept almost always gets covered in the context of failing when trying to succeed. In other words, you do your level best to accomplish something – and something in that approach failed. The lesson is that you should have done something differently.

I’m interested in what happens when you deliberately try to fail or at least try something the world deems incorrect. You either confirm what you expected or get surprised by the results.

Of course, some activities lend themselves better to this approach than others. For example, I wouldn’t try to fail while learning to fly an airplane. However, in marketing – and especially content – this approach gives you an invaluable opportunity to expand your toolkit.

According to the book Brilliant Mistakes by Paul Schoemaker, advertising icon David Ogilvy made deliberate mistakes frequently. He would run ads that he and the client team had rejected just to test their collective thinking. Most would fail. But a few, including the iconic eye patch Hathaway shirt ad – would become legendary campaigns.

Think about making time, money, or content available to test your core instincts. I don’t mean running a simple A/B test to evaluate two good efforts. That only determines which one resonates better.

I’m suggesting that you try a content piece that poses an idea you flat-out rejected. Or try investing in a channel that from every angle seems to be wrong. For example, later this month, I’m going to try TikTok. I’m 99.9% sure I will fail spectacularly.

What if you invest in a channel that from every angle seems wrong? Soon, @Robert_Rose will try on #TikTok via @CMIContent. Click To Tweet

But what if I’m wrong?

There’s a famous quote attributed to IBM founder Thomas J. Watson: If you want to increase your success rate, double your failure rate.” It seems to me there’s only one mathematical way to double your failure rate – and that’s if you occasionally deliberately try to fail.

How to fail on purpose

A friend and I had a funny saying we used to tell each other whenever we failed a test at school. We’d say, “I’d rather get a zero than a 59.”

Why? Because getting 59 means we tried and still failed.

Of course, we weren’t saying no one should ever try. We were being silly teenagers.

In content marketing, we know the value of tests and experimentation. However, most testing is done to confirm an initial assumption. In fact, a core piece of an A/B test is to form a hypothesis first. You have a suspected or proven winner, and you test an alternative version to see if it performs better.

Making a deliberate mistake is a bit different. In these are experiments, you assume you’re going to fail.

What could be the value of doing that?

Well, there can be two valuable outcomes. One is that you confirm your assumption of failure and learn something. The other is that you succeed (in other words, you fail at failing) and that long-shot effort might pay off handsomely. Even if it doesn’t, you’ve learned something.

If you make a deliberate mistake and succeed, you not only learn something, but the takeaway could pay off handsomely, says @Robert_Rose via @CMIContent. #ContentMarketing Click To Tweet

There are different flavors of deliberate mistakes. One of my favorites was made by a vice president of marketing at a big B2B company. Over the years, they had accumulated tens of thousands of subscribers to their email newsletter. Every week they would dutifully email almost 90,000 newsletters, and every week, the engagement rates were extremely low.

So, the vice president did something interesting. He sent a large segment of the subscribed but disengaged audience an email with the subject line: Sorry to See You Go.

In the body of the email, the copy told the recipient the company was sorry they’d unsubscribed from the newsletter. But, the text continued, if they thought this unsubscribe might be in error, they could respond by clicking through to a survey.

This move was clearly a deliberate mistake. Certainly most, if not all, of these subscribers wouldn’t engage with the email. But their marketing team decided it was worth making the mistake and risk losing 30,000-plus subscribers to see if they had any shot with this unengaged audience.

The result? About 60% never responded or clicked and got officially unsubscribed from the newsletter. But 40% clicked and responded, “No, this was a mistake.” They hadn’t unsubscribed. For a while, this email had the highest click-through rate.

One other surprising result? Among those who responded, about 10% indicated they were interested in subscribing to a different topic addressed by the company.

The vice president of marketing told me, “We learned a lot from that ‘mistake.’”

There are a few key moments when deliberate mistakes might make sense for your content marketing:

1. There’s less to lose

Obviously, risk plays a role in how big a mistake you should deliberately make. Skydiving, for example, isn’t the best activity where an intentional mistake is likely to pay off. You don’t want to publish a content piece completely off-brand, run afoul of legal or compliance issues, or really offend your audience. But like the vice president of marketing at that B2B company. What could they lose other than a third of the email database that wasn’t engaging anyway?

2. Rigid, institutional rules

When you intentionally make a mistake, it will more likely go your way when the mistake goes against an institutional rule or rigid, outdated convention. A great example of this is the marketing for the movie Deadpool. It was, by most counts, a campaign filled with deliberate mistakes. Perhaps the biggest was its outdoor billboard campaign with a pictogram of a skull, a poop emoji, and the letter “L” with the premiere date. Adweek called the campaign “so stupid, it’s genius.”

Billboard containing a skull, poop emoji, and letter L.

How about that rule that says never publish blog posts on the weekend? Everybody says it doesn’t work, and publishing on those days is a mistake. Why not make that “mistake” and see what happens?

3. You are the newbie

An optimal time to make a deliberate mistake is when you’re new to a particular problem or challenge. It’s when your audience, customers, or colleagues are most likely to forgive your mistake – and then, of course, you can refer to the first moment above.

One of my good friends has been the CMO of several startup companies. He told me that when he joins a new company, he goes on a “listening tour” to hear from the practitioners. He often introduces a marketing newbie mistake into the conversation to see if a practitioner will push back, correct it, or just go with the flow. Certainly, he risks coming across as inexperienced. But, he says, what’s more important is that they start on equal footing, and he can start a dialogue with his new colleague.

Failing to fail to fail

Of course, not every deliberate mistake will end up with a successful outcome. Sometimes, after all, a mistake is a mistake – and deliberately making it will get you exactly what you asked for.

There is only one thing that’s assured: If you only fail when we’re trying not to, you may just miss out on proof you should trust your initial instincts.

Get Robert’s take on content marketing industry news in just three minutes:

https://www.youtube.com/watch?v=videoseries

Rose-Colored Glasses is a new weekly column in which Robert Rose shares his view of content marketing challenges. Every Friday, he offers reasoning, rationale, and rhetoric to help you advance the practice of content marketing in your organization.

Subscribe to workday or weekly CMI emails to get Rose-Colored Glasses in your inbox each week.

 
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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