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Your Audience Isn’t Really Interested in ‘Just the Facts’ Anymore [Rose-Colored Glasses]

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Your Audience Isn't Really Interested in 'Just the Facts' Anymore [Rose-Colored Glasses]

Last week, I spoke to a client in the health care industry whose team wanted to develop a new digital content customer experience. But they felt frustrated.

Five years ago, with help from a couple of ad agency consultants, they’d come up with the idea to launch a digital platform to provide easy access to facts. All they needed, they thought, was to set up a digital library that could answer every question existing clients might have.

They would “let the facts speak for themselves” and win the customer retention battle.

<Narrator>: “It didn’t work.”

You see, facts almost never speak for themselves (they’re bashful that way). And they almost never win an argument.

Think about the last time you presented a set of facts you thought would clinch your argument. Boom. You dropped the mic and the knowledge bombs. You won, right?

Nope. Presenting facts does nothing to correct a false belief, and it usually causes your opponents to double down on their beliefs.

A group of researchers actually have studied this so-called “backfire effect” and found that correcting someone “actually increases [emphasis mine] misperceptions among the group in question.”

The backfire effect indicates correcting someone actually increases misperceptions among the group in question, according to #research from @UMich and @GeorgiaStateU via @Robert_Rose @CMIContent. Click To Tweet

In a big data, deep fake world, we have more “facts” than ever before. The question is: Does anyone care what we have to say?

A few years ago, researchers at Wharton showed people various algorithms. Most people in the study found them interesting and valuable – until an algorithm made a mistake. Once people saw the mistake occur, they were “very, very unlikely to use it and didn’t like it anymore.” Study participants seemed to judge algorithms more harshly than they would people, one researcher noted.

But, if these people had input into the algorithm or were allowed to adjust the forecasts, they not only liked the algorithms more, they didn’t lose nearly as much confidence when an error occurred.

These findings bode well for preserving the role of human involvement in an increasingly automated world. But it also speaks volumes in terms of how delicate belief and trust are.

So, the content question in 2022 isn’t about how to present “just the facts.” The question is how to make people care about any of the facts. And this isn’t just a marketing question. It’s a fundamental communication question.

Increasingly, facts are a commodity. They’re easy to attain, so we don’t value them. And because we don’t value them, they can be assailed with … well … “alternative facts.”

Facts are easy to attain, so readers don’t value them, says @Robert_Rose via @CMIContent. Click To Tweet

As I told my health care client, companies have to give people something to believe in (to quote the classic Poison song). You have to give audiences something more than facts to care about.

If you don’t, you risk creating some version of this scene from the TV show The Simpsons: Lisa feels sad because one of her favorite teachers left. Her father, Homer, doesn’t get why. “I knew you wouldn’t understand,” she says. “Hey,” says Homer, “just because I don’t care doesn’t mean I don’t understand.”

Ultimately, with every piece of content, ask this: “Do we want people to care?”

If not, there’s no problem going with the cold corporate template and “let the facts speak for themselves.” If you do want people to care, you better give people more than content they can believe. You better give them content they can believe in – even if it means putting in more effort.

Creating belief is about understanding intent

So, how do you start creating content that goes beyond simple fact-based research, data, and information?

Go back to that argument you had on social media or with the colleague or boss who never seems to “get it.” Think about those customers you’re trying to convince to purchase from you or advocate for you.

You’re never going to win those battles with facts – you must understand why they are arguing, searching, or deciding. You must understand their intent.

To understand intent, you must first create mechanisms – content-driven experiences – that enable your brand to listen more effectively to the signals generated across their interactions.

Create #content-driven experiences to understand audience intent and listen to signals generated by the interactions, says @Robert_Rose via @CMIContent. Click To Tweet

As one might expect, this requires more effective use of data than is likely available for most businesses. A thorough content strategy is needed to provide data to help the business understand each piece of content’s type and purpose and how they apply contextually to each step of the customer’s journey.

What does that content strategy look like?

In my research and consulting practice, I’ve seen marketing organizations create a self-enablement process to create this level of capability. It typically involves a three-step process:

1. Arrange the data house

Create a dictionary or interpretation for understanding intent. Put simply, you need to discern the most appropriate response to the customer’s interaction with your content.

This is where a metadata structure and content tagging system to track behavioral context (or intent) come in. For example, a white paper called Discover How Digital Marketing Is a Good Thing for Your Business might be tagged with a “beginner” or “learning” intent. Someone who consumes this white paper would NOT be considered a lead but will be nurtured as an engaged audience.

2. Develop best next capability

Once you have an intent signal, you need to understand what’s the “best next” thing to make that customer understand and care about the answer.

Businesses need to create content-driven experiences to deliver a “best next” experience to content consumers. For example, that targeted messaging to the beginner or learning audience member should prompt them to want to read a how-to-change piece.

That’s overly simplistic, of course, but you can see how levels of nuance may need to be captured with more than just answers to a question. Through additional content consumption, a poll, or a survey, you can glean if this beginner is feeling confident or fearful about change. As you learn more about the nuanced aspects of the customer’s journey, you can automatically deliver the best next experience for that customer.

Similarly, it’s not all about technology and dynamic content. There’s a human element to this, too. You can share this information with others who can deliver additional experiences that fall outside the digital content realm. For example, you could share insights about the beginner prospect’s behavior with sales. Once sales understands what the prospect needs, their role can evolve from a persuader to a consultant helping the prospect understand the best way to move to the next step.

3. Connect the experiences

This step enables the most insight. Once you map your content to understand what you need to deliver based on intent, you must develop the capability to aggregate this data and serve up the content (and the intent) contextually across the different experiences. You need to find a way to connect the experiences into a singular view of the audience’s progression through their journey.

For example, if the beginner persona ultimately purchases your services, you might want to connect their profile to the onboarding or training module of a 101-level set of training classes. The insight gleaned from a more statistically relevant data set improves these activities or even makes them possible in the first place.

This third step may be the most difficult part of the process because it often means integrating multiple technologies to create a single view of the customer.

But you can start small. Even if you can just connect the intent upper/beginning part of the journey (awareness) to the mid part of the journey (sales), you are starting to get much better.

It’s the content, not the data, that makes people care

Data gives you the opportunity to make people care about what you have to say. To get beyond just “answers,” you must create compelling content that integrates those answers (facts, figures, data, information) into compelling experiences that appeal to the audience’s feelings.

One widespread marketing fallacy is that buyers want factual answers about the products and services they’re considering.

It’s not true. More often than not, the brand that supplies the least information, facts, data, etc., about a product and provides the most inspiration, belief, and emotional connection will be the chosen one.

You need to convince customers they are buying into a brand they can believe in. To do that, you need to give them an experience they believe in, too.

Want to learn how to balance, manage, and scale great content experiences across all your essential platforms and channels? Join us at ContentTECH Summit this March in San Diego. Browse the schedule or register today.

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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