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Are You Overselling the Power of Data? [Rose-Colored Glasses]

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My old boss, the CEO of a former employer, was one of the best salespeople I’ve ever known.

He could work a room, listening and knowing just the right thing to say to keep interest piqued and drive value in the conversation. Just as importantly, he knew exactly (and I mean exactly) when to end the meeting and walk out the door. It’s just like show business, “leave them wanting more.”

Anyone who has experienced a bad salesperson has seen the opposite – the classic moment when the rep doesn’t know how to take “yes” for an answer. The customer has usually implied or even overtly said they are interested in the next step, and the rep continues to oversell features, benefits, discounts, and value-added services – all of which are unnecessary.

Two consequences happen when we oversell something. First, we talk the customer out of their decision to purchase. I once witnessed a rep continue to talk and talk and talk after the customer had expressed interest in purchasing. The rep mentioned something about the future development of the product, and it made the customer suddenly question whether that roadmap matched their needs. It killed the sale.

The second effect is almost as bad. The rep wants so badly to ensure there are “no surprises” that they oversell by continuing to offer more and more benefits until the customer finally says, “Stop.” By then, the sales rep has often set such unrealistic expectations that they’re set up to fail.

That’s the situation for marketing and content practitioners selling the use of data to business leadership.

#Content marketers often set unrealistic expectations about the value of data, setting up their programs for failure, says @Robert_Rose via @CMIContent. Click To Tweet

Data driven to the wrong destination

“We’re data-driven!” If I had a dollar for every time I heard that when I ask about the measurement strategy to a larger marketing, brand, or demand generation team, I’d be on a beach somewhere sipping a fancy tequila.

Most of the time, once we dive into what’s behind that statement, we find “data-driven” quite literally means the team is driven by data. They have no insight into how (or if) the data is helping.

They are so awash in metrics, analytics, and numbers that they search and find some data that drives every move that they make. Everything they do is driven by data. Every action is supported in retrospect by finding the data.

What these “data-driven” marketers fail to realize is that by doing this, they also build a wall that prevents attempting anything new.

Whenever purely “data-driven” is the starting place, I know what the next challenge will be when someone wants to innovate and do something new. To do that, a “business case” must be made. Someone – usually the person responsible for making the business case – will inevitably ask, “Well, what does the data say?”

But data doesn’t (and can’t) say anything definitively if the idea is truly innovative. What happens? The business-case maker looks at the data they’ve used to justify all previous decisions. When they can’t find helpful data, they look at external best practices to see if the innovative thing matches up to what other people are doing.

Data doesn’t and can’t say anything definitively if the idea is truly innovative, says @Robert_Rose via @CMIContent. Click To Tweet

Of course, if many best practices that will point to this innovative thing exist, is the thing really all that innovative?

Hmmmm …

Do what the data said, not what I said

For the last 10 years, content and marketing practitioners have been sold the magic of data – a way to increase the efficiency and performance of digital experiences. In turn, many marketing teams desperate to show proof-of-life of anything they do with content oversold the power of data. It now hamstrings them from doing anything that deviates from being incrementally above or below average.

I recently worked with a B2B technology company that wanted to launch a new digital thought leadership magazine. For them, this was an innovative new approach to delivering education to decision-makers in their industry. They spent time developing a solid set of “big ideas.” They decided on a content strategy of cutting-edge ideas rather than pragmatic how-tos. They planned to position their subject matter experts as people who could pull customers into the future. The team was excited.

The vice president spearheading this initiative made the rounds to get buy-in from the product, brand, public relations, and C-suite teams.

It didn’t go very well.

In each conversation, the vice president got a lot of resistance with questions about what the data said. In an ironic twist, the data referenced by these other teams was what the marketing team had used to demonstrate the success of previous campaigns. The vice president heard:

  • “This sounds like it runs counter to what our SEO data says.”
  • “Data says that the end buyer isn’t senior leadership – shouldn’t we be solely targeting the buyer?”
  • “Where is the data that shows that senior leaders need this information?”
  • “What is your forecast for the number of leads we will get from this?”
  • “Do we have data on whether these topics are popular?”

In the end, the magazine project was put on hold.

The lesson isn’t that the company didn’t get to launch a new digital magazine. The lesson is why they didn’t get to launch it.

The team had oversold their use of data to justify every single thing that they did. They had established that they were “data-driven.” Their colleagues simply responded based on what they had been sold: “Why did the data drive you to this conclusion?”


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Data should ride shotgun, not drive

Measuring content and experience is difficult. It always has been and always will be. As I’ve written, our objectives matter more than the accuracy of the data. Ask what is the most important insight to get – that the blog post or white paper was found, it was read, or it changed a behavior? Often, we want insight from the latter, but we use data and make decisions based on the former.

One of my favorite books about data and measurement is The Haystack Syndrome: Sifting Information Out of the Data Ocean by Eliyahu Goldratt. I always reflect on this quote:

Tell me how you will measure me, and I will tell you how I will behave. If you measure me in an illogical way, don’t complain about illogical behavior.

In our selling of data’s capabilities, we must acknowledge occasions will arise when we’ll need to go against the data or proceed without it. Otherwise, we’ll be data-driven to mediocrity.

Data informs the answer to questions. We should drive the car. Data should ride shotgun.

Content marketers should drive the car. Data should ride shotgun, says @Robert_Rose via @CMIContent. Click To Tweet

To have the flexibility to try innovative things, we must reframe how we sell data as a value to our content and marketing strategy. These two ideas can help:

  • Stop treating data as proof of life: You should cease using and selling the value of data to justify decisions already made. Data-driven value determined retrospectively, as in “Did this campaign work?” is helpful. But if you let data drive your entire strategy, you will put future content marketing ideas into a box – every decision becomes about “beating” the last decision. You’ll never try anything that isn’t trying to “fix” the last decision.
  • Content and marketing strategy is not Jeopardy: Get beyond scanning mountains of data to come up with an answer in the form of a question, which shapes your strategy. First, form a purpose, an objective to reach, and then assemble a list of key business questions to help form a plan to reach that objective.Remember, in business, it’s much better to know what you don’t know than to not know what you don’t know. When faced with the latter, the tendency is to dive into the data and find an answer that matches a question you could have.

If you start with an objective, develop the key questions to meet it. Then design what data is needed to answer those key questions. Only then are you using data to inform a decision, not to justify one. Indeed, a key question might be, “Should we do this?” But then, if it’s a new thing, you can acknowledge that answer may not be known before the project begins.

Learning to succeed

Sometimes it’s better to learn than succeed.

Here is an experiment that you can run with your teams. On your next Zoom call (or in your office as the new normal may be), ask everyone three questions. The first is “Should companies like ours be innovative?” I’d bet a fancy cocktail that 90% will nod their heads.

Then, immediately ask the next question: “Is our company (or team) innovative?” This query will almost assuredly result in questions: “Do you mean, like, ever?” or “ Do you mean, now? Are we innovative now?”

Clarify as necessary: “Yes. Ever. Have we ever been innovative?”

Depending on the type, age, and size of your company, your mileage will vary. But for those yes responses, I would bet another fancy cocktail on the answer to the third and final question: “When was that?”

With, I dare say, with few exceptions, everyone will cite something that ended up successful.

You see. Everybody LOVES and remembers innovation, just so long as it worked.

In a business only driven by data, nobody wants to be the dope who said yes to the new strategy that had no data to support the decision and failed.

In a “data-driven” business, you can become incapacitated by the feeling that data should always be the driving force. You’re unable or unwilling to embark on any activity that you can’t ensure will nudge your stats in the right direction.

If you reframe the use of data and measurement, get agreement on the objective, then ask better questions to enable you and your team to make more things that might succeed spectacularly or fail with a thud. As Nobel Prize-winning physicist Niels Bohr once said, “An expert is someone who has made all the mistakes which can be made in a very narrow field.”

So, let’s go use data to empower the decisions that free us up to make some of the best mistakes.

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

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

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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