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Why UX is critical to digital marketing

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In the world of digital marketing, there are many “Jacks-of-all-trades.” Part of the reason for this is that this industry is only approximately 25 years old — and in the beginning people in the industry had to cover all bases by themselves.

Over the last 10 years, as the industry evolved alongside the evolution of the internet, the web, and digital apps, there has been a growing and positive trend to do a way with the “Jacks-of-all-trades”(and masters of none) and engage specialists instead. One of the key skills that the digital marketing industry is sadly ignoring, howver, is that of the User Experience (UX) expert.

The absence of UX experts on digital marketing teams shows that those in charge of these teams still believe that everyone can be all things. Just as digital marketing teams tend to rely on the digital analytics team to help evaluate the success or failure of their marketing campaigns, or to help optimize the campaigns after they are launched, they should be engaging with a UX expert before the launch to ensure there is nothing in the experiential aspects of the campaign that will hold it back from its potential.

To delve into the signs that you need to engage an UX expert as part of the digital marketing process, we asked one of the world’s leading UX experts, Jared Spool from UX design school Center Centre – UIE to answer a few questions on UX and digital marketing.

What percentage of digital campaigns do you feel involve any level of UX thought and or testing?

I would have no idea. I would hope all of them do, but I’m sure they don’t. So, it’s definitely a number below 100%. Since I know some do, it’s above 0%. So, I’d say the likely range is between 1% and 99%. Beyond that, it would be hard to narrow it down.

What would be a sure sign in analytics reports that a digital campaign has failed from a UX perspective?

Unfortunately, you can’t tell from analytics whether a campaign has succeeded or failed from a UX perspective. To understand the UX, you need to really understand the experience of the users. (That sounds obvious, but you’d be surprised how many people don’t really know what their users’ experiences are.)

Let’s say you have a simple campaign that drives people to a landing page, with the intent of getting them to sign up for whatever the landing page is intended to sell. Now, a lot of folks will tell you that you could look at the conversions to see if there was a failure. Unfortunately, conversions only tell you half of a story.

Conversions tell you whether someone converts (signs up) or doesn’t. The analytics could tell you how many people visited the landing page and how many converted. Dividing the latter into the former would be your “conversion rate.”

However, this assumes that every visitor should convert. What about the people who legitimately shouldn’t? Maybe they didn’t understand what was offered by the campaign, yet when they landed on the page, they suddenly realize this isn’t the offer for them. Should they convert?

If they do, you might have a disgruntled customer on your hands. Or you’ve overinflated your number of people who signed up. This means there’s four possible combinations of people who come to your landing page through your campaign:

1) Those that should sign up and do. (Yay!)

2) Those that shouldn’t sign up and don’t. (Also, this should be a ‘yay!’)

3) Those that should sign up, but don’t (Hmmm.)

4) Those that shouldn’t sign up, yet do. (Uh-oh.)

If your efforts try to optimize for #1 (this is standard “conversion rate optimization”), you’ll end up ignoring the intentions of #2 and #3. When you optimize for conversion rate, success isn’t measured in terms of what makes your customers happy, but what’s good for your wallet. 

Unfortunately, there’s no analytics in the universe that can tell you about #3 or #4. The only way to learn about these is to do hard-core user research (which is a fancy technical term for “talking to your customers”).

What is the most common aspect of UX that digital marketers forget?

Simple: talking directly to their customers and prospects. Having conversations. Learning what they need and what they don’t.

People try to use digital marketing campaigns to replace having salespeople. And there’s lots of good reasons to do that. Yet the one advantage a salesperson has is they usually have to talk directly to customers and prospects. Those conversations are research into what the customer and prospect really need. And the salespeople are always learning.

Once you eliminate the salespeople from the equation, digital marketers often don’t replace that research with anything. The absence of research has a technical term for it: guessing. If you’re guessing what your customers and prospects want, you’re probably doing it wrong.

Have you seen any digital campaigns recently where you felt the designers did a good job from a UX perspective?

Sure. But you can’t isolate things to a campaign. When we’re talking about user experience, we’re talking about their total experience. 

Let me give you an example: Insurance companies try to get people to switch to their product from someone else’s. (This is because, in many places, people must have insurance. The market isn’t growing. The only way to grow your business is to steal someone else’s customer.)

If the marketers see their business as a commodity business, they see the biggest differentiator is price. Yet the biggest reason people switch insurers isn’t price. It’s the quality of the service they get during a claim. Someone has a bad claim experience (the company makes it really hard to get the claim settled satisfactorily), then they’ll switch instead of renewing. Where do they go? Someplace they believe offers better service.

Therefore, the UX of the insurance purchase has virtually nothing to do with the campaign that gets them to switch. It has to do with the quality of service. By the way, do you know what the No. 1 way people learn about better quality service? Not from ads, because every ad claims their company’s service is great.

It’s from their friends. Word-of-mouth advertising is the No. 1 influence on who people choose for their next insurer. What drives word-of-mouth? Not clever campaigns. No. It’s great service. So, the best thing UX people can do to help digital campaigns is to make sure the overall service is the best quality service.

If you could make digital marketers and designers implement a single aspect of UX into their campaigns, what would it be? How could they measure it to be sure they did it correctly?

It would be to deliver high quality service at every touchpoint. How would you measure that? By talking to customers and prospects to make sure you’ve delivered high-quality service everywhere.

Any other thoughts you’d like to share on UX and digital marketing?

My thought is that UX is digital marketing. A great user experience is the number one driver of every marketing metric. Investing in better UX is the best way to improve digital marketing. Not just of the marketing campaign experience, but of every aspect of the product or service.

When you invest in better UX, you improve the experiences people have. You improve the way they talk about you and your services. You make everything in your marketing efforts easier. 

It’s far easier to market a product or service everyone thinks is great than a product or service that nobody thinks is great. So much of the heavy lifting in marketing is because the company hasn’t made the investment in UX that they need to.

The main takeaways

To sum up the points and perspective Jarred Spool shared, UX should be and needs to be part of every digital marketing strategy. Failure to incorporate UX efforts into digital campaigns, typically results in poorer performing campaigns which could have been much better performers.

Unfortunately, there are no KPIs or simple analytics measurements to inform you that your campaigns would be helped from UX nor that integrating UX into your campaign development process will help your campaign performance. At best you could compare a current campaign with UX versus an earlier campaign without it.


Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.


About The Author

Alan K’necht an independent SEO, social and analytics consultant, a public speaker, award-winning author and a corporate trainer (SEO, social media marketing and digital analytics).

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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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