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The only two things that matter in marketing

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The only two things that matter in marketing

Is there a shortcut to generating revenue and results in marketing?

Marketing is complex and complicated. Many technologies, tools, and tactics promise to be an “easy button” to success. 

No matter what the latest trend is, experience proves that none of them can generate endless leads, consistently boost conversion rates, or predictably increase revenue.

But what if there was a “shortcut” — a straightforward, repeatable way to generate predictable results in marketing — that we have overlooked?

After advising and coaching dozens of marketing teams of billion-dollar brands, I believe such a shortcut exists.

If you want more revenue and results from your marketing, here’s the fastest way to success.

The two drivers of revenue and results

Marketing is a complex machine with many moving pieces and parts. This brings many challenges and a misunderstanding of what drives revenue and results.

Most people think of marketing simply as a creative endeavor. After all, the visual and written components that are the cornerstone of all marketing collateral are creative work.

However, marketing is more of a process than it is a creative effort. 

Does marketing require creativity? Absolutely.

But without the proper process, it becomes almost impossible to generate predictable results. 

Process creates predictability — and that’s especially true in marketing

To create predictability and maximize marketing revenue and results, you need two processes: experimentation and optimization.

  • Experimentation helps you find out what works (and, most importantly, what doesn’t).
  • Optimization lets you get the most out of your marketing (once you know what works).

These two processes go hand-in-hand and are fundamental to marketing success. Lacking one or both will cause your efforts to be stagnant and subpar. 

Unfortunately, most teams have no structure or systematic approach for either one.

Read next: Driving marketing at scale: People, processes, platforms and programs

Experimentation

Experimentation is about testing things to find out what works. It’s a powerful tool every marketer should leverage.

In marketing, no one really knows what will work. Not you and me — nor your team, vendors, agencies, partners, and influencers.

Even if you have an idea, it might not last long because the one constant thing in our industry is change.

Dramatic shifts in technology, competitive landscape, customer behavior, and even the culture happen every day – all of which are constantly upsetting the status quo. This volatility makes our work so exciting, yet also highly uncertain. 

When you don’t have insights, you’re throwing darts with a blindfold on and hoping to hit anything. With insights, you’re standing inches from the dart board and can effortlessly hit the bullseye every time. 

The best way to get insights? Experimentation.

With experimentation or testing, you apply the scientific method to uncover answers to specific questions. It can lead to get better results such as increased conversion rates, reduced cost, higher ROI, and more revenue.

That said, it’s important to realize that improved results are a byproduct of testing, not the main objectiv.

What’s essential is that it allows you to generate insights to improve all facets of your marketing through optimization.


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Optimization

Optimization is the process of making continual, incremental improvements to deliver a better result with the same or fewer resources. 

Want more revenue and results from your existing budget? Focus on optimization.

Just like with experimentation, you’d be crazy not to optimize your marketing. And yet, countless teams are too preoccupied with producing more content and launching more campaigns to be bothered with optimizing what they’re doing.

The benefits of optimization are apparent (who doesn’t want more from less), but it’s not as easy to do as it may sound. There are many moving pieces, and it requires building an optimization program to coordinate the efforts and the team to succeed.

First, optimization requires insights. You have to know there’s an opportunity to improve. Otherwise, you’re wasting your time. 

Benchmarks are the starting point of most teams but realize that almost everything your team is doing can be improved. 

Focus your optimization efforts on the areas that:

  • Will have the highest impact (e.g., where you spend the most money).
  • Are attainable (e.g., your team can actually execute).

Other times, knowing what to optimize isn’t quite as clear, like when a new landing page isn’t driving as many conversions as other, similar landing pages. What optimization should you make then?

This is why optimization and experimentation are inextricably linked. Use experimentation to develop and test your ideas to find what works and what doesn’t, then apply optimization to make the improvements across all of your marketing initiatives.

Once you know there’s an opportunity to improve, you have to make the changes.

Sometimes this is easy and simple, like turning off the underperforming ads. That’s fairly obvious, yet marketing teams routinely overlook such simple optimizations. 

Additionally, consider applying optimizations globally. If you developed new messaging for your email marketing that produced an increased response, don’t stop there. Apply that messaging to your social content, landing pages, and paid ads. 

Finding opportunities to leverage optimizations across your marketing is key to maximizing your results and the impact of your efforts.

Optimization is more involved than experimentation because it engages most of your marketing team. It requires coordination, collaboration, and communication to ensure that the necessary changes are made timely and their impact is measured correctly.

If you’re not optimizing your marketing, you’re wasting your budget and missing out on opportunities. Work smarter, not harder and invest in optimization.

Conclusion

How many experiments are you running every month? How much time and resources do you spend on optimizing your marketing versus creating and launching new efforts?

Both experimentation and optimization are essential. Each requires structure, diligence, and effort to execute consistently and effectively. Most importantly, they must be ongoing if you want to maximize the impact of your marketing efforts.

The most successful marketing teams invest in developing experimentation and optimization processes.

Because the more you experiment and optimize, the more revenue and results your marketing will produce.


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


About The Author

Tim Parkin is a consultant, advisor, and coach to marketing executives globally. He specializes in helping marketing teams optimize performance, accelerate growth, and maximize their results.
By applying more than 20 years of experience merging behavioral psychology and technology, Tim has unlocked rapid and dramatic growth for global brands and award-winning agencies alike.
He is a speaker, author, and thought leader who has been featured in AdAge, AdWeek, Inc, TechCrunch, Forbes, and many other major industry publications. Tim is also a member of the American Marketing Association, Society for the Advancement of Consulting, and an inductee to the Million Dollar Consulting Hall of Fame.

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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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