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The end of marketing or a new beginning? The truth about AI

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The end of marketing or a new beginning? The truth about AI

Love it or hate it, the artificial intelligence revolution is here. People can’t stop talking about ChatGPT, OpenAI and how AI will fundamentally change the world. Marketers everywhere are obsessing over the newly discovered power of AI.

Amid the jaw-dropping realizations of what AI is capable of, marketers are faced with an existential question that’s a bit daunting to consider: Is this the end of marketing as we know it? After all, you can ask ChatGPT almost any question and get an exact answer.

Need to know how to set up form conversion tracking in Google Tag Manager? Just ask ChatGPT and it will give you step-by-step instructions.

Want to build a keyword strategy, craft a job description for a new hire, or write the copy for a new landing page? The AI will do it for you immediately and effortlessly.

It’s no surprise that marketers everywhere are concerned that AI will render many aspects of marketing obsolete, but before you start worrying, let’s remember that this has happened before:

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  • Direct mail was disrupted by email.
  • The phone book was replaced by online search.
  • Newspapers and magazines were largely replaced by social media.
  • Telemarketing was replaced by SMS marketing.

Artificial intelligence is the next technological innovation that will fundamentally shift marketing approaches. Here’s what you need to know to stay up-to-date and prepare for the new world of marketing.

What’s changed (and what hasn’t)

The fast-paced evolution of artificial intelligence has already changed many things about marketing, but there are still several pillars that will stand the test of time.

Here’s a quick preview to give you a perspective on how to think about the future of marketing in a world dominated by AI.

Search engines

Searching on Google for information or answers will soon seem as useful as flipping through the yellow pages to find a reliable business. People will ask AI questions and get specific, contextualized and detailed answers.

You can even ask the AI to clarify an answer or provide more options. It’s time to rethink your SEO and content strategy.

Content production

Creating content is already much easier and faster, thanks to artificial intelligence. As if we needed more content, AI will result in an explosion of content like we’ve never seen before. This isn’t speculative — it’s already happening. 

There are now tools capable of creating complete articles, slide decks, talking head videos and even reproducing anyone’s voice. These tools are fast, affordable and extremely accurate. Plus, they’re only going to get better.

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Dig deeper: 5 AI writing assistants in action

Data analysis

Consuming lots of inputs and information and making sense of it is complicated, complex and time-consuming. Not anymore.

AI makes data analysis, insights generation and even predictive analytics easier. Your data and reporting will evolve to a significantly new level of sophistication without too much effort or cost.

Consumer behavior

Fortunately, people are still the same. We are still emotional, irrational and human. That means that the marketing fundamentals will still work the same, only the mechanism has changed.

Consumers will still look to each other for guidance, approval and recommendations. Creating conversations, connections and community is still a smart and reliable approach. 

Thought leadership

No artificial intelligence is sentient (capable of conscious experience) — yet — so most of what we’ll see in the near term is the repackaging, repurposing and regurgitation of existing thoughts, ideas and content.

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Thought leadership is still paramount in the form of original and innovative ideas. Every marketer should continue to pursue creating and distributing thought leadership to stand out in the sea of sameness.

Brand

Nothing beats having a powerful brand. Building a recognizable, trustworthy and desirable brand is still challenging, time-consuming and expensive.

The power of having a strong brand will only increase. Savvy marketers should continue their investments in brand building.

Dig deeper: Building a brand strategy: Essentials for long-term success

The pros and cons of AI in marketing

Artificial intelligence isn’t perfect. Although it will make marketing easier, it also brings some significant concerns and considerations. 

First, here are some of the major wins:

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Improved efficiency

Marketers are constantly overwhelmed with many tasks that AI can easily take over. Using AI will enable small teams (or individuals) to scale their efforts and be more efficient, which will have an enormous impact on the results that can be achieved.

Data-driven insights

Consuming, processing and summarizing large data sets is one of AI’s biggest strengths. Marketers can use this to mine insights from multiple data sources to inform and optimize our marketing efforts.

Google Analytics already provides “Analytics Intelligence,” which uses machine learning to answer questions about your data and build customized reports. 

Personalization

Delivering truly customized experiences is no longer a pipe dream. Email platforms can already use machine learning to send messages to customers at the exact right time, based on their historical behavior of when they’ve opened emails in the past.

As the capabilities evolve, AI will be able to create brand-new, unique and personalized experiences, content and conversations with users.

Cost savings

The time and cost savings of using AI tools will be one of the biggest impacts on every marketing organization. Marketing budgets are notoriously thin. The ability to reduce expenses and achieve greater scale will be transformative for marketers. AI also brings a new level of automation that will deliver immense time and cost savings.

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Dig deeper: How AI can help your marketing right now

However, there are some significant concerns with the adoption of AI:

Quality and accuracy

Since artificial intelligence cannot think independently, there are massive concerns about the quality, accuracy and integrity of its output.

How can you trust what the AI says? What source is it relying on? We must be vigilant about ensuring that anything the AI produces can be verified.

Job displacement

Unsurprisingly, artificial intelligence will replace and displace jobs, with some marketers being more affected than others.

As with any new technology, there will be a shift into different types of work required to leverage AI to support the marketing organization. Companies are already hiring for “AI specialist” roles to understand and capitalize on what’s possible.

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Privacy and ethical concerns

Since AI relies on consuming lots of data and information, how can we protect user privacy? In addition, how can we ensure that artificial intelligence is not biased or discriminatory?

There are major concerns around privacy and ethics that must be addressed before fully adopting AI.

Dig deeper: 4 areas of martech with ethical concerns

Will AI replace marketers?

AI will dramatically change how marketing is done. It will make it easier, faster, cheaper and better. Those benefits come at a price: replacing the need for many specific tools and shifting certain roles.

There is no question that some marketing roles will be made redundant by artificial intelligence, especially on small teams with limited resources.

However, all artificial intelligence requires a creative brain for input and guidance just as much as it needs critical thinking and proper review to maintain the quality and integrity of what it produces.

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Every successful marketing team will embrace the use of AI throughout their tech stack and their processes in order to maximize their efficiency, creativity and productivity. In doing so, it will usher in a new generation of marketers who understand how to mold and shape AI to produce better marketing assets at record speed.

The ultimate question

There’s one important question that every marketing leader should be thinking about and asking: Is your marketing team using artificial intelligence?

If you don’t know the answer, you better find out. The benefits of using AI in marketing are massive: getting more done faster and cheaper. However, the risks must be understood and controlled. 

Is your marketing team regurgitating your competitor’s content — or are you creating authentic thought leadership and building a powerful brand?

Artificial intelligence is just another tool. It’s your job to help your team understand how and where to use it to create powerful marketing.

This isn’t the end of an era, but the exciting merger of human creativity and cutting-edge technology will revolutionize how we connect with our customers.

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

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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.”

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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.”

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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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