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Why we care about AI in marketing

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Why we care about AI in marketing

To engage customers in a personal way, at a very large scale, AI or machine learning is essential. Chatbots and intelligent assistants are already leading client interactions, and AI-generated content is around the corner. AI also enables the analysis and interpretation of data at a speed and volume beyond human capabilities. Algorithms are continuing to improve as well, accelerating optimization in near real-time. As AI improves, use-cases are only going to increase.

Artificial intelligence (AI) in marketing leverages machine learning to make automated decisions. With AI, brands can boost the ROI of marketing campaigns through predictive modeling, advanced segmentation, and personalization.

In this piece, we will dive deep into the value of AI technologies in marketing. We’ll cover:

Estimated reading time: 8 minutes

Why AI marketing matters

AI has transformed digital marketing and reduced the risk of human error while streamlining marketing campaigns. To truly connect with customers, you’ll still need the human touch — especially concerning compassion, empathy, and storytelling. However, when it comes to certain aspects of marketing, like predictive analytics and digital advertising, AI is capable of incredible things. For example, when your goal is to understand your customers’ needs and match them to appropriate products or services, this is where AI shines.

A 2020 McKinsey survey found that those working in marketing and sales had the third-highest rate of AI adoption. What’s more, the survey reported that “A small contingent of respondents coming from a variety of industries attribute 20 percent or more of their organizations’ earnings before interest and taxes (EBIT) to AI.”

Since then, AI use in marketing has continued to grow. As rates of use increase, if you don’t consider implementing these technologies, your brand risks being left behind. Your competitors will adopt AI strategies to improve their business outcomes, allowing them to increase sales, boost customer retention, and more effectively launch new products.


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Benefits of artificial intelligence in B2B marketing

There are plenty of artificial intelligence marketing tools designed specifically for business sales, allowing B2B vendors to leverage the power of personalization, machine learning, and more. From AI-powered workflows to automated next best action features, AI is helping companies solve challenges specific to B2B.

Here are some of the benefits of artificial intelligence in B2B marketing.

Campaign ROI. With the help of AI technology, B2B marketers can better predict the performance of data and campaigns. They can then make suggestions for optimizing those campaigns to reach the maximum ROI. When leveraged effectively, marketers can use AI to transform their marketing campaigns, extracting the most valuable insights to act on them in real-time — for example, analyzing the most effective ad placements to increase engagement.

Better informed decisions. Data integration automation replaces manual processing to enable faster, real-time decision-making. The goal is to gain promptly actionable customer insights. For example, with the help of predictive analytics, you can access buying patterns that help forecast purchasing decisions. Since these buying patterns are often more complex to spot in a B2B model than in a B2C model, the help of AI can be a game-changer.

Marketing metrics. The ability to track the effectiveness of campaigns can significantly affect your marketing ROI. Artificial intelligence can help monitor the outcomes of countless customer touchpoints, thus supporting campaign optimization.

Better data management. AI marketing tools also help to significantly reduce the risk of improper data interpretation, support optimal data integration, and eliminate data silos. An AI marketing tool is software that leverages AI technology to automate decisions based on collected data.

Components of artificial intelligence

Artificial intelligence encompasses various features and capabilities that can scale your marketing programs. Here are some of the main components.

Machine learning. Unlike task automation, which is fairly limited, machine learning relies on algorithms trained to use large quantities of data to make complex predictions and decisions. Machine learning models can decipher text, recognize images, perform audience segmentation, and even anticipate how customers are likely to respond to various initiatives. More importantly, as the name implies, it can correct and improve itself without supervision (although some human supervision is often involved).

AI-powered platforms. Many marketing platforms today have AI “baked in,” using it to analyze and drive insights across large pools of data. Most of the large marketing suites have AI components providing cross-platform support for the solutions the suites offer. Examples are Salesforce’s Einstein and Adobe’s Sensei.

Platforms to perform specific kinds of work are also available, for example, AI-powered content creation platforms. Some solutions offer draft content for emails or specific social media platforms, such as Twitter. Other solutions optimize content for SEO.

Big data and analytics. Collecting data is one thing — effectively analyzing it is another. AI presents one solution to the challenge of analyzing and interpreting data at a scale beyond the reach of human beings. Using AI in B2B marketing can efficiently analyze immense amounts of data, link that data, recognize patterns, and drive predictions. The more data AI is given, the better it can learn and improve., but there is still a lack of emotional intelligence. For this reason, as well as the need for human supervision (e.g., data stewardship, the maintenance of data catalogs, etc.), people continue to play a vital role in data management and analytics.

Examples of artificial intelligence in marketing

Many companies across all industries are implementing unique AI marketing strategies. Here are some common examples.

AI chatbots. STCHealth uses intelligent chatbots to answer vaccine-related questions and help people access their immunization records. Botco.ai, an automated, AI-powered conversational marketing platform, made the seemingly impossible possible. Thanks to this automated platform, the company saved approximately 52,000 human hours across two million chatbot interactions.

Visual intelligence. The eBike manufacturer Serial 1 used Vizit, a visual intelligence platform that uses machine learning and AI to help the company determine what images resonate most with consumers. As a result, Serial 1 was about to optimize digital assets and boost website conversions by 98%. This use case is a prime example of how AI marketing leads to better decision-making.

AI platforms for SMBs. Mailchimp uses a tool called Smart Platform, bringing AI to the SMB market. Some of the latest tools allow SMBs to automate content creation, gain access to dynamic recommendations and leverage a new customer journey builder.

Frequently asked questions about AI marketing

While AI technologies have been around for decades, many brands still wonder how to apply them to their marketing processes. Here are some of the most common questions people ask.

How does AI differ from automation?

AI is software that is designed to mimic human thought processes and solve problems. Automation, on the other hand, refers to technologies that follow programmed rules to accomplish tasks at scale with little to no human intervention.

Brands use both of these tools to improve their marketing processes. AI, with its machine learning capabilities, can help marketers optimize demand generation and increase personalization by analyzing customer behaviors. Automation is designed to help these teams accomplish more repetitive tasks at scale.

Do marketers need AI?

In our digital world, marketers need better ways to understand audiences across different devices and channels. And while brands can still engage with their customers without AI, it’s going to become increasingly harder to scale these efforts as technologies evolve. AI is essential if brands are to engage with prospects and customers as individuals, but do so at scale.

What are some AI marketing challenges?

As mentioned, not every member of the marketing team needs to have an extensive understanding of AI to enjoy its benefits. However, if brands don’t have at least one person working with them who has this experience, it can prove difficult to incorporate it into your systems. 

Brands also may not have the tools to handle the data and resource requirements of AI technologies. That’s why so many marketers are turning to marketing work management platforms and other helpful technologies to more effectively manage these demands.


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How artificial intelligence is changing the marketing landscape

Companies can accomplish much by implementing artificial intelligence in marketing campaigns, strategies, and tools.

Whether your goal is to study consumer behavior on your website or develop tailored advertising campaigns, AI can play a pivotal role. Many digital marketing strategies can benefit significantly from these technologies, ranging from social media to SEO.

Some of the ways that AI continues to change the marketing landscape include:

  • Customer acquisition and the need for personalized marketing: The digital world is fueled by data now more than ever, especially as the number of people online continues to abound with growth.
  • Customer service through chatbots: Customers want their needs to be taken care of immediately, and the use of AI to power chatbots has enabled some businesses to handle customer inquiries more effectively while reducing the role of human agents.
  • Advertising campaigns: Campaigns can now be more precisely optimized and targeted.

As AI continues to evolve, there are disruptions in day-to-day marketing operations. However, it’s likely that AI technology will continue to enable sophisticated marketing strategies for years to come.

Learn more about AI marketing

Want to learn more about implementing successful machine learning and AI? We recommend the following resources:

Dive deeper into the latest marketing technology, tactics, and strategies, including those based on artificial intelligence with news from MarTech.


About The Author

Why we care about AI in marketing
Danni White is a writer covering B2B and martech industry news and trends.


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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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