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3 ways B2B marketers can use generative AI

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3 ways B2B marketers can use generative AI

As technology and automation evolve, B2B marketers can access tools and information faster than ever. With the rapid adoption of generative AI, that evolution is happening in real time. As B2B marketers, we must embrace and use this technology to our advantage. 

This article will cover three ways to use generative AI: keyword research, content creation and data analysis. Doing so will completely change your approach to how you market products and services across the digital ecosystem, leaving competitors who are not up to speed in the dust.

Unleashing the power of generative AI in keyword research

Traditional keyword research includes many methods, but they all have one thing in common: It’s a manual process. Some paid tools, free tools and plug-ins can help marketers analyze keywords, but this takes time and effort. It can also be costly when outsourcing this work to an agency. Even so, keyword research is an integral part of marketing. It should never be skipped or overlooked.

Some of the tools marketers use for keyword research include Google Keyword Planner, Google Search Console, Semrush and Surfer SEO which integrate AI into the platform. Browser plugins like MozBar and Keyword Research have also come a long way and continue to add value to B2B marketers.

Up to 44.5% of marketers use generative AI for keyword research. Platforms like ChatGPT can help marketers be more efficient with keyword research. SEO automation speeds up the process and makes it easier to find keywords, but humans are still required to ensure that generated keywords are relevant, make sense and fit the context. While AI outputs are improving daily, smart prompt engineering is now becoming a critical skill marketers need to learn to achieve better results.

Using generative AI for keyword research has many benefits, such as improving efficiency and accuracy and finding keywords that have yet to be used before. They speed up research and give users a competitive edge by letting them respond quickly to changes in search behavior.

These models also develop more specific and valuable keywords, ensuring marketing efforts reach the right people. Generative AI models can find low-volume or long-tail keywords that make it easier to rank content. 

Even though generative AI models for keyword research have plenty of potential, a few challenges should be addressed. For example, if you rely too much on AI, you might optimize content with keywords that could be taken out of context. The accidental spread of biases in the AI data could lead to keywords that could harm your brand reputation.

The biggest challenge with generative AI is that it lacks cultural context. Global multinational companies with markets everywhere could have an issue with using AI to optimize for local languages and ensure that all the content aligns culturally, considering slang and other local issues.

To overcome these challenges, finding a balance between AI-generated results and human oversight is essential.

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

Integrating generative AI models into content development

The significance of content in digital marketing cannot be overstated. It enables B2B and technology companies to engage with target audiences, elevate brand recognition and establish an integrated marketing program deployed across all channels.

High-quality and relevant content that delivers value results in customer trust and loyalty. Companies must always prioritize content to thrive in the highly competitive digital landscape.

Like keyword research, content creation is a labor-intensive process. Marketers frequently invest considerable effort into writing long-form content like blogs, white papers, ebooks and reports. They also write short-form content for social media, headlines and other ad copy.

It’s also common for marketers to outsource content production to agencies, freelancers or copywriting platforms like Compose.ly. This increases expenses and complicates communication. Consequently, traditional content generation methods consume substantial time and resources.

ChatGPT and similar platforms offer marketers unprecedented opportunities to enhance all content creation and production. These models can generate content that appears to be handcrafted, ensuring consistency in the brand’s voice and simplifying the creation of diverse, engaging and contextually relevant content. 

However, marketers must always balance AI with an added layer of human supervision when employing generative AI in content development. While these models can expedite content production, human context remains necessary to ensure coherence, accuracy and cultural relevance. By incorporating feedback loops and refining procedures, marketers can achieve an equilibrium between AI-generated content and human expertise, ultimately enhancing content quality and efficacy.

The advantages of generative AI for content production include accelerated processes, increased precision and the capacity to generate substantial volumes of content. These models can rapidly create high-quality material, allowing marketers to respond to market fluctuations and seize real-time engagement opportunities.

Additionally, generative AI can generate accurate and relevant content tailored to specific audiences, ensuring the success of digital marketing campaigns. Producing high volumes of content allows marketers to think more strategically instead of writing a blog post.

Despite the transformative potential of generative AI, specific challenges exist. For instance, current AI technology can not fully grasp the cultural or business context, which could result in superficial or nonsensical content.

Ownership and copyright concerns may emerge as AI-generated content obscures the distinction between human and machine authorship. Transparency is vital in AI-generated content to preserve audience trust and mitigate misinformation.

Businesses must proceed cautiously when incorporating generative AI in content creation, ensuring that human oversight and transparency remain indispensable components.

Dig deeper: 5 AI writing assistants in action

Using generative AI in data analysis

Generative AI models bring in a new era of advanced data visualization. These methods enable real-time data tracking and dashboard creation, complex network visualization and various data display options. As a result, organizations may obtain the most up-to-date information, make informed decisions and quickly adjust to market shifts by leveraging real-time monitoring.

Detailed network visualization reveals the complicated connections between data points, providing crucial insights into the interactions between different data points. This multidimensional data representation allows businesses to understand each component of their marketing campaign performance.

AI models can likewise help marketers extract actionable insights from data. With the right prompts, AI outputs can find anomalies and outliers, assess feelings and emotions, segment markets and develop buyer personas. 

Anomaly detection identifies unusual variances that may indicate possible problems or possibilities. This is extremely helpful when managing large paid media campaigns across paid search and display ads. 

When analyzing large conversational data sets, AI outputs can find the emotional impact of the content through sentiment analysis and emotion recognition. Market segmentation and consumer profiling help organizations focus their marketing efforts by allowing them to modify their strategy accordingly.

Generative AI models can also improve predictive analytics. For example, time series forecasting uses historical data to predict future trends and events. Machine learning algorithms are critical in generating data-driven predictive models. Generative AI models lead to more accurate forecasts by developing these methodologies, which can help predict campaign performance.

Text analytics has also advanced significantly. Topic modeling and document clustering, network analysis, named entity recognition and relationship extraction, text summarization and content production are all tasks that use these models. 

Topic modeling identifies fundamental topics in large data sets like social media mentions, call center transcripts or media coverage. It can help find patterns of hidden context and narratives.

Network analysis reveals the connections between diverse communities, named entity identification and relationship extraction, on the other hand, reveal connections between separate entities. These text analyses can help marketers identify higher-authority influencers and content creators.

Generative AI is also making social media analysis more efficient. Social network analysis and community detection reveal the links between people in online communities, revealing user behavior and interests. 

Trend analysis and hashtag monitoring measure the popularity of specific subjects and discussions, allowing marketers to keep up with industry developments and trending topics. Influencer identification and interaction make finding notable industry individuals and future collaboration opportunities easier.

Making the most of generative AI in your B2B marketing efforts

As the digital marketing landscape changes, B2B marketers must use cutting-edge technologies to stay ahead of the curve. The good news is several generative AI statistics show marketers are starting to adopt this new technology, and for a good reason.

Generative AI can potentially change keyword research, content creation and data analysis in ways that have never been seen before. This will usher in a new era of data-driven and integrated marketing strategies. Even though there are still challenges and limits, generative AI models can lead to incredible results when used wisely and with human expertise and oversight.


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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.”

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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