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How to build a basic genAI strategy for your marketing organization

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How to build a basic genAI strategy for your marketing organization

A few days ago, I joined a virtual holiday happy hour event with my best friend from college, Will, and our close mutual friend, Erin. Erin is a seasoned PR veteran and communications professor and I often invite her into my University of Oregon classes as a guest lecturer. As we were toasting the holidays, the conversation moved toward AI and the impact it would have on the new year. 

My friends shared that I was one of the only professionals in their network that had a positive view of AI and had embraced using it at work. I was pleasantly surprised to hear that I was offering up a unique perspective and happily shared the many ways I had been using generative AI for marketing. Erin then asked me a great question, which was:How do you get started bringing such a disruptive tool into an organization?”

So — Erin and Will, my loves — this article is for you and for anyone looking to be a champion for AI at work. This article will help you:

  • Better understand how AI can help you and your team.
  • Build a basic AI strategy.
  • Bring generative AI into your organization.

However, this article is not:

  • A risk assessment analysis.
  • Legal advice on how to navigate risk.
  • Governance strategies or guardrails.
  • A list of the best AI tools (they will change over time).

There are some great resources available for those important topics, including the National Institute of Standards and Technology AI risk management framework, a great starting point if your role is responsible for AI governance. 

Building a basic AI strategy

As with any project I take on, whether personal or professional, I always start with strategy. The most basic building blocks of an effective strategy are an overall objective; a target audience and audience profiles; budget; and KPI’s to help you measure the outcome of your strategic planning. So, let’s start with these three basic questions:

  1. Identify the opportunity and objective: Which marketing tasks could benefit from the help of AI tools? 
  2. Identify your target user base: Which roles within my organization complete these tasks?
  3. Define KPI’s: How will we measure the success of our AI tools to positively impact the completion of these tasks?
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Identify the opportunity

This first step is to identify the areas and specific marketing tasks that would benefit from generative AI. This includes assessing content creation, data analysis, customer engagement and campaign optimization tasks. I recommend a good old-fashioned brainstorming session, where you list all tasks that are currently part of your team’s workflow. You may want to review the previous posts in my Decoding AI for Marketing series, as they are chock full of creative marketing applications for generative AI tools. 

For additional inspiration, I have crafted a blog post and infographic that lists out seven marketing categories or pillars and the specific marketing tasks associated with each one that can benefit from AI. 

The pillars include:  

  • Content creation and management.
  • Data analysis and insights.
  • Customer engagement and experience.
  • Campaign optimization and management.
  • Product marketing and strategy.
  • Digital marketing and ecommerce.
  • Event marketing and PR.
7 Pillars Of Ai Inmarketing Scaled7 Pillars Of Ai Inmarketing Scaled

Target users

Once you have identified the specific tasks and objectives of your AI strategy, you can then work to identify the roles within your organization that will need access to AI tools. Depending on the size of your company, you may have hundreds or even thousands of individuals within the marketing department that manage very specific tactics. As one of my Intel colleagues loved to say, you do not want to “boil the ocean.” So a small pilot group that is focused on one specific area would be a good place to start. 

If you work for a startup or SMB, your marketing team is likely to be lean and nimble, with only a handful of roles dedicated across many marketing channels. Identifying key contributors responsible for the tasks you identified in step 1, who are eager and willing to evaluate these new tools, will help ensure your plan is successful. 

You might also want to conduct a skill gap analysis, assessing the current skill levels of the team regarding AI technology and identifying areas where training or hiring may be necessary.

KPIs for your AI toolset

The next step in this basic strategic planning process is to determine how you will measure the outcome and performance of your AI toolset. These metrics will be specific to the tasks you identified and could help measure increased productivity, creativity, quality of work, customer engagement and many other potential outcomes.

The most important ingredient of this step is for all stakeholders to align on which measurements will determine the effectiveness of AI-powered outcomes. 

Budget and human resources

Determining the budget, time, and human resources required for your AI implementation is an essential part of the planning process. This includes software costs, training expenses and potential hiring. I recommend starting with a small pilot, proving the ROI of a small initial investment, and then scaling more broadly across the organization. 

To help you organize the output of this basic strategy, I have crafted an AI Strategy Worksheet or template, that you can use as a collaboration tool when brainstorming with your team. I recommend loading this up in Miro, an interactive whiteboard tool, so that remote teams can ideate together and easily save everyone’s input. 

A strategy worksheet with blue grid

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Next steps

Constructing this basic AI strategy will set you and your team up for a successful AI implementation. Stay tuned for additional articles within the Decoding AI series that discuss more details on these next steps:

  • Building an AI toolbox — tool selection
  • AI communications and education — training and adoption
  • KPI’s and measuring outcomes

As always, I am available for any questions or help you may need bringing this into your organization. I offer up some great free resources including my XR/AI Marketing Brief — a monthly newsletter — and the AI Marketing Revolution Challenge Video Series.

I would love to connect on Linkedin and perhaps meet at my next live XR Pub Crawl for Marketing on January 19 at noon PT. Happy new year.

Get MarTech! Daily. Free. In your inbox.

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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