MARKETING
Insights from real-world usage (so far)
We had no idea what to expect when we launched MarTechBot in April. While factually correct, characterizing our experiment as the “first generative AI chatbot designed specifically for marketing technology professionals” seemed somewhat grandiose.
Was MarTechBot innovation? Would it be useful? Did anyone care? All we knew for certain was we’d trained MarTechBot on MarTech.org content and we’d learn a lot by putting the technology into your (and our) hands.
Since April, MarTechBot has engaged in over 600 conversations with marketers. It’s answered questions, made recommendations and generated content based on your prompts. Based on the responses it’s created, MarTechBot has delivered value to the marketers who’ve used it.
Understanding MarTechBot’s versatility and functionality
To discern how you’re using MarTechBot, we categorized each conversation in two ways.
First, we determined the purpose of each conversation. Was the conversation a question (e.g., “What is a CDP?”), a request for a recommendation (e.g., “What is the best CDP?”) or a generative/creation task (e.g., “Write a project plan for implementing a CDP.”)?
We then categorized each conversation by identifying a central theme. For example, general marketing conversations were tagged as “marketing,” while conversations specifically about CDPs were tagged as “CDP.”
This heuristic illustrates how MarTechBot has been used so far. In all cases, we used our judgment to classify each conversation manually (a labor of love!). This methodology is decidedly imperfect but useful.
Generative/creation tasks: Inspiring innovation and creativity
Completing generative/creation tasks was the purpose of nearly half of all conversations with MarTechBot. This category includes brainstorming sessions, content planning and ideation of new strategies and frameworks.
By leveraging the creative potential of MarTechBot, marketers tapped into its vast knowledge base to generate fresh ideas and discover novel approaches to tackle marketing challenges. The bot was the catalyst for solving problems or generating suggestions about creativity.
Example conversations:
- For beginners in martech, prepare a roadmap for learning and becoming an expert in 4-6 months.
- Act as a marketing strategist for a B2B cybersecurity firm specializing in big corporate companies as clients with several decision makers and decision influencers. Devise an Account Based Marketing (ABM) plan.
- Can you write a good email subject line for an email with a prize element and informational content?
Questions: Unraveling marketing dilemmas
The second largest segment, constituting 43.5% of conversations, was marketers seeking answers to questions, much like they’d use a search engine. MarTechBot was used to clarify concepts, explain best practices and provide expert opinions.
Example conversations:
- What is CDP? (by far the most common question)
- How big is the MarTech landscape?
- What does ABM mean?
Recommendations: Unlocking insights and strategies
Recommendation-oriented conversations account for less than 10% of interactions with MarTechBot. In this category, marketers sought guidance, e.g., selecting the right marketing technologies, optimizing campaigns and the best ways to optimize customer experiences.
Example conversations:
- What is the best way to level up as a marketing technologist?
- What marketing channel works best for individuals older than 65 years old?
- Which CDP provider can you recommend for a midsized company?
Themes of conversations: Insights into marketers’ areas of interest
Our analysis also revealed the themes that dominate conversations with MarTechBot. Here are the top five:
- Marketing (21.5%): Marketers seek insights, trends and best practices across various marketing disciplines, including digital marketing and social media marketing.
- Customer data platforms (12.5%): Discussions around CDPs revolve around understanding their capabilities, implementation strategies and leveraging customer data for targeted marketing campaigns.
- Marketing technology platforms (9.7%): Conversations in this category focus on exploring different marketing technology platforms, such as marketing automation tools, CRM systems and analytics solutions and their integration into marketing operations. This theme was a catch-all for conversations about multiple platforms or more general platform questions.
- Industry (6.9%): Marketers engage with MarTechBot to gain industry-specific insights, ranging from niche marketing strategies to understanding industry trends and benchmarks.
- Marketing operations (6.7%): Marketers seek guidance on streamlining marketing operations and improving efficiency through automation and process enhancements.
Rounding out the top themes 10 were email marketing, the catch-all category “general,” martech, data and AI.
We identified and classified ~50 themes, a diverse collection highlighting modern marketers’ wide range of opportunities and challenges. Beyond the top themes shown in the graphic, honorable mentions to Google Analytics/GA4, marketing automation and SEO.
What’s next for MarTechBot
The insights gained from analyzing real-world MarTechBot conversations provide valuable guidance to marketers looking to harness the power of this innovative tool and other generative AI platforms. By understanding the categories of recommendations, questions and generative/creation tasks, as well as the primary themes, marketers can leverage MarTechBot to gain insights, make informed decisions and foster creativity in their marketing endeavors.
As MarTechBot continues to evolve and address its limitations, it holds immense potential to transform how marketers strategize, execute and achieve their goals. The power of AI-driven chatbots like MarTechBot lies in their ability to provide personalized recommendations, unravel complex marketing dilemmas and inspire innovative ideas. By embracing this technology, marketers can enhance their capabilities, stay ahead of the curve and unlock new dimensions of success.
We’re committed to improving MarTechBot by including more information in its language model.
For example, we’re addressing issues that result from training MarTechBot on long-form articles, video transcripts, or PDF files. In those cases, the bot has trouble parsing information and may sometimes generate unexpected or inaccurate responses.
In other cases, the bot provides answers that are incomplete or incorrect. We are actively working on refining these aspects and ensuring more accurate responses.
Additional reporting and analysis by Karina Sarango.
Get MarTech! Daily. Free. In your inbox.
MARKETING
YouTube Ad Specs, Sizes, and Examples [2024 Update]
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!
MARKETING
Why We Are Always ‘Clicking to Buy’, According to Psychologists
Amazon pillows.
MARKETING
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.”