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Three ways to organize your martech stack

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What’s in your marketing stack? Let us know

Earlier this month, Scott Brinker revealed the 2022 Stackie Award Winners. The Stackies are a contest for organizations to submit a visual illustration of their martech stack. This year, five different winners were selected.

My favorite part of the Stackies is observing all the different ways organizations choose to organize and catalog their martech stacks. After reviewing this year’s entries, below are three of the most common ways to consider organizing your martech stack and some of the unique benefits of each approach.

1. The customer journey

One of the most popular ways to organize your martech stack is to align your technologies with the stage they support in the customer journey. Different companies have different terminology for the different phases, but it typically goes something like “Awareness,” “Consideration,” “Purchase” and “Onboard.”

In this example, SEO tools would typically be categorized under the “Awareness” phase, whereas e-commerce platforms would easily fit under the “Purchase” phase. When categorizing your tools this way, there are two challenges you want to be sure to account for:

  • Make sure you have a way to tag some technologies under multiple customer journey stages. For example, your marketing automation platform would likely be used across multiple stages, including “Awareness,” “Consideration” and “Onboard.”
  • You will also want to have an entirely separate category or two for tools used for internal purposes that customers don’t necessarily directly interact with along their journey. Data and analytics tools, as well as internal workflow and collaboration tools, would fall into these categories.

And there’s an added benefit. Categorizing your tools this way gives you a great visual to see what tools are affecting multiple stages of the customer journey and, therefore, may require more investment or resources. For example, if your marketing operations team has been pushing for increased headcount, showcasing how the platform impacts nearly every stage of the customer journey may help you garner internal support from leaders even outside of marketing, such as sales or customer support.


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2. Technology category or subcategory

One of the most common and popular ways to categorize your martech stack is simply by the technology category they belong to. This is how the famous Martech Landscape supergraphic, along with the new interactive MartechMap, organizes tools. When organizing your martech stack, you could choose to keep your categorization at the highest level, such as “Advertising and Promotion,” “Content and Experience,” “Social and Relationships,” “Commerce and Sales,” “Data” and “Management” or you could choose to get a step more granular and assign your tools according to their appropriate subcategories.

For example, the subcategories under “Content and Experience” may include “Email,” “Social,” and “Web” among others.

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Another added benefit: One of the biggest challenges that marketing organizations face is the proliferation of technologies available. Marketing organizations struggle to take full advantage of their martech stack’s potential. According to the Gartner Marketing Technology Survey 2019, marketing leaders report utilizing only 58% of their martech stack’s potential, down from 61% in 2018.

Organizing your tools by the category they belong to can help you easily identify where there may be opportunities for consolidation within your martech stack. For example, you may discover that you are using multiple survey tools across the organization because individual teams needing a quick survey have set up free or low-cost accounts on platforms like SurveyMonkey or Google Forms. You may have a customer experience group using a more robust platform such as Qualtrics, which handles customer surveys. That could be an opportunity to consolidate onto one survey platform.

Consolidating your martech stack can help you take better advantage of your martech stack’s potential by cutting costs, reducing data silos, and ultimately enabling users to spend more time diving deep into all of the available features of one tool and sharing that knowledge with others.

3. Internal organizational structure

 Another way to organize your martech stack is by the internal teams responsible for operating those technologies. For example, an organization may typically include a data and analytics team, a marketing operations team, a content management team and an advertising team. In this situation, one team may own some tools, such as display advertising tools for the advertising team or the website CMS, which only the content management team can access. However, there are likely quite a few tools that multiple teams have access to, such as some data and analytics tools, like Google Analytics.

When categorizing Google Analytics within your martech stack, you may realize that it needs to be associated with more teams than you initially thought. Of course, the data and analytics team has access to Google Analytics, but so does the advertising team, who is using it to focus on conversion rates of their campaigns. The content management team may have access to look at page load times. The marketing Operations team may also use it to determine the highest converting pages they should incorporate into their lead scoring models.

1655472348 402 Three ways to organize your martech stack

Added benefit? Cataloging your martech stack along organizational lines helps highlight where there is shared access and ownership within certain tools. This gives you the visibility to ensure you have the right policies, procedures, and rights management in place to ensure that different teams are not stepping on each other’s toes or operating in different ways that could ultimately hurt overall efficiency.

For example, in Google Analytics, you would want to ensure that multiple teams do not share editor rights, which would allow someone in the marketing operations team to edit the default channel groupings, which could potentially break some of the ways that the advertising team is optimizing spend across channels.

Read next: How startups and small companies should build their marketing stacks

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If you categorize your martech stack by your organizational structure, set up regular reviews of tools with shared access to ensure that you have the right governance policies in place and that they are being followed.

There is no right or wrong way to categorize your martech stack, as each approach has its purpose and benefits. You also do not have to limit yourself to just one approach. As you can see above, taking the time to categorize your martech stack in different ways may help you achieve particular goals or better suit you when sharing that visualization with a particular leader. No matter how you categorize it, the most important thing is to ensure you regularly audit and update your martech stack. 


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


About The Author

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5 tips to increase user adoption of new martech tools

Megan Michuda is currently the SVP, director of marketing operations and innovation at BOK Financial. Prior to joining BOK Financial, she served as global head of marketing technology at Janus Henderson Investors. Janus Henderson was a Stackie Award winner in 2018. Megan is currently responsible for BOK Financial’s marketing technology stack, marketing automation, digital analytics, and marketing operations. In 2020, Megan’s startup Stacktus was acquired by CabinetM, a leader in martech management. Megan is now both a user of CabinetM as well as an advisor. Megan received her bachelor’s degree from Brown University and her master’s of science in technology management from University of Denver.

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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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