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How to establish a new martech role

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How to establish a new martech role

Although I’ve spent most of my career in roles and functions adjacent to marketing technology, I’ve only been truly dedicated to martech during the past few years. I formally entered the martech space when I was asked to create a new role at my previous employer, Western Governors University (WGU). 

It took a while for my former boss and me to flesh out a job description and select a job title. That required researching various job postings to see what other companies were doing regarding what we had envisioned for my role. A few years later, I moved to my current gig at Zuora.  

Thankfully, my WGU job title — marketing technology manager — and description matched up with what Zuora was thinking. Nevertheless, persistent confusion about what “marketing technology” and “marketing operations” mean hints at some need to standardize martech role terminology.

At both WGU and Zuora, I’ve had to originate my martech maestro/orchestrator role. In addition to figuring out my duties and the associated skills, I’ve also had to help my colleagues become familiar with the new position. There are certainly some valid questions: What’s a marketing technology manager? What do they do? How do I interact with them?

This is no easy task. It requires support and help.

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Job title and description

At the time, it seemed like a great opportunity to select my job title and create my own job description at WGU, but it was challenging.  

That’s why I feel that martech job descriptions are still too nebulous given the growing maturity of the field. When considering what the employee and their employer want to accomplish with the new role, using Scott Brinker’s marketing operations job type framework is helpful – especially when considering if it’ll fulfill a T-shaped (jack of all trades) or I-shaped (specialist) function.

Read next: A closer look at MarTech role types and T/I-shaped individuals

Leadership backing

Perhaps a big key to success is leadership backing for the new role. Leaders should also assist in educating others about it.  

For instance, when a marketer is looking for a new technical tool, they should consult with the marketing technology manager, so they should be referred to the marketing technology manager. That referral educates and enforces the importance of the new martech practitioner.

That’s a great time to start showing the role’s value to their work. If the requester doesn’t have a solution identified yet, offering them a directory site like G2, Gartner Digital Markets, CabinetM or TrustRadius is a great way to show them that a marketing technology manager can quickly find viable options.

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The position is typically created after a marketing department has matured somewhat. In that case, the broader organization has likely also matured to establish some bureaucracy — for better or worse. 

Instead of simply picking a solution, appeasing Legal, and arranging payment, the marketer now faces numerous processes like IT security reviews, privacy evaluations, multi-step procurement processes, and stakeholder buy-in; buying and renewing technology is a lot more complicated than in the past. These are tasks that a marketing technology manager could take off of the marketer’s plate to focus on what they’re assigned and paid to do.


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RACI is a useful tool

A useful tool for establishing a new position and educating others about it is a RACI chart. RACI stands for: Responsible, Accountable, Consulted and Informed. It usually lists different generic duties and tasks in the rows and different generic roles (martech manager, stakeholder, IT, etc.) in each column. In the cell that associates a duty or task with a role, one of the letters from the RACI acronym is listed to show what is expected of that role at that time.

While one can introduce complexity into the RACI framework, its beauty is tied to the fact that it is pretty easy to get an operational grasp of the concept. Thus, creating or interpreting a RACI chart doesn’t take a lot of effort.  

When creating a RACI chart for a martech role, it helps to group tasks under general categories — buying new tech, retiring tech, renewing contracts, researching solutions, etc. Not only does this help make the chart digestible, but it also helps people place tasks into context better.  

For instance, the same task may exist in different categories, but the RACI distribution may differ. When it comes to finding a new solution, a stakeholder will likely kick off the process while the marketing technology manager may kick off a renewal or optimization process.

Challenging but doable

It is challenging starting work in a new position, and there’s more complexity when originating that role at the organization. However, it is best not to proceed alone. Some frameworks can help guide job description drafting. Leadership should help and advocate for the new role. Further, tools like the RACI framework can help simplify a seemingly daunting process.  

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Given all of this, take input from the community instead of reinventing the wheel, which is unnecessary and can cause further confusion amongst practitioners.


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


About The Author

Steve Petersen is a marketing technology manager at Zuora. He spent nearly 8.5 years at Western Governors University, holding many martech related roles with the last being marketing technology manager. Prior to WGU, he worked as a strategist at the Washington, DC digital shop The Brick Factory, where he worked closely with trade associations, non-profits, major brands, and advocacy campaigns. Petersen holds a Master of Information Management from the University of Maryland and a Bachelor of Arts in International Relations from Brigham Young University. He’s also a Certified ScrumMaster. Petersen lives in the Salt Lake City, UT area.

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Petersen represents his own views, not those of his current or former employers.


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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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