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How is marketing operations evolving?

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Those of us who advise about martech stacks can go pretty deep on topics like service and platform boundaries and intersections, and we have strong opinions on what a future-proof stack should look like for any given enterprise. Just look at Real Story Group’s latest reference model:

RSG Reference model for an omnichannel stack. Source: Real Story Group

But what about the teams that actually run and leverage those platforms? How should the people who manage and run martech be organized? Where should they fit in the larger organizational structure?

I’ve long believed that organizational design for martech constitutes more of an art than a science, but perhaps that’s just because I’ve never seen a large and clear enough data set from which to draw useful conclusions.

As a first step towards better research in this area, Real Story Group invited our MarTech Stack Leadership Council to share and critique each others’ organizational models. I can’t describe the details because these sessions are confidential, but the high-level discussion was fascinating, and some patterns emerged, which I’ll share below.

A trend toward global

RSG Council member organizations are larger enterprises, usually with an international or global footprint. Over time, I’m sure you’ve witnessed the push-and-pull dynamic between globally central control vs. local autonomy for digital. Council members indicated that Covid (among other factors) has lately pushed the pendulum towards centralized operations.

One general theme was: “Centralized platforms, with local expression.” This means every business line or region might use, for example, the same outbound marketing platform, but deploy campaigns locally. For some this was the only way to scale during a period of intense growth in digital customer touches. It also creates space for an overall compliance framework and ops team to support ever-expanding local privacy regulations.

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Applying the chart above, the more foundational the platform, the more likely you can successfully apply it globally. As you get closer to the customer (i.e., move up the diagram), the more local teams may need their own capabilities, e.g., for outbound campaigns and messaging, or social media management. 

Growing operational control 

The trend toward centralized platform management also extends to product management. Here again, individual marketing and customer-experience execution teams may vary in how they use a central platform, but most RSG Council members have carved out a core team defining the stack, and — critically — setting individual platform roadmaps.

This often requires central martech teams to serve in a consultative way. We saw several models for executing on martech centers of excellence. Some Council members have marketing services organizations, similar to or combined with internal agencies, often working with a centralized DAM/omnichannel content platform, outbound marketing platform and/or CDP. 

This trend has not come without push-back. Nevertheless, greater centralization and growth of formal martech operations can bring a solid business case, especially around efficiency. It can provide faster time to market, asset and campaign re-use and formalized lesson-capture. Centralized ops can also bring faster time to value when entering new markets. “This is the only way we could get to scale across markets,” observed one stack leader.

This doesn’t mean that these run in a vacuum. In nearly every case study, we saw a steering committee representative of broader institutional stakeholders, including IT, enterprise data, and key adjacent services, like sales and/or support. 

Enduring friction points

Several points of friction endure for centralized martech teams, and different Council members addressed them (or not) in different ways.

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Should martech teams embed or partner with IT? At RSG we tend to see a mix of both models. Marketing teams still struggle to manage and retain heavier-duty (often back-end development) talent. Also, systems integrators often prove essential for heavy-lift projects. 

A similar dynamic arises around analytics. Marketers need ever-faster reporting and optimization cycles, with reference to non-marketing data (like sales transactions). This function tends to straddle marketing and BI departments, not always comfortably. Savvy teams are building internal data analytics skills, but may not have access to all the data or tools this requires. 

On the plus side, there’s a growing consensus around savvier enterprises that AI/ML is best considered an enterprise concern. Prudent martech leaders will remain cautious about having potentially immature AI/ML services embedded in marketing and engagement platforms.

Inevitably, martech operations encounter the limits of centralization. The pandemic has spawned more cross-functional teams (a good thing!) and any large enterprise will experience waves of localized initiatives. 

The picture below is from a Council member org chart. It shows how the central team has to increasingly serve an interwoven set of other initiatives and tiger teams. Adaptation from a central core of capabilities becomes the watchword, as strategic martech operations respond to shifting tactical needs. 

Parat of an org chart showing tiger teams...
Excerpt of a martech org chart from a global hospitality firm. Source: RSG

Conceptual convergence, descriptive diversity

At one session, eight Council members presented org structures and another 20 commented on them. I was struck by the diversity of visual representations, even if members seem increasingly aligned on the substance of where they’re going organizationally.

It’s possible we still lack a common vocabulary — and certainly universal visual metaphors — for describing these issues. Still, I’m certain we’re going to discuss more on this topic when Council meets in person (finally!) in September. In the meantime, I hope you found this summary useful, and feel free to share on LinkedIn if you’d like to continue the conversation.

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


About The Author

The right way to select a CDP

Tony Byrne is founder of Real Story Group, a technology analyst firm. RSG evaluates martech and CX technologies to assist enterprise tech stack owners. To maintain its strict independence, RSG only works with enterprise technology buyers and never advises vendors.

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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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