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Where is the puck going in 2022?

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Where is the puck going in 2022?

Perennial NHL all-star Wayne Gretzky famously advised skating to where the puck was going, not where it is right now — a phrase that has since gotten widely adopted in a martech world fraught with constant change.

Of course, this approach makes sense. If you always target the status quo, what happens when digital marketing currents shift, as they constantly do? Consider: Who today wants to build a martech strategy based on third-party cookies?

At the same time, two decades in the analyst world has taught me that nobody — nobody — in our industry really knows exactly where the puck is going, at least not with Gretzky-like certainty. So the best you can do is strengthen your core to be able to shift quickly as the martech state-of-play evolves. Think of it as a kind of “pilates for your stack.”

Three exercises for your martech Stack

If we’re talking pilates, then what you want to do is focus on your core. Here are three important muscles to prioritize in 2022.

1. Customer data unification and management

Customer data platform (CDP) technology is hot right now, and for good reason. At RSG, we evaluate thirty vendors and climbing. But the industry’s dirty little secret is that many initial implementations offer only scant value amid a dearth of robust, clean, unified customer data. Enterprises that thought their CDP would provide the essential building blocks of customer data processing have, for the most part, come away disappointed.

In 2022, focus on your broader customer data enterprise “fabric.” You’ll find it a complicated but potentially very rich tapestry. But most importantly, you need to get the base infrastructure right: Ingestion, cleaning, stewardship and identity. The good news is that more than your marketing department needs this sort of hub as well. This short briefing explains some key architectural considerations.

2. Component content for an omnichannel future

There’s a similar muscle to build on the content side of your stack. If you are pursuing an omnichannel strategy — and who isn’t? — then you will need a reliable store of reusable content components. Not just snippets of text, but base image/media building blocks and relevant data, especially product data.

Where would you find the authoritative source for all these components today? Probably all over the place, and worse, mostly stuck in a single engagement channel like email, web, or e-commerce.

Prioritize getting a handle on all of these marketing assets and ensuring that you have a common metadata regime to classify (so you can find and manage!) the individual pieces. Unfortunately, the Omnichannel Content Platform marketplace remains nascent, but savvier enterprises are beginning to step into the pool.

3. Internal capacity

The martech capacity gap (see image below) predates COVID, but the pandemic has surely aggravated it for every enterprise. RSG vendor evaluations can help you with the hyperbole gap, but capacity is a more complex challenge. For example, nearly all the large-enterprise members on RSG’s large-enterprise MarTech Council have suffered talent shortages.

Where is the puck going in 2022
Solid vendor evaluations can close the hyperbole gap, but it’s up to martech leaders to close the internal capacity gap. Source: Real Story Group

I don’t have any magic wand to wave here that will close this resource gap, except to encourage you to keep working to create an attractive workplace for martech specialists. What you want to avoid is over-reliance on an outsourcing strategy. First of all, martech integrators are also short-staffed too, but more importantly, you want to keep and maintain as much learning and expertise in-house so you can move quickly as the puck gets passed around.

The case for not overskating

In some enterprises, I see the “future puck vision” justify over-buying complex marketing technology, ultimately undermining the very speed and agility you seek.

The thinking goes like this: “We probably can’t handle this advanced technology today, but in a couple of years, we’ll get there, so let’s license it now, and maybe it will push us to become more proficient.” Of course, in two years, the puck will have moved even farther. Or, to put it another way, if you can’t master an overly complicated martech platform now, you will only find it all that richer (read: more complex) two years hence. So you end up perennially behind.

RSG always advises looking at vendors representing differing points on the complexity spectrum. Ideally, you can find the right match with a system that stretches you a bit yet won’t go underutilized. Meanwhile, any technology supplier should constantly be innovating themselves, so in all likelihood, the simpler solution you adopt today will get enhanced over time. Careful diligence in a structured selection process can uncover whether that vendor or open-source community is skating towards the ever-moving puck at a pace and direction that works for you.

Real Story on MarTech is presented through a partnership between MarTech and Real Story Group, a vendor-agnostic research and advisory organization that helps enterprises make better marketing technology stack and platform selection decisions.


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


About The Author

Is that vendor a zombie

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.”

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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