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5 enduring trends in martech

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5 enduring trends in martech

During my MarTech Master Class workshop, “The Right Way to Buy Marketing Technology,” I received a very interesting question from a participant.

What five things do you think will remain unchanged about marketing technology in the next ten years?

The question made me pause because usually I receive queries about what will change, as martech leaders try to “skate to where the puck is going.” In a fast-paced martech world that affords too-little time for reflection, it’s useful indeed to think about continuity, among other reasons, because it can help focus your energies and keep you from wasting time chasing ephemeral fads.

So herewith are my top five predictions for enduring trends.

Effectively managing customer data is table stakes for prospect and customer digital engagement. Most of us haven’t been doing this very well. Getting on top of customer data management will likely become a decade-long (or more) pursuit.

At RSG, we advise many large enterprises on CDP evaluation and selection. The nature and scope of these projects vary widely, but one theme remains constant: Implementing a CDP just exposes long-latent structural problems in the way you’ve been collecting, processing, securing and managing customer data. This work is hard!

Moreover, the coming years will make it even harder. Security risks are growing. Regulatory compliance is stiffening. Consumers and their advocates are (rightly) pushing for better norms and transparency around how their data is collected and used.  


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I have been on the front lines of advising and sometimes implementing personalization technology for twenty-five years now. The dirty little secret in our industry is that everyone talks about personalization, but very few enterprises actually do it in a methodical way.

For most of you, it’s been a five-steps-forward, four-steps-backward endeavor. Measuring the effectiveness of personalization remains difficult, and despite breathless vendor case studies, many if not most pilot projects here fail to achieve meaningful ROI. Part of this relates to customer data deficits noted above, but other challenges revolve around customer unpredictability and mistaken assumptions about what the person on the other side of the screen really wants (as opposed to what you think or wish they would want). It shouldn’t surprise you that many consumers find personalization efforts creepy or hackneyed.  

This is not a blanket indictment of trying to tailor more effective user experiences. The best organizations systematize test-and-optimization cycles. Segmenting customers in cohorts can make messages more germane.  Prioritizing business use cases really helps. But given the resource overhead, one-to-one personalization has not yet found its magic bullet.  

So here again, we’ll be spending a decade figuring out personalization. I suspect that, for most of you, slow and steady will win the race. 

As a journalist in a previous life who now taps out research reports for a living, I’m biased towards the power of good content. However, it seems like the commitment to crafting excellent content among the large enterprises RSG advises tends to wax and wane over time.  

I’m not sure exactly why. Clearly, content is central to any effective user experience – just ask any UX designer. Yet good content can be expensive to create and complex to manage and re-use in an omnichannel world. Some AI adherents boast that new platforms can solve the former challenge of content creation. Pro tip: They cannot.  

With respect to content management, we’ve come a long way, but many challenges endure.  Too many enterprises over-rely on agencies for content creation and lose a lot of intellectual property along the way. Traditional web content management platforms are really bad at omnichannel content management. Component content management is essential in a customer-centric world, but it’s as hard to manage now as it was twenty years ago.  

Fortunately, some new technologies and approaches are emerging, and some enterprises are getting smarter about content supply and demand chains. Still, ten years from now, the successful digital transformers will have gotten content right in the interim.

New martech markets will evolve over the coming decade. Some existing vendors will fold or consolidate. But I guarantee one structural continuity: In any given market, you will confront a plethora of choices. In general, this is a good thing, though it can cause vertigo for enterprise technology buyers (hint: apply design thinking to your tech choices).  

I’ve written elsewhere in these pages why these markets remain so fragmented. The cost of new-vendor entry keeps falling, and cloud models tend to reduce supplier risks. Despite recent contractions, we live in a world awash in investment capital, and the next decade will see it continuing to gravitate to martech. At RSG, we’ve tried to focus on the most important 160 vendors for our enterprise subscribers, but it’s an ongoing challenge.  

Vendor marketplaces remain highly fragmented, though some “suite” vendors offer multiple solutions across domains.  Source: Real Story Group

This doesn’t remove risk calculations in your supplier choices as much as juggle them. You might end up with a “zombie” vendor. Your supplier might prove finicky and radically adjust roadmaps (we’re seeing more of this in the CDP space). So you will likely still have many good and bad vendor choices, but likely fewer catastrophic ones.

Today martech marketplaces are characterized by suite vendors purporting to sell collections of platforms (see the center of the map above) versus more focused, “point solution” suppliers. It’s an old story in the tech world and increasingly germane in the marketing space. 

At RSG, our research and anecdotal experience suggest that savvier enterprises who build their stacks one service layer and component at a time see better results. They’re also less apt to be bullied into making poor decisions.  

But the debate endures and I think will carry over into the 2030s. That said, you might see a new class of suite vendors emerging.  Today Adobe, Salesforce, and to a lesser extent, Microsoft and SAP offer manifold marketing and digital experience solutions. I’ll go out on a limb and guess that over the next decade, they will all accumulate (even more) technical debt and become the IBM and Oracle of the 2030s.  

But the “suite” bundle will remain attractive to martech leaders looking for shortcuts to thorny integration problems, so I’ll also guess that a new crop of vendors will take their place.


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.”

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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