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Deep changes in the CDP space

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Deep changes in the CDP space

There’s a sense of tectonic shift in the marketing technology space right now. Of course, it’s a space which has been growing and evolving at a staggering pace for some ten years. I’m talking about more fundamental change.

Think of marketing automation, CRM and customer data as three of the tectonic plates which make up marketing technology’s crust. They’re moving. Imperceptibly, perhaps, at the level of day-to-day operations and campaigns, but discernibly at a more strategic level. I’ll be honest, I don’t know what the new configuration of marketing technology will be – but I think I know the right question to ask: What is the future of the customer data platform? Will CDPs take over the activate-and-execute role of marketing automation? Will they supersede CRM as repositories of customer data?

Boom or bust for independent CDPs?

It’s easy – and accurate – to think of the CDP category as large and growing. There’s a lot of interest in CDPs, especially, but not only, from enterprise B2C brands trying to personalize engagement with their customers at scale. At the same time, independent CDPs, some of them well-established and well-known, are being gobbled up at an unprecedented rate by more comprehensive marketing suites or digital experience platforms.

AgilOne acquired by Acquia, Segment by Twilio, Zylotech by Terminus, Boxever by Sitecore, Zaius by Optimizely, BlueVenn by Upland Software, Exponea by Bloomreach: That’s a trend. At the same time, independent CDPs like Treasure Data, Amperity, Tealium and Blueshift raised significant amounts in funding this year.

Is the independent CDP category under threat or not? I asked Tasso Argyros, the founder and CEO of a leading enterprise CDP, ActionIQ, what’s going on.

“My view is that the independent CDPs that have been sold so far were either struggling to break through and had given up on being leaders of the CDP category, or they weren’t core CDPs – by core CDPs I mean the CX hub.” An example of the first kind, AgilOne, was such an early entrant to the category that its product was built on outdated technology, he said.

“In the other category are companies like Segment. Segment was a tag manager and they were trying to expand more towards intelligence and orchestration, but the reality is that their roots were in capturing and moving pieces of data around, not so much in doing analytics and orchestration – so it was hard for them to compete as an enterprise standalone.”

Argyros, naturally, sees an opportunity for ActionIQ to be the dominant independent player in the space. “That opportunity is massive and it would be too early for us to sell right now. That being said, how much space is there for dominant CDP platforms? I think two or three at most. Everyone else will get acquired at the end of the day – that’s my prediction.”

Part of the value proposition of an independent CDP, he explained, is to enable a best-of-breed stack. Adopting an Adobe or Oracle or Salesforce CDP can have the effect of locking brands in to other solutions from those big players. “You go with ActionIQ, you can have a truly best-of-breed stack. We play well with anyone.”

Treasure Data, another CDP with large enterprise brands on its client list, raised $234 million in venture funding in November. Founder and CEO Kazuki Ohta echoed Argyros’s observations. “The vendors who cannot grow faster and cannot raise the money from VCs are obviously trying to find an exit,” he said. “This is a hot industry, it’s a good time to exit.”

Like ActionIQ, Treasure Data’s proffer is vendor neutrality. “It’s sort of like a Switzerland approach,” said Ohta. “

The need for independent CDPs

I turned to one of the closest observers of the category, David Raab, founder of the CDP Institute and an occasional MarTech contributor. “The companies buying them, in most cases, have multichannel delivery systems. Those systems are often acquired and not natively integrated, and they realize they need that CDP to pull the data together to integrate their own systems, as well as pull in data from other channels that they’re not managing. There’s a demand from their buyers for unified data. It’s really hard to build a CDP so it makes more sense to buy one. Easier and quicker.”

Does this trend threaten the independent CDP category? “It certainly shrinks the market for the independents,” he said. “What we expect to happen is that the independents will specialize more in particular niches, making it easier for them to defend their position.”

There will continue to be a need for independent CDPs at the enterprise level, where multiple functions – not just marketing – need to be able to manage and activate customer data. “That’s where ActionIQ sits, that’s where Treasure Data sits,” Raab said. “You need that CDP to be vendor-neutral. Then there will be the verticals, specializing in transport or healthcare or education. We’re seeing a lot of CDPs that are vertical industry specialists and that’s also a defensible position.”

In fact, ActionIQ recently staked its claim as a CDP for the healthcare space, while Treasure Data, which started out selling into marketing organizations, is now explicitly addressing other functions in the enterprise with its CDP for Service and CDP for Sales.

This made sense to Raab. “There are multiple buying centers. Marketing has been the primary one, but customer success has always been a buying center for CDPs. There are paths into companies which are not the marketing path or the IT path or the data team path. There’s a value in department verticalization where you have special features that work best for customer success or for whatever department you’re selling into.”

One thing that puzzled me initially about offering CDPs for what Raab calls departmental verticals is that it surely creates data siloes. Ohta explained: “It’s just a fact that our customer’s organization is siloed and also data is siloed. If you look at the 150-plus CDPs in the market, they’re trying to pitch their product primarily to the marketing department. We’re trying to change our customers’ behavior to use data in every single division so they can better serve their customers in every part of the customer journey.”

The next step, then, is to pull together the profiles in marketing and service and sales siloes, to produce a comprehensive view? “Yes,” said Ohta, “of course.”

Next generation campaign management

It’s practically received wisdom that not every solution offered as a CDP is a real CDP, but in fact it might be accurate to say that there have always been different types of CDP. The distinctions are becoming starker as some CDPs aim to be not just the single source of truth on customers, but the hub for orchestrating and delivering customer experiences.

This type of full-service CDP goes by various names. Vijay Chittoor, founder and CEO of Blueshift, calls CDPs which deliver profile unification, audience segmentation and campaign activation “smart hub” CDPs, a term borrowed from Gartner. Argyros talks about CDPs as the “CX hub” or “next generation campaign management.”

“There are a couple of types of CDP in the market,” said Ohta. “One is the vendors that came from a tag management space where they focus on the website and mobile data collection side. The other one is more on the activation, the execution side. There’s a lot of confusion around the category itself, I admit.” Treasure Data, said Ohta, could expand into execution, but it positions itself as being able to activate the data, but feeding it into other solutions – ESPs, messagine channels and so on – for execution.

The idea that the main job of the CDP should be to connect data is just wrong, Argyros told me. “We do it because we have to.” How much depends on whether the client has their data in order. “Connecting data is a means to an end, and if we don’t have to connect data we love that. We can deploy faster. What the CDP is becoming,” he said, “is essentially a next generation campaign management platform and a next generation customer intelligence platform.”

He continued: “In the past, campaign management was completely disconnected from the data, because there wasn’t much data to begin with. Now that you have terabytes and terabytes of data, your campaign management platform has to do very large-scale data processing. What you see is a collapse of the data mart that was used for campaigns into a single stack that’s called a CDP today. It’s like campaign management 3.0.”

Customer intelligence is critical too, Argyros argues. The tools for doing customer intelligence outside a CDP are inherently limited. Web analytics is restricted to website activity. Business intelligence gives good aggregate level data but cannot provide customer journey level insights. “The CDP has become the de facto place to gather intelligence and tie it really well with campaigns. You go from data to intelligence to action in the same platform, which is the CDP.”

Read next: Enterprise Customer Data Platforms: A marketer’s guide

Will smart hub CDPs make marketing automation redundant?

Raab doesn’t see CDPs usurping the role of MA. “Most marketing automation systems are really sending out emails primarily and there are some CDPs that can send out emails.” He offered Algonomy as an example of a CDP with core email marketing capabilities.

“There’s quite a few that have very strong delivery capabilities, channel-facing capabilities, and they’re absolutely doing what marketing automation can do,” he said. “In other cases, not so much. Marketing automation often has a B2B flavor to it and there’s a close integration with the CRM system. You have a bunch of specialist features that you’re getting from a marketing automation system that may not be built into a CDP.”

There’s also a key difference in the way data is structured in marketing automation and CDPs, he added. “You have a big bulk data store that stores everything in all the gruesome detail – semi-structured at best.” That’s the CDP. “Then you have a more structured data store that does all your segmentation and runs your marketing automation and so on. You’re always going to have basically two different kinds of technology, each doing what they’re best at. Are they in the same system? Great, that saves you some trouble.”

About The Author

The holiday season is upon us
Kim Davis is the Editorial Director of MarTech. Born in London, but a New Yorker for over two decades, Kim started covering enterprise software ten years ago. His experience encompasses SaaS for the enterprise, digital- ad data-driven urban planning, and applications of SaaS, digital technology, and data in the marketing space. He first wrote about marketing technology as editor of Haymarket’s The Hub, a dedicated marketing tech website, which subsequently became a channel on the established direct marketing brand DMN. Kim joined DMN proper in 2016, as a senior editor, becoming Executive Editor, then Editor-in-Chief a position he held until January 2020. Prior to working in tech journalism, Kim was Associate Editor at a New York Times hyper-local news site, The Local: East Village, and has previously worked as an editor of an academic publication, and as a music journalist. He has written hundreds of New York restaurant reviews for a personal blog, and has been an occasional guest contributor to Eater.


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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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