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How do they differ from packaged solutions?

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How do they differ from packaged solutions?

“Composable CDP is not a thing. Composable architecture is,” my colleague Craig Howard previously penned in an internal missive. He explained that customer data platforms (CDPs) gained traction when organizations could not implement their own cloud-native customer data store and could purchase a commercial, off-the-shelf solution — a “packaged” CDP — that could help them realize the benefits of cloud technologies by managing their customer data. 

But things have changed more recently:

  • IT organizations have evolved and built skills around cloud technologies. 
  • Data integration needs have often surpassed the CDP capabilities. Many CDPs struggle to manage complex data structures or handle answering complex questions about the data.
  • Policies and a patchwork of global laws have introduced complexity around privacy, consent and data residency.

Brands are now creating their single customer view with cloud-native identity resolution, data integration and data storage capabilities. CDPs are adjusting to this paradigm, the data clouds and the resulting composable architectural pattern, calling themselves a “composable CDP.” 

Packaged vs. composable

A composable CDP is based on an architecture anchored on a cloud data store for customer data. In composable, the CDP becomes an orchestration platform — managing audiences and journeys and activating the customer data. 

Yet, deciding to go with composable vs. packaged CDP is not straightforward. First, if you’re shopping for either, your head is in the right place. Activating first-party data across channels is the future. If your decision is composable vs. standalone, there is much to unpack.

Convergence

In 2021, one had to choose between reverse ETL (composable) or CDP. Today, that choice is not clear-cut. Many CDPs and marketing technologies can query a database.

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For example, Lytics, ActionIQ, mParticle, Blueshift and others have made strides toward connecting natively to a client data warehouse and the valuable data that lives in it. One can effectively practice composable with some CDPs previously considered packaged.

Implementation

It sounds simple — slap a reverse ETL over an existing data warehouse. Yes, “composable” may be easier to implement. Time to value is typically faster if you have the following:

  • All key data streams easily accessible in your data warehouse.
  • Identity resolution strategy worked out.
  • An engaged analytics or enterprise data team.

Thus, a composable CDP pushes dependencies to the client data warehouse. A CDP may provide comparable or superior time to value if you do not meet the above criteria. For example, an identity resolution strategy is established during onboarding with many packaged CDPs. 

Additionally, common connectors for email platforms and other martech may provide the client with datasets it hadn’t previously stored. This new data and the identity resolution strategy give many clients a “customer 360” as a value-add. 

Dig deeper: Where should a CDP fit in your martech stack?

Composable vs. packaged CDP use cases

The use cases achieved in a composable approach do not fundamentally differ from packaged CDP. There are exceptions — CDPs such as Lytics and BlueConic offer simple site personalization. 

If the data underlying the segment is reliable for marketing purposes and the identity resolution strategy permits activation in a given channel, use cases are limited only by the capabilities of the team using the tool. However, packaged CDPs may have built-in machine learning (ML), reporting and support for real-time that composable practitioners may need to solve for separately.

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Identity resolution

A composable solution will not create identity resolution. Composable architectures rely upon pre-existing join keys, cloud-native identity resolution for disparate data sets or a pre-existing customer table with all relevant segmentation criteria. 

CDPs can work with a pre-existing identity resolution strategy, similar to composable architectures — or they can create an identity resolution strategy for the client as part of their implementation. Often, there is a hybrid approach where a CDP utilizes the client’s pre-existing identity resolution strategy and then maps new channels and data streams into that identity resolution strategy.

Dig deeper: A guide to the strange new world of identity resolution

Segmentation

Many packed CDPs offer no-SQL front ends, and composable reverse ETL solutions have made progress on this front. Likewise, not all CDPs are created equally and some place more technical burden on the end user. 

Some CDPs need to flatten or map data to limit complex joins. This is to limit the dimensionality of the data and provide real-time responses. 

The real-time nature of this architecture may be an advantage to some. However, it places real limitations on the ability to ask complex questions of the data. If real-time is important, packaged CDPs may have an advantage. If complex questions and less onerous data mapping in implementation are critical, composable may work better for you.

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Data governance

Complex legal requirements for consent, data storage, data residency and rights to access/deletion are top of mind for many decision-makers in the composable architecture vs. packaged CDP decision. In this area, composable enjoys an advantage.  

Composable puts the data warehouse at the center of the marketing universe. Cloud data warehouses offer flexible controls for consent and data residency. Composable solutions can work within a pre-existing governance framework, including multi-region support, data expiration and column-level protection.

Packaged CDPs often recreate key aspects of customer data in a CDP-managed environment. This creates process issues for things like GDPR- and CCPA-related requests. They are also forced to work with client-provided consent attributes or integrate with third-party consent platforms. Some CDPs try to mitigate this by installing their CDP “on-prem.” 

Time to value

Time to value varies all too widely by client. As mentioned above, theoretically, time to value is faster with composable if certain organizational criteria are met. If those criteria are not met, the packaged CDP has some structural advantages. 

However, CDPs cannot always claim success. We’ve seen time to value in as little as 30 days and we’ve unfortunately been called in to rescue multi-year efforts with little value provided. Though, if you have a multi-year issue with no success, the issue is probably not the technology as much as your use case strategy, your process to adopt the new technology or lack of skills, availability or continuity in your staff.

Data science and machine learning

The composable approach relies upon an enterprise bringing their own intelligence or a best-of-breed solution to the data set. Many CDPs offer out-of-the-box data science. In our experience, CDP-provided capabilities are limited to the team using the platform. If the team is advanced, they may be able to extract value from data science features. 

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We believe data science should be well-ingrained inside a marketing operation. If your team hasn’t found utility in the ML capabilities they have, you have the wrong team or the wrong process. If your team doesn’t have ML capabilities, work with an expert who can help you modernize your marketing processes.

Dig deeper: Measuring CDP adoption: A comprehensive framework

Key questions to consider before going with composable CDP

The decision to go composable or packaged CDP is highly nuanced. The distinctions overlap and there are specific dependencies of a brand’s data warehouse, complementing technologies (BI, machine learning, etc., etc.) and desired use cases. 

Before deciding on an approach, brands should ask themselves some of the following questions:

  • What use cases am I trying to solve for? Considerations around the deletion of third-party cookies, the need for real-time use cases and connectivity to the existing martech stack must be considered.
  • Do I have all the key data already resident in my data warehouse? For example, do I have my email, website and key data from stores or other owned channels available at a customer level? Can I join these data sets together for a reasonably-reliable customer view already?
  • How mature is my reporting and analytics capability? Can they easily support reporting of the audiences I intend to build, use cases I intend to deploy and ROI associated with these efforts?
  • Do I have the tooling needed to support ML-based decisioning in my audiences?

When we work with companies deploying a CDP, our team has generally made an organizational commitment to deploy first-party data at scale. This inherent commitment has helped the velocity and success of CDP deployments. 

It’s early to tell how reverse ETL solutions will impact first-party customer data deployment at scale. However, the future is bright for rapid time-to-value applications and the ability to allow for data residency and privacy concerns.


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

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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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