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Should you use your data warehouse as your CDP?

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Should you use your data warehouse as your CDP?

The advent of cloud-based data warehouses (DWHs) has brought simpler deployment, greater scale and better performance to a growing set of data-driven use cases. DWHs have become more prevalent in enterprise tech stacks, including martech stacks. 

Inevitably, this begs the question: should you employ your existing DWH as a customer data platform (CDP)? After all, when you re-use an existing component in your stack, you can save resources and avoid new risks.

But the story isn’t so simple, and multiple potential design patterns await. Ultimately, there’s a case for and against using your DWH as a CDP. Let’s dig deeper.

DWH as a CDP may not be right for you

There are several inherent problems with using a DWH as a CDP. The first is obvious: not all organizations have a DWH in place. Sometimes, an enterprise DWH team does not have the time or resources to support customer-centered use cases. Other enterprises effectively deploy a CDP as a quasi-data warehouse. (Not all CDPs can do this, but you get the point.)

Let’s say you have most or all your customer data in a DWH. The problem for many, if not most, enterprises is that the data isn’t accessible in a marketer-friendly way. Typically, an enterprise DWH is constructed to support analytics use cases, not activation use cases. This affects how the data is labeled, managed, related and governed internally. 

Recall that a DWH is essentially for storage and computing, which means data is stored in database tables with column names as attributes. You then write complex SQL statements to access that data. It’s unrealistic for your marketers to remember table and column names before they can create segments for activation. Or in other words, DWHs typically don’t support marketer self-service as most CDPs do. 

This also touches on a broader structural issue. DWHs aren’t typically designed to support real-time marketing use cases that many CDPs target. It can perform quick calculations, and you can schedule ingestion and processing to transpire at frequent intervals, but it is still not real-time. Similarly, with some exceptions, a DWH doesn’t want to act off raw data, whereas marketers often want to employ raw data (typically events) to trigger certain activations.

Finally, remember that data and the ability to access it don’t maketh a CDP. Most CDPs offer some subset of additional capabilities you won’t find in a DWH, such as:

  • Event subsystem with triggering.
  • Anonymous identity resolution.
  • Marketer-friendly interface for segmentation.
  • Segment activation profiles with connectors.
  • Potentially testing, personalization and recommendation services.

A DWH alone will not provide these capabilities, so you will need to source these elsewhere. Of course, DWH vendors have sizable partner marketplaces. You can find many alternatives, but they’re not native and will require integration and support effort. 

Not surprisingly, then, there’s a lot of chatter about “composable CDPs” and the potential role of a DWH in that context. I’ve argued previously that composability is a spectrum, and you start losing benefits beyond a certain point. 

Having issued all these caveats, a DWH can play a role as part of a customer data stack, including:

  • Doing away with a CDP by activating directly from the DWH. 
  • Using the DWH as a quasi-CDP with a reverse ETL platform.
  • Coexisting with a CDP.

Let’s look at these three design patterns.

1. Connecting marketing platforms directly to your DWH

This is perhaps the most extreme case I critiqued above, but some enterprises have made this work, especially in the pre-CDP era and platforms (like Snowflake with its broad ecosystem) are looking to try to solve this.

The idea here is that your engagement platform directly connects to push-pull data with a DWH. Many mature email and marketing automation platforms are natively wired to do this, albeit typically via batch push. Your marketers then use the messaging platform to create segments and send messages to those segments in the case of outbound marketing.

Marketing platforms directly ingesting from DWH

Imagine you had another marketing or engagement platform, a personalized website or ecommerce platform. Again you draw data from DWH, then employ the web application platform to create another set of segments for more targeted engagement.

Do you see the problem yet? There are two sets of segmentation interfaces already. What happens if you had 10 marketing platforms? 20? You will keep creating segments everywhere, so your omnichannel promise disappears. 

Finally, what if you had to add another marketing platform that did not support direct ingestion from a DWH?

This approach solves several problems with the first pattern above. Notably, it allows (in theory) a non-DWH specialist to create universal segments virtually atop the DWH and activate multiple platforms. With transformation and a better connector framework, you can apply different label mappings and marketer-friendly data structures to different endpoints.

Here’s how it works. Reverse ETL platforms pull data from the DWH and send it to marketing platforms after any transformation. You can perform multiple transformations and send that data to several destinations simultaneously. You can even automate it and have exports run regularly at a predefined schedule.

Reverse-ETL tools can act as an intermediary layer for modeling and activation
Reverse-ETL tools can act as an intermediary layer for modeling and activation

But a copy of that data (or a subset of it) is actually copied over to target platforms, so you really don’t have just a single copy of data. Since the reverse-ETL platform does not have a copy of data, your required segments or audiences are always generated at query time (typically in batches). Then you export them over to destinations.

This is not a suitable approach if you want to have real-time triggers or always-on campaigns based on events. Sure, you can automate your exports at high frequency, but that’s not real-time. As you increase your export frequency, your costs will exponentially increase.

Also, while reverse-ETL tools provide a segmentation interface, they tend to be more technical and DataOps-focused rather than MOps-focused. Before declaring this a “business-friendly” solution suitable for marketer self-service, you must test it carefully.

3. DWH co-exists with CDP

Your enterprise DWH serves as a customer data infrastructure layer that supplies data to your CDP (among other endpoints). Many, if not most, CDPs now offer some capabilities to sync from DWH platforms, notably Snowflake.

CDP and DWH can co-exist
CDP and DWH can co-exist

There are variations in how these CDPs can co-exist with DWH. Most CDPs sync and duplicate data into their repository, whereas others (including reverse-ETL vendors) don’t make a copy. However, there could be trade-offs you need to consider before finalizing what works for you.

In general, we tend to see larger enterprises preferring this design pattern, albeit with wide variance around where such critical services as customer identity resolution ultimately reside. 

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

Wrap-up

DWH platforms play increasingly essential roles in martech stacks. However, you continue to have multiple architectural choices about which services you render within your data ecosystem.

I think it’s premature to rule out CDPs in your future. Each pattern has its trade-offs to keep in mind while evaluating your options. 


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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.”

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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