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CDP vendors and questions to ask them

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CDP vendors and questions to ask them

When it comes time to purchase or upgrade a customer data platform (CDP) for your organization, the demo can be the best way to really understand what a particular vendor offers.

Which CDP vendors should I consider?

First, you have to find the right vendors. Here’s how to do that:

  • Inventory all your databases and martech applications and be sure all stakeholders weigh in on which data and system integrations are priorities. CDPs offer numerous out-of-the-box connectors and APIs to make the integration process faster and more seamless. Prioritizing the applications you want to integrate first, makes it easy to ID vendors with programs that already have native connections to them.
  •  Speak to your marketing peers to find out who is using which CDP vendor and why. (Many of the vendors also provide whitepapers and interactive tools that can help.)
  • Once you know which vendors meet your criteria, send them the list of capabilities you need and set a timeframe for them to reply.
  • Decide whether or not you need to engage in a formal RFI/RFP process. This is an individual preference. However, be sure to give the same list of integrations to each vendor to facilitate comparison.

The next step is setting up demos.

Why should I schedule a CDP demo?

Demos are an opportunity to assess both the systems and the companies behind them. The second part is every bit as important as the first. You want to see who responds well under pressure, who has a fixed way of doing things versus who can adapt to how you need it done.

Schedule all the demos relatively close to each other to help make relevant comparisons. Also, make sure all potential users and stakeholders are on the demo call. Ask them all to keep in mind these questions:

  • How easy is the platform to use? 
  • Does the vendor seem to understand our business and our marketing needs? 
  • Are they showing us our “must-have” features?

Explore platform capabilities from vendors like Blueconic, Tealium, Treasure Data and more in the full MarTech Intelligence Report on customer data platforms.

Click here to download!


Questions to ask the CDP vendors

To help you navigate the demo, here are a few questions to ask each vendor:

  1. How does the platform provide identity resolution? How does it stitch data points together?
  2. How does it handle both structured and unstructured data?
  3. How does it ingest and manage offline data?
  4. How does it monitor integration success and/or failures, and report on data variances or anomalies?
  5. How does it handle connectors and integrations with outside martech systems?
  6. Are your “must have” integrations rock solid?
  7. What is the CDP’s approach to integrating with the specific martech systems that your company uses? Just because a connector exists doesn’t mean it will necessarily work for your organization and how you use that third-party platform.
  8. How does the platform allow users to create customer segments based on behaviors and preferences?
  9. How does it employ machine learning for data analytics, such as predicting customer trends and patterns?
  10. How can we send personalized and targeted messaging from the CDP?
  11. How can we use the CDP to coordinate and track multi-step marketing campaigns?
  12. What data security regulations does the platform comply with?
  13. What data security certifications does the platform have?
  14. Can we pay the software license month-to-month? Or is an annual contract required? Is there a short-term contract or an “out” clause if things don’t work out?
  15. Will there be a price increase when I renew next year — if so, how much? Will the vendor commit to capped increases over a period of years?
  16. What are the additional fees? (i.e., set-up costs, add-on features, API, quotas)?
  17. How long is the onboarding process typically? Will we have a dedicated resource? Who will be the day-to-day contact?
  18. What is the level of support included in the price? What support is additional?
  19. Who pays if your system/team makes an error?
  20. Will our support team work with us to test new features and assess the results?

Our new report, “Customer Data Platforms: A Marketer’s Guide” is now available for free download.


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Customer data platforms: A snapshot

What they are. Customer data platforms, or CDPs, have become more prevalent than ever. These help marketers identify key data points from customers across a variety of platforms, which can help craft cohesive experiences. They are especially hot right now as marketers face increasing pressure to provide a unified experience to customers across many channels. 

Understanding the need. Cisco’s Annual Internet Report found that internet-connected devices are growing at a 10% compound annual growth rate (CAGR) from 2018 to 2023. COVID-19 has only sped up this marketing transformation. Technologies are evolving at a faster rate to connect with customers in an ever-changing world.

Each of these interactions has something important in common: they’re data-rich. Customers are telling brands a little bit about themselves at every touchpoint, which is invaluable data. What’s more, consumers expect companies to use this information to meet their needs.

Why we care. Meeting customer expectations, breaking up these segments, and bringing them together can be demanding for marketers. That’s where CDPs come in. By extracting data from all customer touchpoints — web analytics, CRMs, call analytics, email marketing platforms, and more — brands can overcome the challenges posed by multiple data platforms and use the information to improve customer experiences. 

Dig deeper: What is a CDP and how does it give marketers the coveted ‘single view’ of their customers? 


About the author

Pamela Parker

Pamela Parker is Research Director at Third Door Media’s Content Studio, where she produces MarTech Intelligence Reports and other in-depth content for digital marketers in conjunction with Search Engine Land and MarTech. Prior to taking on this role at TDM, she served as Content Manager, Senior Editor and Executive Features Editor. Parker is a well-respected authority on digital marketing, having reported and written on the subject since its beginning. She’s a former managing editor of ClickZ and has also worked on the business side helping independent publishers monetize their sites at Federated Media Publishing. Parker earned a master’s degree in journalism from Columbia University.

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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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