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24 questions to ask identity resolution vendors during a demo

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24 questions to ask identity resolution vendors during a demo

An identity resolution platform can be a key tool to enable brand marketers to understand with confidence who their customers are and how to comply with the increasing patchwork of consumer privacy regulations. But how do you decide which platform is the right one for your organization?

For starters, once you have determined that enterprise identity resolution software makes sense for your business, spend time researching individual vendors and their capabilities by doing the following:

  1. Create and prioritize your list of identity resolution use cases, from essential to not necessary.
  2. Take your list of use cases and then do some research. Many of the vendors profiled in this report also provide blogs, e-books and interactive tools that can help.
  3. Make a list of the vendors that meet your criteria. Reach out to them and set a deadline for replies.
  4. Decide whether or not you need to engage in a formal RFI/RFP process. This is an individual preference, however, so be sure to give the same criteria to each vendor to facilitate comparison.

Explore platform capabilities from vendors like Acxiom, Experian, Infutor, Merkle and more in the full MarTech Intelligence Report on identity resolution platforms.

Click here to download!


Once you have moved beyond those steps, begin reaching out for demonstrations. You want to set up demos within a relatively short timeframe of each other to help make relevant comparisons. Make sure that all potential internal users are on the demo call and pay attention to how easy the platform is to use, whether the vendor seems to understand our business and marketing needs, and whether they are showing your “must-have” features?

To further help you out, here is a list of 24 questions you can ask:

  1. Does the platform support first-party data onboarding?
  2. Can we incorporate any of our private customer IDs into the platform?
  3. Do you use a probabilistic, deterministic or hybrid approach to matching?
  4. How do you validate the accuracy of your deterministic matches?
  5. What match rate can we expect, given our vertical market and database size?
  6. How do you comply with privacy regulations and consumer choice?
  7. Do you own or license your referential identity data?
  8. What are your identity data sources?
  9. How do you validate the quality of your identity graph?
  10. How much of your data is addressable?
  11. How is your identity graph linked to offline PII?
  12. Do your identity capabilities apply to non-U.S. markets?
  13. How does the platform integrate with martech platforms (i.e., CRMsDSPsCDPs)?
  14. Does the platform feature any built-in data activation capabilities (i.e., personalized email or ad campaign execution)?
  15. Do you have APIs available for data import/export?
  16. What reporting do you provide that will document the ROI from our identity efforts?
  17. What kind of customer support is included? Can we pick up the phone to report problems?
  18. Will we have a dedicated account manager and technical support?
  19. Do you offer a proof-of-concept to measure potential performance and scale?
  20. Do you provide a self-service option in which we can manage identity data?
  21. What kind of professional services are available? And how much do they cost?
  22. How does the company handle requests for product modifications?
  23. What new features are you considering?
  24. What are the long-term roadmap and launch dates?

Good luck!


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Identity resolution platforms: A snapshot

What it is. Identity resolution is the science of connecting the growing volume of consumer identifiers to one individual as he or she interacts across channels and devices.

What the tools do. Identity resolution technology connects those identifiers to one individual. It draws this valuable data from the various channels and devices customers interact with, such as connected speakers, home management solutions, smart TVs, and wearable devices. It’s an important tool as the number of devices connected to IP networks is expected to climb to more than three times the global population by 2023, according to the Cisco Annual Internet Report.

Why it’s hot now. More people expect relevant brand experiences across each stage of their buying journeys. One-size-fits-all marketing doesn’t work; buyers know what information sellers should have and how they should use it. Also, inaccurate targeting wastes campaign spending and fails to generate results.

This is why investment in identity resolution programs is growing among brand marketers. These technologies also ensure their activities stay in line with privacy regulations.

Why we care. The most successful digital marketing strategies rely on knowing your potential customer. Knowing what they’re interested in, what they’ve purchased before — even what demographic group they belong to — is essential.

Read next: What is identity resolution and how are platforms adapting to privacy changes?


About The Author

20 questions to ask digital asset management platform vendors during

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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