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The new identity landscape: A marketer’s guide

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The new identity landscape: A marketer's guide

The perfect storm has been brewing around digital identity for some time.

We’ve got Google’s ever-impending deprecation of third-party cookies set to take effect in 2023; Apple’s decision to phase out its mobile identifier for Advertisers (IDFA) to track users for targeting, personalization and attribution; and most recently, Google’s announcement that they are planning to follow Apple’s lead and pull the plug on targeting across Android devices.

Those changes, paired with new state-by-state legislation for consumer privacy, force advertisers to rethink almost everything they know about digital marketing.

What do these changes mean to you?

So, what do all these changes mean to you, the marketer? Well, let’s look at the numbers. According to current data from StatCounter.com, Chrome makes up around 65% of the total share of consumer browsing, followed by Safari at roughly 19%. Together, that makes up almost 85% of browser usage that will all but go dark for everything from audience building and retargeting to personalization and multi-touch attribution. According to mobile analytics company Flurry, the stats are now equally challenging for mobile ad targeting, with only 18% of Apple users opting in for app-level tracking.

Fortunately, the adtech wagons are circling and peddling fast and coming to market with a host of privacy-compliant identifiers allowing marketers to target prospects, personalize ads and conduct measurement studies. This new identity landscape is changing daily, with newcomers, consolidations and integrations happening everywhere. For the marketer who wants a birds-eye view of the leading players, I’ve laid out who they are and how they are building their identity graphs.

The new identity landscape A marketers guide

Graph Key:

  • PII-based/authenticated: Large database of personally identifiable information to construct person-based IDs and identity graphs.
  • Probabilistic/inferred: A small truth-set of data used to build audiences with a probability of being accurate.
  • Connected TV: A CTV ID allows advertisers to work strictly within CTV walled garden to create, customize, activate and measure audience performance.
  • CDP/EDP: Platform IDs provide identity resolution tools to collect and organize first, second and third-party data from multiple sources.
  • APP SDK: Captures app registration browser data used to identify and match users to one or multiple devices.
  • Hashed Email: Registered emails are anonymized and these Hashed Emails (HEM) IDs are designed to act as a universal match-key for targeted adverting.

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The challenge with a siloed identity landscape is that many proprietary identifiers work well within their own environments but face challenges when connecting to other identifiers for activation or measurement. This gap in the advertising supply chain of identifiers has led to a new crop of identity players developing interoperable IDs with the promise to serve as the translation layer to bring together and unify disparate identifiers that a marketer may be used for targeting, personalization and measurement. While Unified ID 2.0 has taken the lead position in this race, the jury is still out on how well it and other connected IDs will put you, the marketer, back in the driver’s seat. 

While the identity landscape is bound to keep changing in the coming year, Marketers can get their houses in order and be ready for future changes. The below checklist outlines the top identity-centric moves to consider in 2022. 

Get acquainted with clean rooms

Clean rooms were launched as a secure data hub where marketers can store their data and create second-party audiences with cross-over brands or publishers. Clean rooms have grown into a more extensive and robust set of tools for brands and agencies to maintain privacy and compliance while housing and unifying multiple data types. Clean rooms are also evolving to leverage their ability to join datasets and create various input/output integrations to power end-to-end marketing. This can include all applications in the supply chain, from segmentation, activation, measurement and overlap analysis to reach and frequency analytics and consumer journey analysis. 

As the clean room market has grown, differentiation between the types and functionality of Clean Rooms is emerging. 

  • Neutral providers: The landscape has grown, and many specialties of pure-play providers are in the space, such as LiveRamp and Infosum.
  • Walled garden: Amazon Marketing Cloud, Facebook Advanced Analytics; while you can enrich your own first-party data within their walls, they are not interoperable and require extra data science support to analyze results.
  • Inside platform: Cloud storage businesses such as Snowflake and some other marketing companies like Epsilon are also offering clean room services as part of their larger technology stack.

Across the above buckets, the functionality of Clean Rooms is growing as well. Publishers are soliciting the help of clean rooms to empower marketers to connect their first-party data to impression logs, audience segments and user attributes to deliver more prosperous, more actionable consumer insights. 

Additionally, clean rooms have stepped into the customer journey analysis game. They are giving comprehensive and accurate data about their consumer’s interests and behaviors while not revealing personally identifiable information from tapping into publisher data to deliver better experiences for consumers and more effective campaign performance.

Read next: Why we care about data clean rooms

Build or license a consent management platform

While privacy is at the root of this shift in overall identity management, many marketers may still be exposed to risk as more states follow California’s CCPA/CPRA regulations and require all marketers to get explicit consent for targeted marketing. While outsourcing this to a consent management platform (CMP) may be one route, you can follow the federal and various state guidelines to ensure you have the proper notice in place. At a high level, these include the one-click ability to opt out of targeting marketing, a clear statement whether data is sold, the option to give permission to share data, and a data ethics policy.

Beyond the new legislation, a consent management strategy is about building trust with your customers, and at the root of that is giving them transparency and choice. CMPs empower marketers with safety protocols to ensure accurate data and consent are a part of every customer record. Consent solutions enable customers to see and control the data you have collected. Marketers can use these tools to show their customers that privacy matters. Transparency is crucial and builds even more trust with your customer base.  

Manage your data in a CDP/EDP

Another way to get a jump start on the changing identity landscape is to standardize your customer data and unify all your complex customer journeys to simplify personalization, increase customer engagement and manage customer lifetime value. A CDP will unify offline and online customer touchpoints, stitching together actionable customer profiles and activating data across relevant content and audiences. 

Some marketers may need to look beyond CDPs and utilize Enterprise Data Platforms (EDPs) for a more robust solution. Unlike CDPs, EDPs offer more robust features, including real-time APIs from Facebook and Amazon to Google and TikTok, to name a few. An EDP’s real-time data streaming feature offers first-party data tagging, a proprietary identity graph and a data backbone for customer data enrichment and audience modeling.

Looking ahead to the impending changes in identity, marketers can take comfort knowing that while the big privacy reset seems chaotic now, a host of new tools is afoot to help us all navigate a post cookie world. And while much of the changes are out of our control, there are things marketers can do now to ease into the transition and make sure we don’t lose track of our most important asset, our customer data and the relationships it allows us to build.


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


About The Author

The new identity landscape A marketers guideThe new identity landscape A marketers guide

A leader in the data-driven AdTech space that spans 20 years across both the US and the EU. Ken Zachmann’s worked on the ground floor of a data start-up that yielded an eight-figure exit and served as VP and SVP for two leading digital data firms and saw them through to acquisition in 2017.
In 2018 Ken launched his first consulting firm focused on identity-based solutions and helping companies navigate a cookie-less future. Ken’s background in data and identity resolution, paired with his experience of living and working in both the US and Germany, has afforded him a unique understanding of the complexities of sourcing and building data, identity and measurement solutions.

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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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