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Identity and the changing measurement landscape

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Identity and the changing measurement landscape

Marketing is undergoing a monumental shift as third-party cookies are phased out, and the spicket on mobile data is down to a trickle. For most marketers, this ever-changing privacy and identity landscape is leading to seismic shifts in how marketers look at attribution and measurement.

Fortunately, technology companies are investing heavily in alternate identity solutions to be the backbone of future attribution and measurement platforms. In fact, according to the American Marketers Association, over $2 billion is slated to be invested by a host of players to help solve the impending measurement conundrum as cookies and device data are all but phased out.

Identity and measurement bracing for impact

The real impact won’t be felt until 2023 when Google starts phasing out cookies. But, even now, changes led by Apple across both devices and Safari are putting marketers on their back foot as they scramble to decipher attribution and conduct effective measurement studies.

Success in this new unchartered world of measurement will rely on identity and measurement companies to work together to understand channel mix contribution better and devise new tools and methods to associate and attribute conversion data accurately. This already daunting challenge is poised to get exponentially more difficult as the channel mix expands, and tracking signals look less like a spoke and wheel and more like a spider’s web.

In this new web-like framework without cookies, marketers will depend heavily on identity and measurement companies to map, ingest and correctly assign credit to all the different modes and channels in a marketer’s advertising arsenal. At the core of all this, marketers, identity providers, and measurement teams are huddling to find news ways and new IDs to identify, track and make sense of every channel’s contribution to a marketers internal and external channel media mix.

Read next: What is identity resolution?

Overcoming identity and measurement obstacles

Right now, that aspect is becoming increasingly opaque with severe limitations for deciphering when a particular person has viewed an ad, let alone assigning the correct attribution by channel. As part of that $2 billion dollar industry investment, though, a whole host of long-standing measurement players and new entries are making headway. 

The Prohaska Group, a New York-based digital advertising consultancy, is leading the charge to create a landscape of attribution and measurement companies. Working with Prohaska, the below graphic is a snapshot of a larger measurement landscape The Prohaska Group will be releasing later this quarter. 

While Technology companies like those listed here are peddling fast to come up with alternate solutions for measurement and attribution, it’s important to note that according to a recent IAB research study, only 34% of marketers are currently delving into and testing new measurement strategies. The fatigue over this ever-changing identity and measurement landscape is real.  

What marketers can do now to get ahead of the changes

Simplify models. To start, marketers should steer clear of creating multiple models for assigning credit to outside channels and a separate model to give credit within their organization. For you, the marketer, this translates to conducting a sort of marketing mix audit to ideally identify and stop buying from external channels if fair credit for that placement can be found internally.

This can be a hard sell if your team, like most, is focused on meeting overall KPIs and accurately assigning internal credit as part of the same goal. No one wants to potentially upset the apple cart, even if it’s not working optimally.

Align teams and channels. Further, it’s critical to align all the teams on the definitions of internal versus external channels. For instance, is there a clear and agreed-upon understanding within your divisions to separate video orders on a desktop from video orders viewed on a mobile device? Successful marketers are working hard across silos to get their divisions aligned and aiming to synchronize reporting to capture the most accurate attribution model possible.  

Another way marketers are taking charge of their measurement strategies is increasing the investment in data. This data-driven mindset helps build more accurate attribution and measurement models and utilizes more consumer data and modeling to create a more complete picture of your converting audiences.

Audience profiling. Moreover, a data-rich approach better equips marketers to assess and assign the lifetime value (LTV) of a consumer better.

This deeper dive into audience profiling and enrichment is proving to deliver more actionable insights on which audience profiles makes one conversion type more valuable from a lifetime value perspective.

Bidding. Data helps accomplish this by taking into consideration short-term and long-term LTV and targeting those customers or prospects appropriately. Further, these insights related to LTV can be used to train bidding algorithms that favor internal attribution over external channels yielding a higher overall LTV, and less waste on media spend.  

By adopting a more standardized, holistic attribution and measurement strategy, marketers can land on best practices that will set them up for even more success as the Identity and Measurement companies bring more sophisticated solutions to market. Until then, marketers who get their marketing mix house in order now will have a running start as we all head into the murky measurement waters of 2023.


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


About The Author

Identity and the changing measurement landscape

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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