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Programmatic’s ad targeting and optimization come to digital out-of-home

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Programmatic's ad targeting and optimization come to digital out-of-home

The audience targeting and segmentation of programmatic advertising are now available for out-of-home advertising.

Beeyond Media, a programmatic digital out-of-home (pDOOH) demand-side platform (DSP), has launched a new segmentation tool, Beeyond TrueReach. It lets brands pick from over 900 criteria — like behaviors, locations and demographics — to create audience segments. It also provides up-to-date performance data in order to optimize campaigns on-the-fly.

The DSP is used by brands like Heineken, American Express and others, and, last year, the company expanded its operations from Latin America to the U.S. and Canada.

Why we care. More DOOH inventory is becoming available to advertisers programmatically. With more screens and more premium locations, brands have better opportunities to reach their target audiences and integrate DOOH into multi-channel campaigns.

Dig deeper: Bermuda Tourism launches 3D digital out-of-home campaign in New York

Reducing waste. With better segmentation comes fewer wasted impressions and ad dollars.

“Eliminating wasted impressions in DOOH advertising primarily involves improving the precision of your audience targeting,” said Beeyond Media’s cofounder and CEO Alejandro Donzis. “By implementing these strategies, we can improve the relevance of your ads, increase engagement rates, reduce wasted impressions, and maximize the return on investment (ROI).”

Segments. Here are some examples of segments found in the Beeyond TrueReach tool:

  • Demographic segmentation: Segments are based on characteristics such as age, gender, income level, occupation, and more.
  • Geographic segmentation: Segments are based on geographic locations like cities, neighborhoods, or even specific locations.
  • Behavioral segmentation: Segments are based on specific behaviors like frequent traveling, fitness activities, food preferences, shopping habits, etc.
  • Interest-based segmentation: Segments are based on individual interests, such as sports, technology, fashion, entertainment, etc.
  • Contextual segmentation: Segments are based on specific contexts, such as events, holidays, or seasonal trends.
  • Purchase intent segmentation: Segments are based on individuals who show high purchase intent according to their online and offline behavior.
  • Lifestyle segmentation: Segments are based on lifestyle choices and preferences, such as health-conscious individuals, eco-friendly consumers, or luxury-oriented individuals.

Strategies. To create segments and help brands optimize campaigns, the tool “[leverages] data from various sources to understand your target audience’s behaviors, preferences and demographics,” said Donzis.

Here are some strategies where marketers can use these data insights.

  • Personalized messaging: Create personalized ad content that resonates with the specific preferences and interests of your target audience. Tailoring your message to the individual can significantly increase engagement and reduce the chance of irrelevant impressions.
  • Strategic ad placement: Position your ads in locations where your target audience frequents. You can use location data and traffic patterns to identify these strategic locations.
  • Continuous optimization: Regularly review campaign performance and adjust your targeting parameters based on results. This continual refinement can help improve targeting precision over time.
  • Predictive analysis: Use historical data and analytics to predict future behaviors of your target audience, allowing you to better anticipate and meet their needs with your advertising.

Synthetic population generation. One of the ways the Beeyond Media platform achieves precise targeting while maintaining consumer privacy is by using synthetic population generation, a method used in computational modeling to create a statistically representative population for a given area. 

“This synthetic population mirrors the real-world population in terms of key characteristics like age, gender, income, household size, occupation, etc., but doesn’t correspond to any real individual, thereby ensuring privacy and confidentiality,” said Donzis.

View of Las Vegas through Beeyond Media’s map view. Image: Beeyond Media.
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Engagement and conversions. Marketers can drive a number of engagement and conversion actions using advanced DOOH targeting in their campaigns.

Donzis offered some examples of how brands can engage with DOOH.

  • Brand awareness: This can be measured by metrics such as ad recall, brand recognition, and increased search volume for the brand. For example, after seeing a DOOH ad, viewers may search for the brand online to learn more about it.
  • Website visits: DOOH campaigns can drive traffic to a website. This could be tracked by using unique URLs or QR codes in the ads, and then monitoring the website traffic coming from those specific URLs or QR codes.
  • Social media engagement: Advertisers may encourage viewers to engage with the brand on social media, perhaps by asking them to share a photo with a campaign-specific hashtag, or by promoting a contest or giveaway that requires social media participation.
  • Store visits: For brick-and-mortar businesses, one of the goals of a DOOH campaign might be to drive foot traffic to physical stores. This could be measured using location data from mobile devices or by monitoring sales at the store during and after the campaign.
  • Sales and conversions: Ultimately, many campaigns aim to drive sales, whether that’s online or in a physical store. This could involve tracking coupon redemptions, monitoring sales of the advertised product, or analyzing e-commerce data for increases in online orders.
  • Lead generation: For some businesses, the goal might be to generate leads. This could involve driving viewers to fill out a form on a website, make a phone call, or sign up for a service.
  • Event attendance: For events like concerts, sports or product launches, DOOH ads can drive ticket purchases or event attendance.

“The specific actions will depend on the campaign goals and the nature of the advertised product or service,” Donzis said.


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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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