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Quora’s Lead Gen Ads Beta: Ready For Round Two

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quoras lead gen ads beta ready for round two

As advertisers, we are usually drawn to new ad platforms and betas, first and foremost, for the cost savings. Entering a newer platform, like Quora, is generally cheaper than a mature platform, like Google. However, in our latest beta test with Quora, we found that there’s another MAJOR reason to test younger ad platforms and betas within those platforms: The personalized attention. 

Why are betas difficult?

You might be wondering why advertisers would want personalized attention when launching a beta test in an ad platform and on top of that, why we would value that as high as generating a low cost/lead. Let me paint a picture of what it’s like to NOT have personalized attention when launching a beta.

Here’s the headline for why betas are hard: Uncertainty is high. Best practices are nowhere to be found. Even a good first test may not be enough to convince stakeholders that there should be future tests.

Imagine you test a new campaign type that’s currently in beta. Your platform reps give you access to the campaign type and that’s it. Maybe there’s a support page on their site somewhere with some basic setup details, but since it’s a beta, there’s very little documentation. You set up the campaign to the best of your knowledge and then you launch. The first few days of the campaign, you’ve spent $50/day and generated ZERO leads. This worries you, naturally, but you know that 1. The campaign might be “learning” on the back-end and 2. Your data (ZERO leads) isn’t statistically significant yet. Any experienced, logical performance marketer would tell you not to change the campaign settings, bids, creative, etc. because your results are not yet statistically sound. In other words, you haven’t run the campaign long enough to know, FOR SURE, that it’s OFFICIALLY not working.

So you wait it out. By the time you’ve hit statistical significance, your campaign has spent $500. Maybe even $1,000 or $5,000, depending on the beta, campaign goals, cost/click, etc. At this point, you’ve generated a few leads, but the cost/lead is still 100% higher than your average paid social campaign. Sure, you learned something from this test, because you can always learn from failed tests. Failing is actually fundamental for growth, so that part isn’t too frustrating.

The frustrating part of this whole situation is that you can’t help but wonder if there were some simple best practices you could have implemented from the start to get good enough results to then get the go-ahead from your boss/team to keep testing.

Were there settings you should have used? More niche targeting you could have implemented? Because you were flying blind during the campaign setup phase, you’re never going to be 100% confident that the results are truly indicative of the beta’s potential. I think it’s rare for a new campaign type or ad platform to hit our goals the first time around, but if you miss your goal by a longshot, it’s now going to be even harder to convince your team that this new beta or new platform is worth continuing to test.

Because what’s usually happening with any team that’s testing a beta ad or new ad platform is that stakeholders are looking at it as a one-time test. It either works or it doesn’t. Whether that’s the best way to view it or not (I believe it’s NOT, but that’s a whole separate topic), that tends to be the trend that I see across companies, especially those with smaller budgets.

Testing Quora Lead Gen Ads

Enter 1:1 attention from an ad platform team.

This is what we got from the Quora team, every step of the way when we tested Quora’s new lead gen ads. Here’s the process we went through:

  1. We were offered access to the beta. This is one of the perks of working with agencies. We have more connections, as an agency, to platforms reps. (Shoutout to JD Prater)
  2. The Quora team initiated a meeting where they asked us a number of questions about our goals for the lead gen campaign.
  3. Following the meeting, our team set up the lead gen ad campaign, based on the details discussed in the group meeting.
  4. Quora’s team asked us to let them know when the campaign was drafted so they could review it for potential optimizations, prior to launch. Note: in my past experience as an account manager, I’ve had to be the one reaching out to reps, prior to launching a beta, to see if they have any pointers. The proactivity from the Quora team in this phase was unlike any other beta experience I’ve had.

Pre-launch Feedback

The Quora team reviewed our lead gen ad campaign the same day that we sent them a notification to let them know it was drafted.

Here’s what the ads looked like before we made updates:

Quora lead gen ad before 1

Here are the initial points of feedback (I’ve left out 1 note on audience targeting because it won’t make sense without the full context of the campaign) :

  • Headline sentence – I recommend considering asking questions on Quora as the data shows they perform better than statements. Maybe flip the question in your description with your current headline, “Is your 2020 digital advertising strategy ready?” Try making the question intriguing and qualifying to pull in the right audience. You can also see Hanapin Marketing 3x in the top left so this will help break that up and give you some text characters back. 
  • The logo is too small – I recommend updating the company logo by eliminating the text and just zooming in on the image.
  • Confirmation headline on the lead form – Consider setting expectations here that people can view the report (the ads were for Hanapin’s 2019-2020 State of PPC report) now and they’ll get an email from you later. Something like, “View the report now and look for it in your inbox soon.”

Here are the ads after we implemented the feedback

quora lead gen ad after 1

We made the changes and launched the beta campaign.

Quora Lead Gen Ads: Campaign Setup Friction

On top of providing great feedback throughout the entire test, the Quora team proved to be quite nimble too. Of the three main areas where we felt that there was friction in the campaign setup process, they were already working on resolving one area. They were clearly getting similar feedback from other advertisers and making the product updates as soon as possible. Here are the points of friction we ran into:

  • Not being able to edit the ads or forms – their team was working on this
  • Ads not being approved due to capitalization. Example: Wouldn’t let us capitalize state in “State of PPC report”
  • Not being able to exclude more than one audience. Given the opportunity, we would have excluded an uploaded list and a remarketing audience.

Outside of those few items the overall technical experience was great. We found it to be easy and intuitive!

Why More Advertisers Are Testing Quora Ads

My team will continue to test Quora Ads, and especially the betas, because:

  • Their team gave lots of helpful feedback along the way, helping our team to trust the results and see the potential.
  • Their team was quick to listen and quick to implement improvements.

We aren’t the only advertisers planning to budget more for Quora, either.

According to Hanapin’s 2019 State of Paid Social, “the number of marketers investing in Quora has grown nearly 4x since last year. The platform has rolled out 5 betas just in the last 8 months (from when State of Paid Social was published in June) and shows no sign of slowing down.”

So if you’ve been waiting to jump into Quora Ads, you really need to stop stalling. Your competitors are starting to test Quora Ads and their team makes it easy to get started. But that’s just my two cents! If you want to procrastinate a bit longer, it will be less competitive for our team and I’m good with that.

Quora’s Lead Gen ads are currently in a closed beta but you can request access by completing this form.

PPChero.com

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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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