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What Marketers Need to Understand About Automated Bid Algorithms

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what marketers need to understand about automated bid algorithms

In conversations with clients, I find myself using a phrase like, “We can do that, but we need to make sure we grease the wheels of the machine first.” What my Industrial-age jargon actually means is, we need to make sure that we’re not forcing the algorithms to have to relearn in a way that will be detrimental to our performance. I’m going to spare you arguments as to why you should be turning to machine learning in your work in digital advertising, if you don’t know already – plenty of folx out there can help you. If you’re finding the move towards automation difficult, take some advice from Lauren Rosner’s blog, Relinquishing Control Or: How I learned to Stop Worrying and Love PPC Automation.

Below are the most useful elements to understand about how algorithms work in your PPC campaigns.

Target Strategies

Target ROAS as a smart bidding strategy is best deployed across campaigns that are NOT budget constrained and have had at least 15 conversions  in the past 30 days. The strategy drives the highest conversion value possible while maintaining a consistent return on ad spend.  This strategy does not work well in a hyper-sgemented account structure. Target CPA functions very similarly, except it optimizes toward a CPA value and does not have a minimum conversion volume.

When using target strategies, it’s important to understand what you’re asking the strategy to do:

If you have an aggressive CPA or ROAS target for your industry, let’s say a 20X ROAS, you are asking the algorithm to limit your risk by heavily limiting the number of auctions you’re willing to participate in. Effectively you’re saying: Don’t bid in an auction, unless you’re pretty darn certain you can get me a 20X return. This will cause the algorithm to become very selective and enter into auctions very cautiously. This can result in a drop in impression share, an increase in CPCs, and a general drop in volume. Ironically, in order for automation to work optimally, it needs to have a lot of data to make decisions. So, it’s important to be REALISTIC when setting your targets.  Here are a few rules to abide by:

  1. If you’re switching to one of these automated bid strategies for the first time. Set the initial targets based on your last 30 days CPA or ROAS.
  2. It takes 7-14 days for the algorithm to recalibrate – so give it time to “settle” and don’t make changes. It will not start to “perform” until it’s learning phase has ended.
  3. Shift your CPA or ROAS targets in 15%-20% increments at a time. For example, if your last 30 Days ROAS was 5X and you want to move toward a 10X goal, first shift to 6X – let it run for a few weeks, Then shift 20% again.. Avoid aggressive shifts in either CPA or ROAS to not rock the boat.
  4. Feed your algorithm by layering in audiences. Set these to “Observation” and make bid adjustments accordingly.

If you’re struggling to scale campaign volume, the limiting factor could be your targets. Try increasing CPA or decreasing ROAS targets in 15-20% increments. This will not necessarily result in worse performance. By lowering your bar, you’re effectively telling the algorithm that you’re not as risk averse, and you’re willing to take some chances while trying to achieve your goal. If your accounts experience seasonality, I highly recommend lowering your ROAS and increasing your CPAs at least 1 week in advance to allow the account to scale during times of high seasonality.

Maximize Strategies

Maximize Conversion Value or Maximize Conversions are budget based strategies and should be utilized on campaigns that are regularly limited by budget or spending most of their daily budgets. The minimum criteria for these strategies is 20 clicks/day. In order for Max conversions to operate effectively, ensure that you’re only tracking important conversion actions or set up conversion action sets (linked to PPC hero article). These strategies perform best with broad match type keywords, which allows for more traffic and gives the strategy more data to learn from.

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When you’re asking for the maximum,  you’re asking the strategy to run out into the auction, and capture as many valuable conversions it can based on the budget provided. It gives no effs about any of your other campaign inputs and does what it wants. For example, these strategies will NOT CONSIDER the following when participating in auctions:

  • Device bid adjustments
  • Location bid adjustments
  • Ad Schedule
  • Audience
  • Age Rage
  • Gender
  • HH Income

These strategies also do not care about CPCs. If you have keywords that have set CPC goals, move these to a different campaign, this is not the strategy for these terms. A common misconception when using these strategies is that you have to start with eCPC, then ramp up to Max Conversions. You DO NOT need to do this. In fact, if you do you will cause the algorithm to have to learn twice and recalibrate. You can start fresh campaigns with Maximize conversion or Maximize conversion value bid strategies.

If you’re uncertain if a bid strategy is working for a particular campaign, look to the Bid Strategy Report. This can be found here:

Hover over the Bid Strategy type in the Bid Strategy Type Column and click on it:

target cpa in the bid strategy column

The Bid Strategy Report will look something like this and will provide info on how to optimize your strategy. For example, recently actual CPAs have been climbing, so I may want to adjust my target to make sure I can capture more volume.

bid strategy report google ads

Even though it’s hard, you have to pad in learning periods for your smart bidding strategies. The general recommendation is 7-14 days with no changes before you start to get meaningful shifts in performance. One of the biggest pitfalls for marketers is pre-emptively calling a strategy ineffective prior to the end of the learning phase. Plan ahead, ensure the expectations are understood up front and when in doubt, turn to your Bid Strategy Report to see how you should adjust.

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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.”

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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.”

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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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