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SMX Overtime: When to use PPC automation (and when not to)

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Aaron Levy1 SMX East

Account structures, cross channel attribution, smart bidding and automation reporting were among the many questions during SMX East’s roundtable, “How automation really works and when to use it.” I wanted to take the time to answer a few additional questions from attendees after the event.

I understand it depends on the business, but how do you go about structuring your accounts? How do you set up campaigns?

I wrote a post about three years ago about decision trees for SEM segmentation that, for the most part, still outlines how we structure our accounts.

For each major element (audience or keyword for search, different audience types for YouTube) ask yourself two simple questions. Will performance be materially different? Does messaging need to be materially different? If the answer to either is yes you can assume that segmenting targeting elements is a good idea.

What’s changed over the years is the priority. When the post above was written, there was no punishment for over-segmentation and no upside for campaign consolidation. In modern search, I recommend keeping as much data together as possible. Yes, you can still optimize a group of campaigns together using a portfolio, but it’s best to keep similar performing ad groups/keywords together unless there’s a notable case to be made for segmenting.

Addressing a few common questions. Yes, we do still segment campaigns by match types but have tested moving them together – the results were neutral. No, I don’t recommend SKAG’s – there’s no point anymore. Yes, segmentation by device is okay, but I wouldn’t call it a “default.” Generally speaking the controls in place to bid or message by device is sufficient to control budget.

How can you use smart bidding when your CPA/ROAS goals change frequently (biweekly/monthly)?

First of all, I wouldn’t recommend see-sawing goals unless there’s an explicit reason. If there’s dramatic inventory swings or a lead at the end of the month worth more than one at the beginning, I’ll allow it. Otherwise, stay stable.

With that out of the way, it depends on how dramatic the goal shifts are and/or if they return to where they were. If the swings aren’t particularly dramatic (e.g. a CPA moves from $100 to $90) you should be totally fine nudging your bid targets without resetting the learning period. Usually, a shift of 5 to 10% won’t disrupt the system too much.

If you’re attacking big sale days, for example, seasonality adjustments will effectively “coach” the bidder and tell it to expect higher or lower swings.

How much time do you save with automation on a weekly basis? Are Supermetrics the go-to with automating reporting?

Tough to say as to how much time it saves since we inherently wind up “reinvesting” that time in our clients! You can look at it on a task-by-task basis to give you an idea of time savings:

  • My teams probably spent five to eight hours per month doing search queries. Since we’ve started (mostly) automating via n-gram scripts, that’s down to one or two hours.
  • Manual bidding? That’s AT LEAST an hour or two a week, now bid optimization or analysis is down to an hour a month.
  • The idea of pulling manual reports sends shivers down my spine. Even when we had a platform or “human automation,” the reports would still take about two hours per week per client. Now we’ve managed to automate QA (yep – it’s possible) which takes that time down to a half an hour or so.

With regard to reporting, it largely depends on your needs. We license Tableau for clients that warrant it which is a huge help to automate things like pacing etc. But, as the asker indicates, much, if not all, of that can be done via Supermetrics. We often use Supermetrics to parse additional data into Data Studio for cohesive reports. Just make sure to have an alerts system for when a query fails or times out.

How do you use cross channel attribution data for your bidding and optimization?

There are a few options here. Bear in mind that attribution data usually has a somewhat significant delay, or at the very least isn’t real-time.

  • Use your cross channel data to build in a “discount” factor for bidding. Say Google reports 100 conversions a month, but your attribution tool indicates only 60 of them were incremental. Aim your goals to somewhere in the middle, perhaps lower your CPA target (or increase ROAS target) by ~20%.
  • Feed data back into UI’s as a separate column – I wouldn’t recommend using offline data for real-time bidding (too many things can go wrong), but having the data readily available can make decisions easier
  • Use Google Analytics conversions in lieu of the Google Ads pixel – there are tradeoffs here that I won’t go into, but it is a simple way to ensure that the broader marketing picture is considered in Ads

Can you go around the data limitation for smart bidding by adding micro-conversions along the purchasing funnel with fixed values?

Absolutely! Going higher in the funnel has worked well for a number of our clients. Similar to the attribution challenge noted above, use these micro-conversions as directional data. Check your assumptions often to ensure that you’re not putting too much weight in a certain area. Additionally, make sure whatever actions you choose are unique. Don’t add weight to both time on site and page views (they’re one and the same) and don’t double up on different information capture points.

Will we ever be automated out of our jobs?

No. Though I suppose it depends on your job.

Look at it this way, humans are tremendous thinkers and strategists, but horrible automatons and calculators. The parts of our jobs that will be automated away is highly manual. It’s my (and many others’) outspoken opinion that there’s no reason to bid manually under the guise of control. We’re romanticizing the past, looking back at times when we were hand-picking what we thought were the most important signals (keywords, bids, and copy) while tuning out human signals like demographics, behavior and technological capacity.

The future is showing a job shift rather than a job replacement. Our job won’t be to pull every knob and push every button – it will be making sure the machines are working right. A key trait in paid search success comes from looking under the hood, understanding how the automation works and how to use it to its full potential.

Keywords are going to go away someday. I don’t know when that day is, but it will happen. Those who take the time to understand system quirks and features will win. Those who try to shoehorn their ways into the old way of thinking into the future will be automated out of a job.


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


About The Author

Aaron has been in the industry for the better part of a decade, leading paid media campaigns with clients ranging from Fortune 50 companies to startups and local businesses. He’s the Group Director of SEM at Tinuiti, a full-service digital agency with offices across the US. Aaron’s role is to support a growing SEM team across the US, looking years ahead so his team can look days ahead. In addition to his day to day, Aaron’s a frequent industry speaker and instructor at Drexel and University of Vermont, working to grow the next generation of great marketers. He moonlights as a brewer, hockey player, slow cyclist and claims to be the industry’s top chef.

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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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