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How to get better leads and conversions with Google’s AI

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How to get better leads and conversions with Googles AI

If you’re looking for ways to modernize your PPC optimization, you’ve probably come across value-based bidding (VBB). This technique revolves around teaching AI systems at Google and Microsoft what types of conversions you value most. Together with automated bidding and ad formats like responsive search ads (RSAs), the ad platforms can then prioritize getting you more of the best conversions and significantly improve the results from your ad budget.

VBB can make successful advertisers better, and it can even be a solution for advertisers who’ve tried and failed at PPC because they were unhappy with the quality of the conversions when leads were low quality or buyers made too many returns.

In this article, you will learn how to deploy VBB for three different types of advertisers: pure-play e-commerce, hybrid retail and lead gen.

The principle behind VBB

The idea of value-based bidding is that automated bids should be based on the value the resulting clicks and conversions add to your business. That’s not so different from the idea of bid management in general. But rather than achieving this goal through the manipulation of CPCs or targets like tROAS or tCPA, it’s achieved by teaching the machine the true value of conversions. 

The reason VBB is so important in PPC in 2022 is that automation is now the standard way new campaigns operate and when you give automation bad or incomplete goals, you risk creating a vicious cycle that leads to poor results in those campaigns. 

One problematic scenario is when advertisers give the ad engines an incomplete picture of what their goals are. Is the conversion they’re reporting to Google truly the conversion the CFO of the company cares about, or is it just some intermediate goal that happened to be easier to set up?

It’s similar to a problem you may face with people. When you hire someone for your PPC team, you can only expect them to drive great results if you tell them what results you’re after. If you tell your new teammate to get as many leads on the landing page as possible, don’t be surprised if those leads aren’t all of the most reputable origins.

If, on the other hand, you tell your coworker that the leads on the landing page will go to the sales team and they expect those leads to be well qualified, they will likely change how they go about generating leads and the quality will go up. If you tell them they will be judged not just on the volume of leads but also how many turn into paying customers, results are likely to get even better.

And so it goes with machine learning too. The machine will only do a great job if you teach it what you’re really after!

So let’s look at how you can teach the machines what a conversion really is and which type of conversions are the kind you’d like to get more of.

Optimizing PPC with better conversion data

There are two levels of sophistication when it comes to teaching the machine about the value of your conversions. Let’s start with the more sophisticated and precise method first. For every click or order, we will teach the machine what happened in the weeks after the original conversion event.

For lead gen advertisers:

The most sophisticated method of teaching the ad engines what you value relies on offline conversion imports (OCI), a method that depends on capturing the gclid or msclkid, passing it through your CRM and then feeding it back to the ad engines within 90 days as the value of the ‘conversion’ becomes more clear. 

Recently Google introduced Enhanced Conversions for Leads, a simpler method with many of the same benefits but without the need for storing the click id in your own system.

For retailers:

Ecommerce advertisers don’t need to grab the engine’s click ID but can instead send their own unique order ID with the conversion. As the true value of the sale becomes clear, advertisers can restate values to the ad engine within 55 days. Look up conversion value adjustments to learn how this works.

If you haven’t implemented one of the three methods above, it’s probably not because you weren’t aware of them, but rather because there is a technical limitation within your team that’s made it hard to implement. So let’s look at a new, simpler alternative to optimizing PPC with your conversion data.

It’s called Conversion Value Rules and lets you tell Google more about how to value different conversions based on a common attribute, like location, device or audience. While not as precise as the other methods, it’s a much easier way to teach the machine so it can start to prioritize the types of conversions that matter more to you.

Questions to help determine the true value of conversions

With Conversion Value Rules, advertisers create rules to adjust conversion values based on attributes like location, device, and audience.

When setting Conversion Value Rules, advertisers should focus on elements of a conversion that Google may not be able to observe like lifetime value, average deal size, lead-to-sale conversion rate, returns, etc. Google already knows about conversion rate differences between different locations, but what they may not know is what happens to conversions from different locations after they start to engage with your business.

Let’s look at some example questions to guide yourself to an initial set of Conversion Value Rules.

Conversion Value rule questions for lead gen advertisers:

  • If you generate leads for HVAC installers, do prospects in certain zip codes have bigger houses and spend more on a typical installation?
  • If you generate leads for education, do prospects in cities that are closer to campus tend to stay in the program longer?
  • If you generate leads for plastic surgery, do prospects who read your article about rhinoplasty tend to become repeat customers and have higher lifetime value?

Conversion Value rule questions for pure-play e-commerce advertisers:

  • Do purchases made in a hurry on mobile devices lead to more items being returned for refunds?
  • Do purchases from people who read your blog with tips for runners tend to be more frequent repeat buyers of running shoes from your brand?
  • Do purchases from those who engage with your social media platforms tend to lead to a bigger brand impact when they share their own images of their purchase with their friends?

Additional Conversion value rule questions for hybrid retailers:

Hybrid retailers can ask the same questions as pure-play e-commerce retailers but refine their Conversion Value Rules further with additional questions like these.

  • Are customers in California worth more because it’s the only state with physical stores?
  • Are customers who shared their email address when they shopped in-store worth more because they make fewer returns?

Now that you have an idea of what types of questions to ask to get an idea of conversion signals Google may not be able to detect on its own, it’s time to create rules for your most important traffic segments.

Which segments to score for Conversion Value Rules

The sample questions above can get you thinking about Conversion Value Rules to create, but you may quickly get stuck on deciding for which locations or audiences to answer these questions. That’s where a good PPC management tool like Optmyzr can help. 

Optmyzr’s new tool for Optimizing Conversion Value Rules starts by asking advertisers to rank the typical value for each of the highest volume locations and other segments detected for a site.

The tool also helps solve the challenge of deciding a good value for each rule. It helps with a question like: if a customer from California is worth more than average, exactly how much more valuable are they? The good news is that VBB will work even if your answers are not precise. Just creating a Conversion Value Rule that says a conversion from California is a bit more valuable than typical will help steer the engine’s AI automations in the right direction. It’s like giving it a nudge that says if all else were equal, it should try to get more conversions from California.

To make this scoring process easier, Optmyzr asks advertisers to rank every segment on a scale of 1 to 5. It can be a bit jarring as a data-driven marketer to be asked for a gut-based judgment call, but like Google’s mantra of “don’t let perfect get in the way of good enough,” the beauty is that this type of optimization works well as an iterative process rather than a quest for instant perfection. 

How to get better leads and conversions with Googles AIHow to get better leads and conversions with Googles AI
Rate which attributes correspond to better or worse than average conversions to help build Conversion Value Rules. Screenshot from Optmyzr.com.

After ranking around 30 segments, the tool will have enough data to create an initial batch of Conversion Value Rules which will teach Google’s AI how to get better conversions for your company.

Determining the right Conversion Value Rules

After you’ve thought about the relative value of different conversions for a business, the next step is to translate those insights into rules. Remember Conversion Value Rules can be for a single attribute, like just location, or for combinations of segments, like location + audience, or location + device.

These combinations can be complex to figure out and cumbersome to maintain but Optmyzr’s tools can help with this too. Using the principle of the wisdom of the crowds, it uses scores from you and your team to come up with a sensible set of Conversion Value Rules. For example, an advertiser who values conversions from California a lot and who also sees more value from mobile conversions may see a value adjustment of +20% for that combination.

By setting Conversion Value Rules like this in Google, Smart Bidding strategies like Maximize Conversion Value with an optional tROAS can go to work to find more of the highest quality conversions.

Conclusion

In modern PPC, where bids, ads, and so much more are automated, advertisers can still get an edge over their competitors. This requires taking true-and-tried principles like solid bid management and knowing the new ways to optimize these levers. Value-based bidding is the modern way to improve bidding. And thanks to innovations from Google and Optmyzr that make optimizing Conversion Value Rules easier than ever, better-performing campaigns are now well within any advertiser’s reach. If you’re interested, you can try Optmyzr free for two weeks.


About The Author

How to get better leads and conversions with Googles AIHow to get better leads and conversions with Googles AI

Optmyzr’s PPC management platform provides intelligent optimization suggestions that help advertisers across the world manage their online advertising more effectively. Optmyzr connects with Google Ads, Microsoft Ads, Amazon Ads, Facebook Ads, Google Analytics, Google Merchant Center, Google Sheets, and SA360. The company was founded by former Google AdWords executives. The Optmyzr PPC suite includes over 30 tools to improve Quality Score, manage manual and automated bids, find new keywords, A/B test ads, build new campaigns, manage placements, automate budgets, and automate reports. Optmyzr was named best PPC management software at the 2020 US, UK, and Global Search Awards.

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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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