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3 ways to avoid email automation breakdowns

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Back in February, I used a vacation-rental site to book a beautiful house on the water for my wife and me and a group of friends. We read the reviews, scrutinized listings and picked the winner. 

The booking process went well, but if you have ever booked a site without seeing it in person, you might feel a little anxious after clicking “Book now.” 

Will this place that just ate a big chunk of my credit limit turn out as advertised? Will it live up to the photos in the listing, or is it really a shack on a main road with some stranger’s shoes on the deck?

One day after I booked, I got an email. “We saved your search so you don’t miss out on your dream vacation,” it said.

Yes, folks, I freaked out. 

Attack of the browse-abandon emails

I booked the place, so why was I getting this browse-abandon email? My card didn’t go through? The owner refused my booking? My reservation got lost? Somebody else booked it a second before I did? 

Over the next four months, I looked at the property 22 times, and I received 22 follow-up emails. I know exactly how many because I saved them all. 

I won’t mention the site because we all have things in our email programs that we know we need to fix. But there’s an opportunity to learn from it and make sure we’re not making the same mistake.

If you follow me on MarTech, you know how much I love browse-abandon emails (not). But this gave me a whole bunch of teachable moments, which I have narrowed down to three because I just returned from that beautiful house and am in post-vacation-chill mode:

1. Set up or adjust your exclusions 

Browse-abandon emails make sense in travel and hospitality, because repeatedly viewing locations, hotels and attractions can be strong intent signals. The dollar value is higher, and the travel shopper’s mindset is more considered. 

But…

Your marketing automations must build in exclusions for people who don’t need to receive a follow-up message, even if they meet some of the triggering criteria, like a site visit. Yes, they are still showing intent, but has that intent grown or has it waned? Do you need to change the message?

Read next: 8 major email marketing mistakes and how to avoid them

You don’t have to include everyone who meets your criteria in your browse-abandon sequence. Think deeper. When should you not send a standard browse-abandon email to someone who visited a site but left without clicking the “Book now” button? When should you change the message? (See the next item.)

Exclusions are just as important as inclusions in marketing automation. If you have no exclusions, you’re doing it wrong. In fact, I can’t think of a single automation that wouldn’t have at least one exclusion.

2. Vary the messages

Remember those 22 browse-abandon emails? They were all pretty much the same. Same headline, same copy, even the same recommended properties. It reflects badly on the brand. 

If you have an automation that executes every time an event happens, you can’t send the same message every time. Set limits. How many emails with the same message is too many? 

You need a good reason to operate an open-ended automation. This particular automation did not have a “converted” exclusion. You also should set a limit – say 10 follow-up emails, and that might even be too much. If I view a property 10 times but your data shows I still haven’t booked, it’s time to change the message.

Escalate me to customer service. Ask if I need help. Give me a number to call. Give me a chance to say I’m not interested any longer or I booked the property. 

It’s all about meeting your customers where they are, based on intent and what you know about them.

3. Resolve your data disconnects 

You’re probably thinking, “Ryan, their order system isn’t connected to their email marketing system.” I understand that disconnect in data. But when your program is so disconnected that it’s not effective, you should limit the impact.

If your browse-abandon email isn’t hooked up to your order history, then you should limit your email series to three or four and test to find out at what point extra emails become annoying. 

This limits the damage to your brand experience and forces you to solve the problem.

Even a manual intervention could have helped. You can implement a range of temporary fixes while you formally integrate the data.


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We don’t always have all the data we need to be successful. But you have three choices here:

  • Create a good enough automation even though you lack data.
  • Wait until you have all the data you need so you can perfect the automation.
  • Launch an email that doesn’t hurt the brand experience but still helps you accomplish a goal (the agile solution).

We all have data problems. But our job is to make do with what we have and not tick off people who just spent a gobsmack of money on our products or services.

Wrapping up

Marketing automation is incredibly powerful. I know companies that make 50% of their email revenue on automated and transactional emails on 4% to 5% of their overall volume.

But with that power comes the responsibility to build programs that enhance customer intent, grow that intent and convert that intent — and, when the customer converts, to recognize that conversion.

This intent gap signals us to review our marketing automations and find out what’s in the data, what’s missing, what’s excluded and what’s included. If your marketing automation just sends an email every time an event happens, that’s not automation, that’s just repetition.

The smart play is to make sure your automations have the right data, you have considered your inclusions and exclusions, varied your messages and limited the impact. 

Sometimes you can send too many emails. Just because you have an event on your website, that doesn’t mean you have to send the email associated with it.

Or, as my friend David Baker says, sometimes email doesn’t work. In my situation, the email the company sent me — and sent me, and sent me — didn’t work. But it serves one useful purpose.

It gave me the opportunity to see where we need to improve our programs and look critically at how we execute marketing automations. 

(Drop microphone, walk off stage. Walk back on, pick up mic.)

If you think I’m writing about your company, print out my column, send it to your team and say, “Ryan just wrote about us! Here’s why we need to fix our automations.” Not that I’m so important, but seeing your problem described publicly on MarTech.org might motivate your team to act. Then write to me and let me know how it went.


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


About The Author

As the co-founder of RPEOrigin.com, Ryan Phelan’s two decades of global marketing leadership has resulted in innovative strategies for high-growth SaaS and Fortune 250 companies. His experience and history in digital marketing have shaped his perspective on creating innovative orchestrations of data, technology and customer activation for Adestra, Acxiom, Responsys, Sears & Kmart, BlueHornet and infoUSA. Working with peers to advance digital marketing and mentoring young marketers and entrepreneurs are two of Ryan’s passions. Ryan is the Chairman Emeritus of the Email Experience Council Advisory Board and a member of numerous business community groups. He is also an in-demand keynote speaker and thought leader on digital marketing.

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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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