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Better reporting can improve email performance

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Whether you think of email as an art, a science or both (as I do), there’s no escaping the reality that email is a numbers game through and through. Knowing which numbers (or metrics) to track will help you assess and continually optimize your email program’s performance and help you find useful solutions to improve it.

But that’s only the beginning. Numbers need context to give them meaning. That’s where effective reporting comes into play.


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Six ways email reporting builds your email program

Although it’s a key component of successful email marketing, email reporting can be a major challenge for many email teams. Reporting often takes a backseat when the email team has one person on it, or the team manages many other channels besides email.

But when you do reporting right, your reports can help you drive success in six ways:

  • Track trends: Reporting, especially when done at several time intervals such as weekly, monthly, yearly, and year-over-year, will help you see where your numbers are trending up or down.
  • Generate insights: Your reports will include your insights, based on these trends, into diagnosing problems and suggesting ways to improve your program or new areas to explore.
  • Budget and plan effectively: Regular reports will help you see if you’re spending your current budget on things that work and highlight areas where you might need to seek additional funding to close gaps or move your program forward.
  • Drive testing: The insights you gain from regular reporting can also highlight areas you want to test. This goes way beyond a one-off subject line or call-to-action testing. Instead, your reporting can guide tests in message content, design, strategy and other high-level areas.
  • Inform leadership: An in-depth, organized report with figures, analysis and insights keeps your leadership clued into your email performance. Your executives will be more likely to read your reports if they know they’ll learn valuable information, not just stare at columns of numbers presented without context.
  • Show email’s value to everyone: Reporting helps everyone from your email team understand how email contributes to the company’s goals and bottom line. When everyone understands this value, you can more easily build a business case for sustained or additional funding.

As I noted, email reporting is often something that marketers leave to the last minute. That doesn’t give you much time to study your numbers, to think about what they mean in the long run, or how you could use them to improve or expand your email program.

Good reporting forces you to evaluate what the numbers tell you, whether you are compiling a presentation for your senior executives, prepping for your weekly team meeting or summing up what you know as a one-person email department.

You’ll delve below the surface, ask questions and look for answers in your data and use what you find to do email better. You’re not simply listing numbers on a page or in a PowerPoint and filing it away.

Email reporting: Three insights into the state of play today

As I’ve learned in working with clients, everyone does email reporting a little differently. Some marketing teams do all the work themselves. Others have dedicated insight, data or business intelligence units that can do their work.

Many email marketers who create their reports themselves have just enough time to look at the high-level numbers they see in their ESP dashboards – the campaign-by-campaign opens, clicks, and (maybe) conversions.

The heart of email reporting is what you do with the numbers – how you analyze them, what insights you draw from them and how you use them to improve your email program.

Two recent surveys primarily of UK email marketers show the wide range of treatments email reporting receives.

1. Time spent on email marketing is increasing slowly

Econsultancy’s 2019 Email Marketing Industry Census found that 35% of email marketers spent more than two hours of their regular workweek on email reporting. Although 7% of marketers said they spent no time on reporting, 29% spent 2-8 hours, while 6% spent more than eight hours.

That continues a slow uptick in time spent on reporting over the last few years. In the 2017 Census, for example, 25% spent more than two hours on reporting.

Is this good news or does it mean reporting is getting harder to do?

Given how many email platforms now include campaign reporting in their dashboards, thus making the numbers easier to access, I’d like to assume that means more marketers are looking at those numbers and using them to compile even basic reports. And a basic report is better than nothing – it’s the first step toward compiling a really useful analysis.

2. Marketers are (somewhat) confident about their reporting ability

According to the Data & Marketing Association’s 2021 Email Tracker, 54% of marketers say their companies have a “leading” or “good” level of sophistication in their ability to analyze performance, a key component of email reporting.

That’s just slightly behind their confidence in creating email strategy (62%) and creative content (58%). Again, 6% of marketers said their companies don’t analyze email performance.

3. Marketers struggle with reporting

We know from our own client work and from anecdotal and survey results that many don’t have time to do the in-depth analysis that a good email report needs.

Easy access to campaign-level metrics like open, click and conversion rates and inbox performance (unsubscribes, bounces, delivery rates and inbox placement where available) gives them enough data to compile a simple performance report but not the deep-dive analysis needed to understand what’s happening and how to use the data to improve their programs.

Having an email system that makes reporting easy should be a factor in choosing an ESP. The ideal setup automates as much of the data compilation as possible. This reduces the drudgery of manually importing data from spreadsheets and creating visuals for presentations.

In the work my consultancy does for our clients, we pull data from ESPs, e-commerce systems, CRMs, Google Analytics and other integrated data providers into an Excel file that feeds into a PowerPoint presentation. As long as we can pull data into a master Excel document, it will propagate automatically in PowerPoint.

Thus we spend less time adding numbers and more time reviewing and analyzing the numbers and coming up with useful insights and trends. This gives you numbers and context you can cite quickly as well as an official document to pass on to senior executives and to archive for our clients’ own research and planning.

Items to include in your email report

What you include will vary by your brand, the kind of email marketing you do, your vertical, your email goals and strategies and other variables. But these sections should be part of most reports:

1. Essential performance data

This includes campaign-level data involving both process metrics (email activity) and business metrics (conversions, number of sales, average basket value, average or median revenue per email and other goal-oriented metrics.

2. Historical data and trends

These indicate trends, ideally week-over-week, month-over-month and year-over-year comparisons. Include data for list growth, campaign metrics, all of your automated email programs and other relevant data.

Besides the usual performance data you can pull from your ESP and other platforms, crunch the numbers in several different ways to give you an expanded view:

  • List growth: Include both your total emailable list (all of the active email addresses in your audience, not including unsubscribers, spam complainers, long-term inactive addresses and others) plus new subscribers, new unsubscribers and your subscriber churn (net growth or loss) during the measurement period.
  • Reach analysis: This reviews the percentage of contacts who acted on at least one email campaign over a set period. It includes both open reach (the percentage who opened at least one email) and click reach (the percentage who clicked at least once in the same time period), but both send and purchase reach can also be used.

3. Insights

Here you extrapolate what the data means. Provide context such as extenuating circumstances, changes in your audience or email strategy, external events that affect performance and other enlightening information.

4. Hypotheses to test

Your email data influences your testing program. So, look at things you could test for, such as frequency, content or segmentation strategy, audiences and more.

5. Questions and concerns

Separate from insights, use this space to highlight issues that the numbers reveal, such as declining performance of a specific email program, increase in inactivity or challenges in acquisition.

6. Recommendations

Ideas for improving performance using both capabilities you have in your current technology or outlining how new technology could help you achieve specific goals.

7. Screenshots of recent email campaigns

These visuals keep people in the loop, like your senior executives who don’t monitor your email campaigns as closely as you and your team do. And – let’s be honest – it lets you show off your best work, too.

Report it now, use it later

The time you put in now on your email reporting gives you an advantage later when the time comes to work on your email budget, revamp your email goals and strategies and even help you find a new email vendor as your program grows.

You’ll be a step ahead because you’ve done the analytics, identified your challenges and opportunities, and answered tough questions. Your reports will give you material to guide your steps and choose the right strategies and tactics that will help you achieve your goals.

My hope here is that if your own email reporting practices could use a tune-up, you’ll use the background information and practical advice I provide here to improve your own email reports. And if you’re proud of your email reports and how you incorporate them into your everyday email practices, I would love to hear about it!


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


About The Author

7 common problems that derail ABn email testing success
Kath Pay is CEO at Holistic Email Marketing and the author of the award-winning Amazon #1 best-seller “Holistic Email Marketing: A practical philosophy to revolutionise your business and delight your customers.”

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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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