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8 major email marketing mistakes and how to avoid them

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8 major email marketing mistakes and how to avoid them

As email marketers, we know we need to personalize the messages we send to subscribers and customers. I can’t think of a single statistic, case study or survey claiming an email program of one-to-everyone campaigns outperforms personalization.

Instead, you’ll find statistics like these:

  • 72% of customers will engage only with personalized messages (Wunderkind Audiences, formerly SmarterHQ)
  • 70% of consumers say that how well a company understands their individual needs affects their loyalty (Salesforce)
  • 71% of customers are frustrated by impersonal shopping experiences (Segment)

But what marketers often don’t understand, especially if they’re new to personalization, is that personalization is not an end in itself. Your objective is not to personalize your email campaigns and lifecycle messages. 

Rather, your objective is to enhance your customer’s experience with your brand. Personalization is one method that can do that, but it’s more than just another tactic. 

It is both an art and a science. The science is having the data and automations to create personalized, one-to-one messages at scale. The art is knowing when and how to use it.

We run into trouble when we think of personalization as the goal instead of the means to achieve a goal. In my work consulting with marketers for both business and consumer brands, I find this misunderstanding leads to eight major marketing mistakes – any of which can prevent you from realizing the immense benefits of personalization.

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Mistake #1. Operating without an overall personalization strategy

I see this all too often: marketers find themselves overwhelmed by all the choices they face: 

  • Which personalization technologies to use
  • What to do with all the data they have
  • How to use their data and technology effectively
  • Whether their personalization efforts are paying off

This stems from jumping headfirst into personalization without thinking about how to use it to meet customers’ needs or help them solve problems. 

To avoid being overwhelmed with the mechanics of personalization, follow this three-step process:

  • Start small. If you aren’t using personalization now, don’t try to set up a full-fledged program right away. Instead, look for quick wins – small areas where you can use basic personalized data to begin creating one-to-one messages. That will get you into the swing of things quickly, without significant investment in time and money. Adding personal data to the body of an email is about as basic as you’ll get, but it can be a start.
  • Test each tactic. See whether that new tactic helps or hurts your work toward your goal. Does adding personal data to each message correlate with higher clicks to your landing page, more conversion or whatever success metric you have chosen?
  • Optimize and move on. Use your testing results to improve each tactic. Then, take what you learned to select and add another personalization tactic, such as adding a module of dynamic content to a broadcast (one to everyone) campaign. 

Mistake #2. Not using both overt and covert personalization

Up to now, you might have thought of in specific terms: personalized subject lines, data reflecting specific actions in the email copy, triggered messages that launch when a customer’s behavior matches your automation settings and other “overt” (or visible) personalization tactics.

“Covert” personalization also employs customer preference or behavior data but doesn’t draw attention to it. Instead of sending an abandoned-browse message that says “We noticed you were viewing this item on our website,” you could add a content module in your next campaign that features those browsed items as recommended purchases, without calling attention to their behavior. It’s a great tactic to use to avoid being seen as creepy.

Think back to my opening statement that personalization is both an art and a science. Here, the art of personalization is knowing when to use overt personalization – purchase and shipping confirmations come to mind – and when you want to take a more covert route. 

Mistake #3. Not maximizing lifecycle automations

Lifecycle automations such as onboarding/first-purchase programs, win-back and reactivation campaigns and other programs tied to the customer lifecycle are innately personalized. 

The copy will be highly personal and the timing spot-on because they are based on customer actions (opting in, purchases, downloads) or inactions (not opening emails, not buying for the first time or showing signs of lapsing after purchasing). 

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Better yet, these emails launch automatically – you don’t have to create, schedule or send any of these emails because your marketing automation platform does that for you after you set it up. 

You squander these opportunities if you don’t do everything you can to understand your customer lifecycle and then create automated messaging that reaches out to your customers at these crucial points. This can cost you the customers you worked so hard to acquire, along with their revenue potential.

Mistake #4. Not testing effectively or for long-term gain

Testing helps you discover whether your personalization efforts are bearing fruit. But all too often, marketers test only individual elements of a specific campaign – subject lines, calls to action, images versus no images, personalization versus no personalization  – without looking at whether personalization enhances the customer experience in the long term.

How you measure success is a key part of this equation. The metrics you choose must line up with your objectives. That’s one reason I’ve warned marketers for years against relying on the open rate to measure campaign success. A 50% open rate might be fantastic, but if you didn’t make your goal for sales, revenue, downloads or other conversions, you can’t consider your campaign a success.

As the objective of personalizing is to enhance the customer journey, it makes sense then that customer lifetime value is a valid metric to measure success on.  To measure how effective your personalization use is, use customer lifetime value over a long time period – months, even years – and compare the results with those from a control group, which receives no personalization. Don’t ignore campaign-level results, but log them and view them over time.

(For more detailed information on testing mistakes and how to avoid them, see my MarTech column 7 Common Problems that Derail A/B/N Email Testing Success.)

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Mistake #5. Over-segmenting your customer base

Segmentation is a valuable form of personalization, but it’s easy to go too far with it. If you send only highly segmented campaigns, you could be exclude – and end up losing because of failure to contact – many customers who don’t fit your segmentation criteria. That costs you customers, their potential revenue and the data they would have generated to help you better understand your customer base.

You can avoid this problem with a data-guided segmentation plan that you review and test frequently, a set of automated triggers to enhance the customer’s lifecycle and a well-thought-out program of default or catch-all campaigns for subscribers who don’t meet your other criteria. 

Mistake #6. Not including dynamic content in general email campaigns

We usually think of personalized email as messages in which all the content lines up with customer behavior or preference data, whether overt, as in an abandoned-cart message, or covert, where the content is subtly relevant.

That’s one highly sophisticated approach. It incorporates real-time messaging driven by artificial intelligence and complex integrations with your ecommerce or CRM platforms. But a simple dynamic content module can help you achieve a similar result. I call that “serendipity.”  

When you weave this dynamic content into your general message, it can be a pleasant surprise for your customers and make your relevant content stand out even more. 

Let’s say your company is a cruise line. Customer A opens your emails from time to time but hasn’t booked a cruise yet or browsed different tours on your website. Your next email campaign to this customer – and to everyone else on whom you have little or no data – promotes discounted trips to Hawaii, Fiji and the Mediterranean.

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Customer B hasn’t booked a cruise either, but your data tells you she has browsed your Iceland-Denmark-Greenland cruise recently. With a dynamic content module, her email could show her your Hawaii and Mediterranean cruise offers – and a great price on a trip to Iceland, Denmark and Greenland. Fancy that! 

An email like this conveys the impression that your brand offers exactly what your customers are looking for (covert personalization) without the overt approach of an abandoned-browse email.

Mistake #7. Not using a personal tone in your copy

You can personalize your email copy without a single data point, simply by writing as if you were speaking to your customer face to face. Use a warm, human tone of voice, which ideally should reflect your brand voice. Write copy that sounds like a one-to-one conversation instead of a sales pitch. 

This is where my concept of “helpful marketing” comes into play. How does your brand help your customers achieve their own goals, solve their problems or make them understand you know them as people, not just data points?  

Mistake #8. Not personalizing the entire journey

Once again, this is a scenario in which you take a short-sighted view of personalization – “How do I add personalization to this email campaign?” – instead of looking at the long-term gain: “How can I use personalization to enhance my customer’s experience?”

Personalization doesn’t stop when your customer clicks on your email. It should continue on to your landing page and even be reflected in the website content your customer views. Remember, it’s all about enhancing your customer’s experience.

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What happens when your customers click on a personalized offer? Does your landing page greet your customers by name? Show the items they clicked? Present copy that reflects their interests, their loyalty program standing or any other data that’s unique to them?  

Personalization is worth the effort

Yes, personalization takes both art and science into account. You need to handle it carefully so your messages come off as helpful and relevant without veering into creepy territory through data overreaches. But this strategic effort pays off when you can use the power of personalized email to reach out, connect with and retain customers – achieving your goal of enhancing the customer experience.


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


About The Author

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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.”

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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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