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Why We Love Them + Brand Examples

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Why We Love Them + Brand Examples

According to a 2021 McKinsey report, 76% of consumers get frustrated when they can’t find a personalized experience with a brand.

With personalization becoming more valuable to stand out from the competition, a look at some great examples can serve as inspiration for your own strategy.

Discover brands that are killing it at personalization and get a peek into how impactful it can be.

Why do we prefer personalized experiences?

Twilio’s 2022 State of Personalization report found that 49% of consumers say they will likely become repeat buyers after a personalized shopping experience with a retail brand.

Businesses also report that consumers spend more when they have a personalized experience. In fact, 80% of business leaders surveyed in Twilio’s report say that consumers spend an average of 34% more with a personalized experience.

Conversely, all it takes is one bad experience to deter a customer. One 2021 Zendesk CX report found that 50% of consumers will switch to a competitor following a negative brand interaction.

personalized marketing experiences: 50% of global consumers will switch to competitor after one bad experience.

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So, the question is, why is that? What is it about this type of experience that attracts consumers and why is it so fragile? One study may have the answer.

​​According to a study from the University of Texas, we can attribute our preference for personalized experiences to two key factors: desire for control and information overload. Let’s tackle “desire for control” first.

So, we know that a personalized experience – by its very nature – is in some way different from the status quo. You’re not just getting what everyone else is getting with personalization. Instead, you’re getting something tailored to you. And because of that, it makes you feel more in control.

Even if this sense of control is an illusion, it’s still powerful, and can have a positive effect on your psyche.

Now, let’s turn to the second factor mentioned in the University of Texas study: information overload. According to the study, personalization can help reduce this perception.

For example, when you know that the content being displayed on a website is tailored to you, it provides a more manageable framework for engagement. With personalization, you aren’t presented with thousands of resources to sort through and consume.

Instead, you are presented with exactly the information you were looking for. Hence, you never feel overloaded with information.

Now that you know the psychology behind personalized experiences and how effective they can be, let’s dive into some real-world examples.

Personalized Marketing Experiences

Personalization covers a wide range of strategies leveraged by brands both online and offline. Some brands take an omnichannel approach while others focus their efforts on specific channels.

To get a sense of what’s possible, here are a few examples of personalization:

  • Names in email subject lines and email content.
  • Location-based push notifications.
  • Welcome back messages on a website homepage.
  • Cart abandonment notifications.
  • CTAs based on buying cycle.
  • Product recommendations based on purchase and/or search history.
  • Customer loyalty programs.

According to the same McKinsey & Company report referenced earlier, the top five personalization actions consumers want are: easier online and in-store navigation, personalized product or service recs, tailored messages, relevant promotions, and personal milestone celebrations.

2021 Mckinsey&company report showing that consumers want brands to meet them where they are, know their taste, offer something unique, and check in with them.

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Let’s see those strategies applied by brands.

1. Sephora

When it comes to personalization, Sephora is one brand everyone looks to.

Over the years, the beauty retailer continues to optimize its omnichannel personalization strategy, ranking consistently among the top winners in Sailthru’s Retail Personalization Index for the past five years.

At the center of Sephora’s personalization is its mobile app. One of the first things you notice about the app is its ability to turn customer data (collected through quizzes and user behavior) into recommendations using predictive analytics.A look at sephora offering a personalized experience at every touchpoint

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The app houses data on in-store purchases, browsing history, purchases, and in-store interactions.

Where the brand really shines is in its ability to combine online and in-store to create a seamless, frictionless experience for shoppers.

The app invites users to find products in-store and book in-person consultations. Once in-store, customers will receive a log-in for the app to create a profile that includes the products they used. This way, they can always find the information they need for a future purchase.

In addition, Sephora has a loyalty program in which it offers exclusive access to products, events, and services based on their tier. At every touchpoint, users can track their loyalty points and get custom recommendations.

2. Netflix

Everyone’s Netflix account looks different when you log in.

That’s because the streaming service has made it a priority to personalize the user experience as they navigate the platform.

The first display of this is the large banner ad that displays when you first land on the app. It’s usually a trailer for a newly added show or movie – the difference is the selection is different for everyone.

For me, that might be the newly released season of Peaky Blinders since I’ve seen all previous seasons. For another, it might be something else.

As you scroll through the app, you’ll see two more personalized sections: “Because you watched” and “Picked for you.”

personalized experience on Netflix: the "because you watched" sectionpersonalized experience on Netflix: the "top picks" section

Through AI and machine learning, Netflix’s algorithm is programmed to suggest shows and movies based on a user’s watching history, including watch time and review.

What you end up with is a programming list with elements from content you’ve enjoyed in the past, making it easier to pick something new. Plus, it keeps you coming back for more.

That’s the beauty of the app – you know that everything is curated just for you based on your personal interests.

3. Amazon

If you currently oversee an ecommerce store, Amazon is a great model to get inspiration from.

This retailer has created an interface that offers relevant recommendations based on browsing and purchasing history.

When you first land on the homepage, you’ll have the option to navigate to the following sections:

  • Keep shopping for
  • Pick up where you left off
  • Buy it again
  • You might also like
  • Inspired by your wish list
  • Recommended for you

Every single one of these sections is personalized to the user based on their behavior on the site.

In addition to inferring information about its customers, Amazon will occasionally survey its users.

For instance, shortly after purchasing a product for my cat, the following question popped up on my homepage: “Do you own a dog or cat?” They explained that this information would be used to offer more personalized recommendations.

A key takeaway here is to fill in gaps in your data by reaching out to your users. This will be especially important if you’re using AI-powered software and need to feed it information to guide its algorithm.

Personalized experiences are the way of the now and the future. The earlier you jump on, the easier it will be to keep up with consumer behavior.

Editor’s Note: This post was originally published in Nov. 2014 and has been updated for comprehensiveness.

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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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