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What is a Media Mix & The Most Effective Types [HubSpot Blog Data]

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What is a Media Mix & The Most Effective Types [HubSpot Blog Data]

I’m willing to bet that today, the typical campaign at any brand uses a media mix.

This refers to the process of using multiple channels to meet marketing goals. The question is, how exactly does it help brands better plan campaigns?

Let’s dive into how it works, review some stats, and see some brand examples.

A media mix is another term for an overview of the channels businesses choose to execute their marketing strategies on. Ultimately, media mix optimization is the process of analyzing the performance of those channels.

Think about the last campaign you saw from one of your favorite brands. What did they do differently that really clicked with you? By incorporating a media mix into your yearly planning, you can narrow down what resonates with your audience.

Media Mix Example

To see how a media mix works for a campaign, let’s use makeup brand The Lip Bar’s latest campaign: “Something BAWSE is coming.”

To build anticipation around its latest product launch and celebrate its 10-year company anniversary, The Lip Bar launched a multi-channel campaign that reached audiences both online and offline.

First up, website.

media mix example showing makeup brand's campaign on their website

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When you land on the brand’s homepage, you’ll see an image of the CEO with the following sentence “Shark Tank said no, 12,000 5-star reviews later, 100% that bawse.”

Upon clicking on the image, you’ll arrive at a landing page that has a timeline of the brand. From launching in 2012, to opening its flagship store in 2019, then expanding to 1500+ stores in 2021. Once you get to 2022, you see the tagline “Something Bawse is Coming.”

As you continue scrolling, you reach a section that offers a launch date and time for the brand’s newest product.

The second digital channel the brand leveraged for this campaign is YouTube, publishing a 45-second video showing the CEO’s journey over the last 10 years.

Moving offline, the brand also published billboards in five cities to continue the campaign.

Note how the billboard didn’t use the tagline we saw on social media platforms.

media mix example showing makeup brand's billboard campaign

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The benefit of using a media mix is to leverage different strategies to see which tactics work and lead to better conversions.

The Lip Bar’s latest campaign is a great example of how any brand can leverage channels differently to reach the same marketing goal.

Media Mix Stats in 2022

In January 2022, we surveyed over 600 media planners to discover their strategies, goals, and challenges. Here are some interesting findings:

  • 84% of media planners surveyed leverage a mix of organic and paid media.
  • Email marketing is the most leveraged media channel, used by 1 in 2 media planners.
  • 41% of media planners surveyed will change their media mix in 2022, while 45% will keep it the same.
  • 14% of media planners currently leverage non-fungible tokens (NFTs) while 16% plan to use them for the first time in 2022.
  • 39% of those who use NFTs in their media planning say they have the best ROI of any channel in their media mix.
  • 39% of marketers surveyed say determining the most effective media mix is the biggest challenge of media planning.

Media Mix Optimization

Media mix optimization provides businesses with an understanding of how their messages are coming across to customers. It allows a brand to invest more time and money into marketing strategies that are best suited for their audiences.

Marketers might consider optimizing their media mix if they want to gain some helpful insight into what time and capital is needed to target their audience in a way that gives customers a personalized experience.

But, while media mix optimization is a powerful opportunity for methodizing data collection online, it’s not the best strategy for marketers who employ a lot of traditional marketing techniques, since you can’t really measure the success of a billboard or newspaper ad.

However, to make guided decisions such as what font to use in creative design, when to publish social media posts on various channels, or where to invest resources, this method can be helpful.

Optimizing a media mix means looking at the analytics and ROI of various marketing strategies. This can be anything from engagement data of social media platforms to views on the newest commercial.

That’s where media mix modeling comes in. If media mix optimization is the “what,” modeling is the “how.” Every model can (and should) look different, depending on your marketing and broader business goal.

Media mix models can be used to analyze the relationship between a dependent variable and an independent variable.

For instance, let’s say a business has a question like, “How did paying for a sponsored tweet affect overall blog traffic?” The business’s media mix model should then accurately depict how a dependent variable – like overall blog traffic – relates to an independent variable, such as investing in Twitter.

For businesses still deciding if a media mix optimization is a good idea for them, we’ve put together key tips to guide you when creating a media mix model. Let’s explore those, next.

Tips for Optimizing Your Media Mix

1. Collect personal level data.

The goal here is to find and focus on analytics that will help provide an accurate picture of how customers engage with your media mix.

Analytics software is expansive and offers an array of tools for use. If you’re in the market for one, the HubSpot Marketing Hub is a great place to go.

Having too many metrics can be confusing and lead to inaccurate data. The best plan is to have an idea of which metrics need to be tracked so they can be right at the beginning.

A normal media mix optimization process can take anywhere from a few months to a year. So, collecting the right information at the beginning contributes to getting the most accurate information overall.

2. Have a robust reporting process.

When using a media mix, you often combine online and offline strategies. With this in mind, it can be more difficult to measure the impact of a billboard compared to a paid social ad.

That means you’ll have to think outside of the box when evaluating your media mix. For instance, while an online ad will have clear conversion rates you can find, a billboard may require some creative tracking.

Perhaps, you can track the uptick in branded search volume or improved brand recall.

This requires having a robust reporting process that accounts for the events you’ll be tracking – both online and offline.

3. Choose the right platform.

Marketing teams that use CMS or analytics software are already ahead of the game. Software like this is essential to optimizing a media mix because it can give you numbers that would otherwise take some time to figure out manually.

A brand can analyze its media mix with the use of platforms that collect engagement data in real-time and compile that data into tracking reports.

Look for a platform that can give a holistic view of results across the board, so results will maintain consistency. It’s also good to choose software that specializes in the marketing channels being used at the time.

Because optimizing is measuring a lot of different data at once, stick to as few systems as possible.

For businesses that are in the market for a CMS, HubSpot offers tools that are easy to use for brands of any size.

4. Analyze the data.

We’ve been talking a lot about the kind of data and analyzing that needs to be done in a media mix optimization, but another important factor is being able to interpret and understand that data.

It’s no secret that in the marketing world, there is an abundance of processes and acronyms floating around. While first getting into the groove of understanding them and what they mean can be intimidating, it’s important to know the data being collected and how to use it to your advantage.

For instance, if a marketing team had especially high click-to-open rates for weekly newsletters, that’s useful information to infer that the next campaign could benefit greatly from an email-marketing rollout. Alternatively, if a marketing team has no idea what a click-to-open rate is, those numbers aren’t going to be helpful — just a little confusing.

Reading data to understand its usefulness is just as important as collecting it.

5. Consider public perception.

Knowing how the public perceives your brand can help fill in some interpretation gaps during the modeling process. In the media mix model, think about how to fit in customer opinion. That way, the numbers will have some customer opinions to define them.

There are a couple of ways to do this.

To better understand your brand perception, monitor your brand mentions on social media. Take note of the positives, the negatives, and the questions. You can also create a survey to know how your customers feel about your company and its services and use a net promoter score.

An NPS asks customers how likely they are to recommend a business to a friend. Knowing this will aid in figuring out how a business fares among the competition in the market. It will also assist in future marketing endeavors.

For instance, if customers fill out your survey and ask for more personalized Instagram stories, your team could take that knowledge and include it in your next campaign.

This method ultimately gives the media mix optimization reigns to the customer.

Media mix optimization can help your marketing team figure out which distribution channels will best promote an upcoming campaign, and can ultimately help strengthen your marketing strategy as a whole.

Editor’s Note: This post was originally published in Feb. 2020 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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