MARKETING
Brand Equity and Its Impact on Revenue
It has long been common for marketers to have separate media buying budgets for branding efforts and performance-driving efforts, with each living on their own proverbial side of the house. Branding budgets have historically had their own set of KPIs, different from those tracked and measured for performance marketing initiatives, with the dichotomy implying that one has no influence on the other.
But we all know brand and performance don’t live in vacuums.
For example, even the most sophisticated, omnichannel marketing campaign isn’t likely to drive sales for a product if the manufacturer of that product is making headlines for safety concerns or quality control issues. The problem hasn’t been that marketers don’t believe the two impact one another, rather that they had no measurable way to show it.
Unable to meaningfully tie brand-driven effects to performance outcomes has left marketers relegated to basic metrics like reach and frequency. This puts more pressure on performance dollars to improve metrics including ROAS (return on ad spend), CAC (customer acquisition cost), and CPAs (cost per acquisition).
We hypothesize that brand heavily influences performance and have identified a streamlined way to more effectively measure it on an ongoing basis. Failing to account for brand equity leaves marketers underestimating the total value of their campaigns, missing a fundamental driver of both ROAS and their bottomline business.
What is brand equity?
At Tinuiti, we think of brand equity as the cumulative set of widely held perceptions, incorporating both the value propositions of your product itself, and the positive and negative sentiments about your brand as a whole. These perceptions can significantly influence consumer behavior, and through that behavior, influence revenue over time.
Example scenario:
Imagine you own and operate a popular coffee shop in your neighborhood and have built a great reputation that earns you a lot of word-of-mouth business. Despite your success, you decide to close your coffee shop for a few years so you can travel the world.
Fast-forward a few years and you’re set to reopen, but have decided to go with a totally new name for your shop. Do you think you would receive the same response from the market immediately, as though you’d never left and nothing had changed? We don’t believe so.
Trust is a key ingredient of a strong brand, and just as in other aspects of life, trust is slow to build and can be lost very quickly. However, the promise of your brand could be rebuilt over time to help eventually get back to where you started.
Brand equity is slow to build but has long-lasting effects on consumers, while brand health can have a significant impact on the revenue you’re driving today. Furthermore—business outcomes, such as revenue—have traditionally been attributed to performance marketing…but this relationship is incomplete.
We believe brand equity is a key missing piece in the equation that gets us closer to accurate attribution of business outcomes.
Is brand equity the same as brand health?
As marketers, there is a hidden set of variables we all struggle to measure, understand, and leverage to our benefit as they relate to the slowly-won equity of a brand, with the terms “brand equity” and “brand health” often being used interchangeably. However, we think of brand equity as a broader umbrella term that represents the intangible value of a brand, while brand health is more closely associated with the immediate perception of a brand at a particular point in time.
Impact of Brand Equity: Painting the Complete Picture
As a performance marketing firm, accurate measurement and attribution is key for our teams. However, as we explored above, performance marketing alone paints an incomplete picture.
In our quest to better understand all revenue-driving aspects of a given campaign, we have started on a process to quantify the impact of Brand Equity, which we believe is one of the largest missing pieces in more accurate and complete measurement.
To do so, we are looking at Brand Health in the modeling and analysis phase as it reflects Brand Equity at a given point in time, is measurable in real-time, and can be tied to business outcomes such as revenue.
Ultimately, we hope our research will help us answer important Marketing questions like:
- “How did a given brand campaign impact sales, or help in achieving other campaign goals?”
- “Which investments are the most potent drivers of brand health outcomes?”
- “How do changes in brand health impact bottom-line business outcomes?”
- “How much do I need to invest to move brand health outcomes X by Y?”
These complex questions can be distilled into 2 essential questions:
- How does media impact brand health outcomes?
- How does brand health impact business outcomes?
We are currently developing a robust quantitative framework to answer these two essential questions. While randomized control trials are the gold standard for causal inference, brand-building is a slow process, and running these experiments can be both time-consuming and expensive.
Media Mix Modeling (MMM) provides a neat alternative to this problem by leveraging observational data. We chose Meta’s open-source MMM tool, Robyn, for our framework, thanks to its sophistication and ease of use.
Conclusion
While brand and performance marketing investments are often managed and measured separately, we believe that they have a significant impact on each other. We believe the impact of brand equity on revenue to be supplementary, and this crucial element is often overlooked by marketers when estimating ROAS.
Tinuiti will be working with Meta in 2024 to provide a deeper dive into our collaborative efforts supporting brand health measurement. Stay tuned!
This article was a collaborative effort, with contributions from additional individuals, including Shannon Mullery, Senior Content Specialist, Marketing at Tinuiti, Kevin O’Connor, Manager, Econometrics at Tinuiti and Kolin Kleveno, SVP, Partnerships at Tinuiti.
MARKETING
YouTube Ad Specs, Sizes, and Examples [2024 Update]
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!
MARKETING
Why We Are Always ‘Clicking to Buy’, According to Psychologists
Amazon pillows.
MARKETING
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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