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The truth about marketing attribution

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The truth about marketing attribution

Is marketing attribution a mythical unicorn? Or is it possible to measure the performance of your marketing, including the parts you can’t see?

The fog of marketing

Marketing attribution is the “fog of marketing” that all marketers wrestle with in attempt to square the circle and make sense of the customer’s decision journey. It’s essential for understanding how your marketing is performing and how much each channel is contributing. Proper attribution enables you to maximize your results and your return on investment. But it’s complicated…to say the least.

Attributing success to any initiative or channel is often difficult due to the complexity of customer journeys and the ever-changing landscape of digital media and data privacy. As marketing attribution becomes increasingly difficult, how will marketers decide where — and how much — to invest?

There are many attribution models and high-tech attribution platforms that promise to make it crystal clear where customers came from and how each touchpoint contributed along the way. Unfortunately, none of them are a panacea for the challenges of modern marketing.

The futility of attribution

One of the allures of digital marketing was that much, if not all, activity could be tracked and measured. If that dream was ever realized, it was short lived. Digital certainly makes measurement easier, but there are still dozens of challenges that make accurate attribution near impossible.

Here are just a few to consider:

Data privacy and cookies

Users are fighting to protect their data privacy and the tech giants like Apple and Google are responding accordingly. Marketers are no longer able to rely on these providers for data and insights. Legislation like GDPR continue to create more obstacles towards the transparency and tracking required for attribution to succeed.

Attribution is based on customer behavior, which means it fluctuates with the seasons in line with changes in buying behavior. Attribution can also be significantly skewed by trends. For example, a viral TikTok could make the channel seem like a valuable source to invest in when in reality the value is short lived.

Multiple streams

Companies with multiple product categories or simultaneous campaigns will face the challenge of untangling their attribution data. The customer journey becomes muddy quickly when there is the potential for overlapping or conflicting paths, especially when they can become intertwined and impact one another.

Constant change

Customers and culture keep changing. As technology and trends change, so does customer behavior. There will be new channels tomorrow that didn’t exist today. How your attribution looks today is only a snapshot in time and therefore must be continually revisited and updated.

One thing is for certain: marketing attribution is going to get more complicated, complex, and confusing.

Fortunately, there are three simple parts that, when combined, will help you measure and maximize even the invisible aspects of your marketing.

Part 1: The customer is always right

It’s hard to talk about measuring the customer journey without talking about the customer themselves. If you want to understand how a customer navigates and experiences your marketing, it’s best to start by understanding the customer and taking a walk in their shoes. 

Customer proximity is paramount: Whoever is closest to the customer wins.

Success in marketing comes from an intimate understanding and alignment with your customer, not from meticulous measurement of the ensuing activities.

The better you can truly understand the customer, the more clarity you’ll have around what channels and activities they experience and care about; and you’ll know which channels matter (and how much). Attribution becomes less mysterious the more you know about the customer and what their experience is really like.

There are many ways you can — and should — be understanding your customers, including:

Customer interviews

When is the last time you talked to your customers? Interviewing prospective, current, or past customers is essential, and an easy way to gain insights about what matters to customers and which touchpoints are critical. It’s remarkable the insights that can be generated with a well-prepared set of questions, a skilled interviewer, and a handful of willing customers.

Joining the community

Show up and hang out where your customers congregate. Facebook groups, Discord communities, online forums, YouTube channels, and other social watering holes. Although they might not be talking about your brand or products incessantly, you will discover unique insights and begin to understand your customer’s thinking and decision making process on a new level.

Mystery shopping

Adopt a “beginner’s mind” and act as if you were an average customer of your products or services. What would you do to learn more about the industry, discover your options, and compare alternatives? How would you go about making a purchase decision? You’ll experience firsthand which channels have a bigger impact on your decision making process and most certainly find opportunities for improvement along the way.

Read next: What marketing attribution is and how it identifies vital customer touchpoints

Part 2: Measure in broad strokes

Does data help with measuring attribution? Absolutely. Even partial data is better than nothing. Marketers who disregard the usefulness of imperfect data in attribution fail to realize that marketing is equal parts art and science.

On the other hand, marketers love to dwell and debate over attribution models. First touch? Last touch? Choose any and make the best of it because none of them are right or reliable. 

And don’t fall for the trap that an attribution platform will answer all of your questions, either. All of these — limited data, attribution models, and attribution platforms — are helpful pieces of the puzzle. But it’s far too complicated and time consuming to build a puzzle without stepping back and looking at the big picture.

Attribution is a means to an end, not the end itself. Measure outcomes instead of activities.

Collecting data, analyzing it, and trying to make sense of it is admirable but often wasted effort. When marketers emphasize data too much, they can’t see the forest for the trees. The point of marketing is to generate results. Focus on business outcomes and on the net impact of your marketing efforts instead of scrutinizing the source of attribution.

Instead of trying to measure how the water got in the ocean, pay attention to whether or not the tide is rising or falling.

Part 3: Optimization before attribution

The inherent assumption of attribution is that every channel or activity is performing at an acceptable level (or near optimal) and therefore the only question remaining is how to reallocate resources to maximize return.

In practice, that’s hardly ever the case.

Across all of my clients, I’ve yet to find a channel, initiative, or activity that isn’t rife with opportunities for optimization. In these instances, shifting away resources cuts off the oxygen to profitable growth since optimization is often the fastest and most cost-effective way to increase results, revenue, and profitability. 

Investing in optimization is essential, delivers an immediate ROI, and can often quell any need or appetite for full-scale attribution. Before investing the massive time, effort, and resources into attempting to solve attribution in a major way, focus first on identifying opportunities and optimizing your marketing activities. Optimization before attribution.


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Conclusion: Know and understand your customer

Marketing attribution is complicated and complex and it’s only going to become more so. Instead of exhausting yourself trying to hit a moving target, focus on knowing and understanding your customer, consider the data available to you, and optimize every initiative to its fullest.

The closer you can get to your customers, the more insight you’ll have into their journey and their decision making process, and the more confidence you’ll have about where and how to invest to deliver a remarkable experience and a positive return on your investment.


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


About The Author

Tim Parkin is a consultant, advisor, and coach to marketing executives globally. He specializes in helping marketing teams optimize performance, accelerate growth, and maximize their results.
By applying more than 20 years of experience merging behavioral psychology and technology, Tim has unlocked rapid and dramatic growth for global brands and award-winning agencies alike.
He is a speaker, author, and thought leader who has been featured in AdAge, AdWeek, Inc, TechCrunch, Forbes, and many other major industry publications. Tim is also a member of the American Marketing Association, Society for the Advancement of Consulting, and an inductee to the Million Dollar Consulting Hall of Fame.

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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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