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MOps leaders as psychologists: The modern mind-readers

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MOps leaders as psychologists: The modern mind-readers

This four-part series presents a framework that describes the roles and responsibilities of marketing operations leaders. This part discusses MOps leaders as psychologists, in addition to their roles as modernizers (see part 1) and orchestrators (see part 2).

Exposure to marketing during my early educational journey was limited. With a heavy math/science background, I chose the “easy” path and majored in engineering. I struggled in advanced engineering classes but thrived in electives — communications, business, organizational behavior — which was a sign for my future in marketing.

Because of my engineering background, I was fortunate to get an opportunity to join GE Healthcare through its entry-level leadership development program. There I was exposed to magnetic resonance imaging (MRI). 

MRIs had become go-to diagnostic devices and subsequently were used in neuroscience. I was fascinated by their eventual application in fMRI: Functional MRI. These extensions helped us understand the most consequential medical mystery: how (and why) people do what they do.

fMRI uses the same underlying technology as conventional MRI, but the scanner and a medical contrast agent are used to detect increased blood flow in response to a stimulus in what is commonly referenced as “hot spots.”

fMRI reveals which of the brain’s processes “light up” when a person experiences different sensations, e.g., exposure to different images in common studies. As a result, we now know what parts of the brain are involved in making decisions.

1665035104 966 MOps leaders as psychologists The modern mind readers

Successful marketing ‘lights up’ customers’ brains

Traditional marketing campaigns and measurement left gaps in understanding how and why people choose to buy. We were dependent on aggregated data. 

With digital channels, we gain first-hand insights into an individual’s response to a stimulus, i.e., content. Here’s where the comparison picks up: 

  • We can observe nearly anything and everything that customers or prospects do digitally.
  • Most customers know that we can track (almost) everything that they do.
  • Because of that knowledge, customers expect contextual, value-based content, forcing marketing to provide more value in exchange for the permission to track.

Our goal as marketers is to make our customers and prospects “light up” with pleasure or satisfaction at each interaction. And, we now have the technology to track it. We are effectively reading minds — just as if it were an fMRI scan.

Here’s an overview of three of the primary psychology “tactics” that every marketer should know: 

  • Priming is the attempt to trigger a subconscious reaction to stimuli that influences our conscious decisions. The most common application is in branding and first click-through impressions. If a customer continues their journey, then the use of aspirational product or service images in content are common priming approaches.
  • Social proof is perhaps the most common example, given the impact of word-of-mouth influence. It is commonly seen in product reviews and ratings. Content marketing often relies on case studies and customer testimonials to hear from “people like us.”
  • Anchoring refers to marketing’s role in pricing and discounting. Most decisions people make are relative to the initial set of information they have received.

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MOps leaders manage the mind-reading stack 

MOps leaders are modernizers that now manage the mind-reading martech stack. We then lead the orchestration efforts to analyze the response (the “scan” data) and “prescribe” the next steps of the campaign.

Two catalysts spawned the emergence for martech applications:

  • New channels that delivered stimulus (content) and collected responses: search, social media, retail commerce channels, etc.
  • Tools that organize and manage all of that response data, from foundational CRM platforms to marketing analytics and data enrichment.

These developments led to the new psychological skills that have become essential to the role of MOps leaders. 

Processing and interpreting intent data is an example. ZoomInfo illustrates how B2B marketers are accessing this capability. The company now provides buying signals to marketers based on their customers’ behaviors, in addition to the basic contact information that was the origin of its business. 

MOps leaders as psychologists The modern mind readers

Intent data is already in widespread use. Six in 10 companies responding to a recent survey said they had or planned in the next year to implement intent measurement data solutions. 

The top challenges for effective intent data utilization fit squarely in the role/responsibilities of MOps leaders include:

These trends support the conclusion of the first three parts of this series — that MOps leaders should aspire to be: 

  • Psychologists who elicit responses (i.e., “light up” the brains) of customers and prospects and interpret those signals for the business. 
  • Modernizers who adopt the technology that enables the activation of those signals.
  • Orchestrators who are cross-functional project managers and business partners with IT, legal and compliance.

Next time, I’ll complete the framework with a discussion of how the role of MOps leaders includes being a scientist, constantly testing and evaluating marketing efforts with teams of analytics specialists and data scientists. 

Editor’s note: This is the 3rd in a 4-part series. In case you missed them, part 1 (Modernizers) is here and part 2 (Orchestrators) is here.


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


About The Author

Why marketing operations leaders have become modernizers

Milt is currently Director of Customer Experience at MSI Data, an industry-leading cloud software company that focuses on the value and productivity that customers can drive from adopting MSI’s service management solutions.

With nearly 30 years of leadership experience, Milt has focused on aligning service, marketing, sales, and IT processes around the customer journey. Milt started his career with GE, and led cross-functional initiatives in field service, software deployment, marketing, and digital transformation.
Following his time at GE, Milt led marketing operations at Connecture and HSA Bank, and he has always enjoyed being labeled one of the early digital marketing technologists. He has a BS in Electrical Engineering from UW Madison, and an MBA from Kellogg School of Management.

In addition to his corporate leadership roles, Milt has been focused on contributing back to the marketing and regional community where he lives. He serves on multiple boards and is also an adjunct instructor for UW-Madison’s Digital Marketing Bootcamp. He also supports strategic clients through his advisory group, Mission MarTech LLC.

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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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