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Not all B2B and B2C categorizations are alike

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Not all B2B and B2C categorizations are alike

I recently jumped from a B2C marketing department at Western Governors University (WGU) in the online higher education sector to a B2B marketing department at Zuora that provides subscription management software in the SaaS space. This change has made me think about the value of the B2B and B2C categories.

Perhaps it is more helpful to consider the differences between industries instead of differences between B2B and B2C. For instance, there are definitely differences between the online higher education and SaaS sectors, and that’s where I’m noticing the source of most of the differences in my current situation.

While I believe that the B2B and B2C categories have utility, I’m not sure how useful they are for my experience as a martech maestro. It is very likely, however, that they have significant utility for other marketing operations and technology practitioners.

Not such a clean-cut distinction

There are many ways to distinguish B2B from B2C. For instance, B2B might imply that more than one person is involved in decision-making, while in the B2C context it may only involve one person. However, to state the obvious, that’s not always the case.

An SEO tool that costs only a hundred or so dollars a month is a B2B situation that really doesn’t require many people. Someone as junior as an intern can select it and simply ask a superior who can quickly approve. They then can pay for it using a corporate card without much fuss. Not all B2B products are really so expensive or complicated that they require many stakeholders to evaluate and approve over a long period of time.

WGU provides low-cost bachelor’s and master’s degrees with flexibility. With the exception of the group that tries to strike and sustain partnerships with businesses and organizations to help incentivize their employees to pursue a WGU degree with a discount as a perk, WGU’s marketing efforts are mainly B2C.

Despite the great value, a prospective student likely needs to consult with a partner or employer to discuss finances — let alone make arrangements for lifestyle factors like childcare, as a student has to commit significant amounts of time over several years. Thus college degree programs and other high ticket items (some electronics, automobiles, travel, real estate, etc.) can require significant input and agreement from several different people, and that can take plenty of time.

Similarities between B2B and B2C

Further, B2B and B2C marketing share plenty of common aspects.  

Most marketing departments need websites, analytics, marketing automation, CRMs, and many other major types of systems. Data hygiene is certainly important in both contexts, too.  Additionally, marketing involves persuasion, segmentation, targeting, research, creativity, and many other tactics regardless of the context.  

B2B marketers use firmographics and technographics for their ICP/TAM models, while B2C marketers use demographics and consumer research for their personas and lookalike modeling.  Heck, one could argue that technographics apply to B2C; an iPhone case maker certainly doesn’t want to expend money and effort marketing to Android phone owners.

Besides, both B2B and B2C marketers themselves deal with plenty of B2B marketers as they research, procure and use various stack components.

The maestro perspective

Martech maestros keep a strategic view over the entire tech stack. Thus, they’re generalists, in a sense, when it comes to individual components and they help orchestrate the bigger picture. I argue that this is a benefit since a maestro can help a component-owner or power-user see a broader perspective than they get from working in the weeds with the component. The maestro can help identify and test assumptions and see how the component fits with the department and organization’s broader stack.

Therefore, while maestros should have a good idea of how various types of systems function (CMS, CRM, DAM, etc.), they will likely have to continually deal with all sorts of situations as stacks have a lot of components. That means that there is always something new to learn. Whether they’re assisting with a B2B ABM platform or a B2C-focused CDP, they should employ similar strategies and frameworks to help the stakeholders make more deliberate decisions, interact with other stakeholders and ensure favorable ROI.

Why we should care

As business professionals, we use broad categorizations and labels to help us better understand what we do and what others do. When we use B2B and B2C in marketing, we need to remember that these two broad categories aren’t as clear-cut as they may appear.  Thus, we may make inaccurate assumptions and decisions regarding a situation or interact amongst ourselves.  

For instance, when we discuss different case studies and tactics, we sometimes discount the value of listening and considering insights than the other context. While insights may not translate from a B2B situation to a B2C one (or vice versa), the different categorizations may not be the driving force in that disconnect. Something else might be the culprit. Further, a B2B insight may apply to a B2C situation. Finally, B2C insights don’t universally apply to all B2C situations, and vice versa.

Conclusion

The B2B and B2C categorizations certainly offer value. But do they provide significant value in the context of marketing operations and technology? At least when it comes to maestros and overall stack orchestration, they don’t seem to offer much value — at least not from my perspective.

I’m interested to hear what you all think. For instance, should we indicate which type of marketing we work in? I’m going back and forth on how to represent this, for instance, on my LinkedIn profile.

About The Author

Not all B2B and B2C categorizations are alike
Steve Petersen is a marketing technology operations manager at Zuora. He spent nearly 8.5 years at Western Governors University holding many martech related roles with the last being marketing technology manager. Prior to WGU, he worked as a strategist at the Washington, DC digital shop The Brick Factory where he worked closely with trade associations, non-profits, major brands, and advocacy campaigns. Petersen holds a Master of Information Management from the University of Maryland and a Bachelor of Arts in International Relations from Brigham Young University. He’s also a Certified ScrumMaster. Petersen lives in the Salt Lake City, UT area.


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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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