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Get your front row seat for the race to be the B2B revenue platform of record

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There’s a positioning battle going on across B2B sales, customer, data, and marketing technology providers. Aiming to organize these diverse B2B solutions into a mega-category — we’ll call it “revenue technology” — the race is on to develop more modern, effective systems to generate customers and manage revenue. And while no one system can do it all, it’s clear a big payoff is awaiting the providers who can get it right in this next B2B era. 

Grab your popcorn and beverage of choice. This should be fun to watch. Well, not so much for the marketing, customer, sales, and operations execs who have to pick the right horse(s) to compete in today’s market while also placing bets on the future. 

Before we get into who is competing in this race, let’s talk about the “why” behind the positioning battle underway for the minds, hearts, and wallets of the B2B go-to-market (GTM) teams. 

For the past handful of years, B2B teams have been trying to transition from generating volumes of leads to focusing on the buyer and account engagement that more effectively, efficiently, and predictably generates revenue and relationships.

Developed well over a decade ago, marketing automation platforms (MAPs) have been the system to help marketers generate leads to support sales. CRM has been the default system to manage customers and customer data, primarily with the lens of an internal sales process and management. A range of ABM tools have supported account engagement.

MAPs and CRMs, while workhorses, haven’t been entirely effective in enabling marketing and sales teams to execute the transition. And it’s even more true today as the buying-selling environment is quickly evolving, becoming far more dynamic and complex. 

The B2B buyer-seller relationship makeover needs something different

Today, sales has less direct access to the B2B buyers and accounts they must identify, qualify, and win as customers. In fact, according to Gartner, B2B pros spend only 17% of their buying journey with vendor sales pros. And this is combined time — not just with the chosen vendor! This all translates into Marketing’s, Customer Success,’ and other functions’ requirement to play a larger, more initiative-taking role in the revenue- and customer-generation effort. Generating leads and supporting sales is not enough with today’s reality. 

This means our customer, marketing, and sales systems of record the last decade-plus must do more. Consequently, there’s a huge opportunity for evolved types of systems (and set of providers) to play a bigger role. Many providers see these market shifts as an opportunity to broaden their product visions. Rather than developing systems for singular functions, they’re gearing up to become the B2B revenue system of record. In reality, and as we have learned in other technology markets, it will take five to seven years (or more) to develop technology that can support the evolving buyer- and account-centric approaches that today’s buyers demand. But the flag is up and the race is on. 

Sizing Up the Race for the Next B2B Revenue Platform

The technology and platform options for B2B sales, marketing and customer pros are diverse. And as stated earlier no one solution can deliver what’s required today, nor in the future. But gaining an understanding of the different options and where they fit, both today and leaning forward, is essential for delivering on our customer- and revenue-generation mission. 

Let’s take a look at the platforms vying for top billing in today’s B2B revenue stack. Note, this is a not a deep vendor-to-vendor comparison but a look across the B2B landscape to gain a sense of perspective. And we recognize more categories can be added to this positioning list. The mission here is to simply provide context of what’s happening in the market.

Lastly, this underlines the critical need to create and fund talent in the area of revenue and data operations (marketing, sales and customer success) — talent that can align technology, systems, data and processes with your revenue and business goals. 

  • Marketing Automation (MA) platforms. This large group of providers, who once were the center of B2B marketing stacks and demand gen marketing activity, have decreased in popularity. This is largely because their legacy lead-centric architecture doesn’t align well with the full customer lifecycle and prevailing account-centric requirements. In addition, with the acquisition of the major MA platforms by enterprise software players, namely Salesforce, Oracle, and Adobe, innovation has not kept pace with today’s rapidly changing needs discussed earlier in this article. Primary user = marketing. 
  • Account-Based Marketing (ABM) platforms. Because of the popularity of ABM, this category of providers is broad and deep. Vendors that play a role in developing and executing account-based strategies and campaigns are categorized into this group; in other words, most ABM solutions support only some individual elements of ABM strategies and, therefore, must be cobbled together with other systems and platforms. The providers include the predictive and intent solutions required for account-based GTM strategies, the original account-based advertising solutions providers, and the hundreds of providers that deliver account-based demand generation tools, campaigns, and data. Primary user = marketing with sales access. 
  • Customer Relationship Management (CRM) platforms. With deep roots in managing and tracking sales organizations and creating a single view of the customer, CRM is the sales system used by every B2B team today. Today, we have all-in-one CRM platforms (sales, marketing, customer success/service, data clouds, etc.) and industry-specific platforms that focus on the requirements and nuances of vertical markets. But like MAPs, those major players have consolidated the CRM category, hindering the innovation required to keep up with evolving strategies. This has opened the door and requirements for additional systems to play a role in revenue and customer generation. Primary user = sales with marketing and customer success. 
  • Sales Engagement platforms. Focused on solving the huge sales productivity challenge, sales engagement platforms work alongside existing CRM and email systems to streamline the ways sales communicates with prospects (email to voice to social, for example). The value and promise of these platforms are increased sales productivity via streamlined process, tracking and analysis to deliver more impact at a time when sales has less and less access to buyers. Primary user = sales. 
  • Customer Success (Management) platforms. These applications, with roots in the SaaS/subscription business environment, help customer success teams to manage existing customer relationships. The software relies on pulling data from other systems like email, CRM, live chat, product utilization, and customer satisfaction-scoring systems to understand a customer’s current status, adoption, and likelihood to churn or renew their agreement. The rise of these platforms is directly correlated to the need to increase customer stickiness and lifetime value (LTV). Primary user = customer success.
  • Customer Data (CDP) platforms.  According to the CDP Institute (yes, there is such a thing and it’s pretty informative), CDPs are “packaged software that creates a persistent, unified customer database that is accessible to other systems…It centralizes customer data from all sources and then makes this data available to other systems for marketing campaigns, customer service and all customer experience initiatives.” Primary users = marketing and data teams. 
  • Demand platforms. This is also a broad category, primarily made up of media, marketing services and demand gen providers who are developing technology to migrate from a services-based offering to a SaaS-based subscription model. They are offering some mix of SaaS-based tools, analytics, and data with the promise of making third-party demand gen more efficient, more effective, and more predictable. Primary user = marketing. 
  • Data and Intelligence platforms. There are hundreds of data providers, from sophisticated multi-billion organizations to niche solutions offering access to B2B data. These providers typically offer access to data sets, contact and account records for enhancement, and predictive and intent data with the promise of making data science teams more valuable, sales and marketing more productive, and customer and prospect campaigns more intelligent. Many of these providers also compile data from multiple sources and turn it into intelligence because most teams don’t have the time, resources, or talents in house. Primary users = marketing, sales, and data teams. 

Understand the field before betting on the winner

2023 is just around the corner and the platform positioning and road maps are expanding rapidly. As you lock in your 2023 GTM strategies and business goals, now is the perfect time to take inventory of your systems and processes, identify your needs and gaps and understand the revenue technology landscape.

The good news is there are both incumbent and emerging options. The challenge in this positioning battle is understanding what’s right for your business, what’s real, and what’s next.


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


About The Author

How to turn the great buyer resignation into B2B career

Scott Vaughan is a B2B CMO and go-to-market leader. After several CMO and business leadership roles, Scott is now an active advisor and consultant working with CMO, CXOs, Founders, and investors on business, marketing, product, and GTM strategies. He thrives in the B2B SaaS, tech, marketing, and revenue world.

His passion is fueled by working in-market to create new levels of business and customer value for B2B organizations. His approach is influenced and driven by his diverse experience as a marketing leader, revenue driver, executive, market evangelist, speaker, and writer on all things marketing, technology, and business. He is drawn to disruptive solutions and to dynamic companies that need to transform.

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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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