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5 strategies B2B marketing and sales teams can bank on as markets tighten

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5 strategies B2B marketing and sales teams can bank on as markets tighten

“Efficiency,” “precision,” “agility, “more with less” — these words echo through Zoom calls, board rooms and budget pow-wows as economic uncertainty builds like black ice on a mountain road. Amid tightening markets and increasing KPIs, these words have quickly become imperatives for go-to-market (GTM) teams charged with identifying, engaging, generating and expanding revenue and relationships. 

To help B2B marketing, sales and customer success teams power through the current and foreseeable choppy macroeconomic environment, this article dives into core strategies and tactics to turn GTM efforts into results. This counsel has been gathered from tens of conversations over the past 60 days with GTM leaders on what teams are doing to optimize performance in uncertain times. 

Dig deeper: In this economy CMOs need to spend more on training, not tech

1. Stop chasing leads, double down on customers and top opportunities

First, GTM teams need to stop generating and chasing random leads, accounts and markets with little to no familiarity with your company or solutions. 

Rather, prioritize existing customers to expand relationships. Work to win over prospects and accounts that have been active with your company and in your pipeline. 

Yes, this approach should always be a priority. But right now, there is little choice where buyers, sellers and all players involved in the process are skittish.

2. Surround and co-create with your current customers

The best and most important revenue opportunity already is in your world — your customer base. 

This is not just traditional customer marketing or listening to the voice of the customer (always essential), but a more purposeful, strategic effort to surround your customers and to help them navigate uncertain times. 

Start with tapping into the customer data and account intelligence you have accumulated to understand your customers, their current state and their priorities.

To get in front of customers, leaders can organize an orchestrated tour of customers where executive sponsors, solutions experts and small teams are formed in your company to focus on specific accounts. 

These dedicated tiger teams are deployed to sit down with and listen to customers’ needs, challenges and plans for the road ahead. They are accountable for:

  • Identifying client needs.
  • Determining if, how and when to act.
  • Rallying the right resources.
  • Delivering on client requirements. 

An example deliverable of this high-touch collaboration is using product and engineering know-how to tweak solutions required to help the customer be more efficient or effective. 

This customer co-creation approach not only helps keep customers and become a preferred provider, it also sets up the opportunity to increase revenue and deepen customer relationships. 

3. Review your prospect opportunity pipeline over the past 18 months

With the same vigor and use of data you applied across connecting with customers, GTM teams should prioritize accounts where interest, engagement and past conversations are active or have gone dormant. 

This prospect effort starts with revenue operations, data science, sales and marketing teams coming together to identify the accounts and then develop a strategy to win their business. 

Apply creative thinking and innovative tactics. Be ready to be flexible with customer priorities. Agility can be applied to how customers purchase. Use your solution or add specific resources as part of your relationship agreement to help the company achieve immediate ROI and payback in tight times. 

The focus on top account opportunities outlined above is relentless. It requires disciplined, regular sessions (weekly and fortnightly, for example) to review opportunities, roadblocks and ways to creatively break through. 

Marketing and sales can orchestrate specific roles and plays, much like what GTM teams implement in account-based 1-to-1 motions where resources are maniacally concentrated on a few accounts. 

Operations teams are on the ground monitoring and ensuring key account and buying committee data is used and available to all the players on the team. 

With resources shifted to this more precision GTM approach, marketing can also add always-on programs to listen to customers and top prospect account and buyer activities using intent and first- and third-party data. 

Dig deeper: How ABM strategies can accelerate marketing and sales velocity

4. Strengthen your channel and partner ecosystems

To bring additional resources and trusted expertise to both customers and prospects, organizations can turn to partners with specific expertise to provide strategy and execution support. 

Top partners have relationships with prospects and customers and are often in a strong position to:

  • Understand how to get more out of your solution.
  • Combine your offerings with other providers to develop pin-point solutions.
  • Deliver where companies have gaps. 

In addition, partners are under the same type of urgency to prioritize and look for ways to add more value to existing and new customers. 

Just like the customer engagement strategy outlined earlier, now is the time to prioritize and sit down with like-minded partners to build GTM strategies and co-create solutions to meet specific prospects and customer requirements. 

Many teams are shifting sales and marketing resources to concentrate on forming deeper partnerships and solutions. In the spirit of trust and collaboration partnerships, sharing customer data with your partners to mine for opportunities and craft smarter solutions should be part of the effort.

5. Invest in and utilize AI and buyer and account intelligence to identify and prioritize your GTM focus

Today, GTM teams can tap into useful buyer and account data and AI-driven models available in sales, marketing and revenue tools, platforms and data subscriptions.

For those GTM teams that have already made the investment, now is the time to double down on fully utilizing intelligence in your GTM strategy, outreach and account and buyer identification. If you don’t have the experience or expertise of using data, now is the time to make it happen. 

Let’s start with the GTM teams with access to AI models and account intelligence. This can come from optimizing ABM tools, intent engines or solutions that apply AI models to identify account propensity, priority, fit and timing. 

No longer can we skim the surface. Rather, we must dig in to:

  • Maximize the use of our first and third-party data.
  • Aggregate and mine intent signals across all the sources and tools we are using.
  • Activate this intelligence in-market to identify existing customer and best prospect opportunities. 

For those teams just getting started, it’s a steep climb, no doubt. The positive side is this work will be foundational to creating a high-performing customer and revenue generation machine going forward. 

If you need help and hands to accelerate your effort, both individual consultants and strategic firms can be brought in to provide deep expertise to put intent, propensity models and other forms of buyer and account intelligence to work. 

Don’t stand still, act now

Buckle in. Nobody really knows what we are in for in the coming year. However, history has proven if we focus, remain flexible and act swiftly, the likelihood of success increases. 

The good news is GTM leaders have access to much more intelligence and experience than we’ve had in the past. Now is the time to put it to work.


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


About the author

Scott Vaughan

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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