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6 martech contract gotchas you need to be aware of

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6 martech contract gotchas you need to be aware of

Having worked at several organizations and dealt with many more vendors, I’ve seen my share of client-vendor relationships and their associated “gotchas.” 

Contracts are complex for a reason. That’s why martech practitioners are wise to lean on lawyers and buyers during the procurement process. They typically notice terms that could undoubtedly catch business stakeholders off guard.

Remember, all relationships end. It is important to look for thorny issues that can wreak havoc on future plans.

I’ve seen and heard of my share of contract gotchas. Here are some generalizations to look out for.

1. Data

So, you have a great data vendor. You use them to buy contacts and information as well as to enrich what data you’ve already got. 

When you decide to churn from the vendor, does your contract allow you to keep and use the data you’ve pulled into your CRM or other systems after the relationship ends? 

You had better check.

2. Funds

There are many reasons why you would want to give funds in advance to a vendor. Perhaps it pays for search ads or allows your representatives to send gifts to prospective and current customers. 

When you change vendors, will they return unused funds? That may not be a big deal for small sums of money. 

Further, while annoying, processing fees aren’t unheard of. But what happens when a lot of cash is left in the system? 

You had better make sure that you can get that back.

3. Service-level agreements (SLAs)

Your business is important, and your projects are a big deal. Yet, that doesn’t necessarily mean that you’ll get a prompt response to a question or action when something wrong happens. 

That’s where SLAs come in. 

It’s how your vendor tells you they will respond to questions and issues. A higher price point typically will get a client a better SLA that requires the vendor to respond and act more quickly — and more of the time to boot (i.e., 24/7 service vs. standard business hours). 

Make sure that an SLA meets your expectations. 

Further, remember that most of the time, you get what you pay for. So, if you want a better SLA, you may have to pay for it.


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4. Poaching

Clients and vendors alike are always looking for quality people to employ. Sometimes they find them on the other side of the client-vendor relationship. 

Are you OK with them poaching one of your team members? 

If not, this should be discussed and put into writing during the contract negotiation phase, a renewal, or at any time if it is that important.

 I have dealt with organizations that are against anti-poaching clauses to the point that a requirement to have one is a dealbreaker. Sometimes senior leadership or board members are adamant about an individual’s freedom to work where they please — even if one of their organization’s employees departs to work for a customer or vendor. 

5. Freebies

It is not unheard of for vendors to offer their customers freebies. Perhaps they offer a smaller line item to help justify a price increase during a renewal. 

Maybe the company is developing a new product and offers it in its nascent/immature/young stage to customers as a deal sweetener or a way to collect feedback and develop champions for it. 

Will that freemium offer carry over during the next renewal? Your account executive or customer success manager may say it will and even spell that out in an email. 

Then, time goes by. People on both sides of the relationship change or forget details. Company policies change. That said, the wording in a contract or master service agreement won’t change. 

Make sure the terms of freebies or other good deals are put into legally sound writing.

Read next: 24 questions to ask ABM vendors before signing the contract

6. Pricing factors

There are many ways vendors can price out their offerings. For instance, a data broker could charge by the contact engaged by a customer. But what exactly does that mean? 

If a customer buys a contact’s information, that makes sense as counting as one contact. 

What happens if the customer, later on, wants to enrich that contact with updated information? Does that count as a second contact credit used? 

Reasonable minds could justify the affirmative and negative to this question. So, evaluating a pricing factor or how it is measured upfront is vital to determine if that makes sense to your organization. 

Don’t let contract gotchas catch you off-guard 

The above are just a few examples of martech contract gotchas martech practitioners encounter. There is no universal way to address them. Each organization will want to address them differently. The key is to watch for them and work with your colleagues to determine what’s best in that specific situation. Just don’t get caught off-guard.


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


About The Author

Steve Petersen is a marketing technology 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.

Petersen represents his own views, not those of his current or former employers.

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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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