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My Stack is Bigger than Your Stack, So What?

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My Stack is Bigger than Your Stack, So What?

How big should a martech stack be? The answer is, as big as it needs to be, which I know isn’t a helpful answer. On our platform we have almost 1,000 stacks under management, ranging from 10 products to more than 250. Our own stack has 43 and we are a small company with a limited marketing budget.

 It’s virtually impossible to benchmark stacks from a size perspective due to a lack of consistency regarding:

  • The categories to be included  – Only marketing tech or marketing tech + sales tech + adtech (some consider ad tech entirely separate from martech) + service tech + data sources? Note: we see data sources showing up more and more in tech stacks.
  • The types of products included – Generally it’s purchased products, internally developed ones and those acquired and managed by agencies on the company’s behalf. But what about free products? Our data shows most companies don’t bother tracking them because it’s seen as too difficult or unimportant because it doesn’t impact the budget. This is a mistake. Some free products are critically important gems that are important to know about. 
  • How comprehensive it is – Some companies choose to look only at their critical foundational platforms. We, on the other hand, catalog every single piece of technology we use.
  • The scope – Some companies have one comprehensive source of truth (aka stack), while others manage technology at a department, business unit or geographical perspective and manage multiple stacks. We’ve even seen companies building stacks for specific marketing objectives e.g., lead acquisition, engagement etc.

Read next: Here’s how startups and small companies should build their marketing stacks

In building your stack, don’t focus on trying to find a guide to tell you how big your stack should be. Instead work from the ground up:

  1. Establish your foundational technology infrastructure. For most companies this includes:
    • A way to create campaign materials, 
    • A system to be your source-of-truth for data, 
    • A way to manage prospect and customer relationships,
    • A means to acquire and nurture leads and engage customers,
    • One or more systems to support collaboration,
    • Tools to analyze and assess results.
    • Tools to manage assets, budgets and technology, and a platform to facilitate online sales if needed.

You may not need discrete tools for each function, depending on your environment your marketing automation platform may also function as your CRM and email platform.

  1. Consider things beyond core functionality:
    • Suitability for the size and skills of your team. If you choose a product that is too complex than your team can handle, it will never be fully utilized and you will not get enough of a return on your investment.
    • How well everything works together. Can critical data get where it needs to go? Find out if your products can easily integrate before you buy them. Otherwise you will have to develop custom integration code (depending on the system it could be a six-figure cost).
    • Scalability. You should be able to use your foundational elements for 3-5 years. That means they must be able to grow with the company. It’s a huge task to swap systems out, taking from six to 18 months to do. 
    • Cost. It’s important to understand on a product-by-product basis and at the stack level how your purchases factor in and impact customer acquisition costs (CAC).
       
  2. What do you need to achieve your objectives? With more than 9,000 martech products on the market, how do you sort through them? Your marketing goals will focus your efforts in the right place. Also, it’s critically important to consider whether the technology you already have can handle your expected future needs. One of the key contributors to stack bloat is redundant functionality within the stack. This is caused by looking at each set of technology requirements on its own and not considering the stack as a whole.
1656343307 445 My Stack is Bigger than Your Stack So What

Remember, the need to create new campaigns, leverage new channels, improve targeting, etc., means you are going to add more technology to your stack. That’s okay, as long as you keep the CAC impact in mind. 

Is smaller better?

There’s an idea going around that we should all make our stacks smaller via consolidation. The argument is that a smaller stack will be easier to manage and less costly – but will it? Replacing five products with one product doesn’t guarantee easier stack management and lower costs. A new product could add a new level of complexity and require a long implementation and onboarding period and extensive training. It could also cost significantly more than the products that are being replaced.  

Consolidation is a favorite theme of vendors with large multi-function systems that want you to use their product over everything else. There are times when this makes sense, particularly when integrations are involved, but there are plenty of times when it doesn’t. As yet there is no single platform that can deliver the functionality needed across the stack so don’t waste any time thinking about that.   

Consolidation can be needed when a stack gets out of control due to lack of centralized oversight and purchasing. Then bloat becomes obvious through skyrocketing expenses without the ability to demonstrate return on investment. We’ve worked through this process with a number of customers and in every situation it’s because of redundant contracts, products and functionality. If you have processes in place to prevent this, your only risk of bloat is keeping products that didn’t live up to expectations or no longer serve your marketing objectives. This is easily avoided by establishing performance benchmarks and conducting regular stack reviews. 

If we can’t define the optimum size of a tech stack then we certainly can’t look at a stack and say “that needs to be consolidated.”  Stop worrying about stack size, the perfect size for your stack is one that ensures you meet your marketing objectives in a cost-effective way.


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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

Heres how startups and small companies should build their marketing

Anita Brearton is founder and CEO of CabinetM, a marketing technology management platform that helps marketing teams manage the technology they have and find the technology they need. A long-time technology marketer, Anita has led marketing teams from company inception to IPO and acquisition. She is the author of the Attack Your Stack and Merge Your Stacks workbooks that have been written to assist marketing teams in building and managing their technology stacks, a monthly columnist for CMS Wire, speaks frequently about marketing technology, and has been recognized as one of 50 Women You Need to Know in MarTech.

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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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