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Why martech integration needs more than technical skills

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Why martech integration needs more than technical skills

Integration is essential when building and optimizing your marketing stack. It creates a more efficient and effective digital ecosystem data that allows marketers to gain real-time insights into campaign performance while providing a holistic view of customers and marketing efforts. 

Yet for most organizations, martech integration is not a priority — and understandably so. The technical part of the endeavor makes it a resource-intensive and daunting task. On top of this, additional capabilities are required beyond technical readiness to successfully tackle the martech integration challenge. 


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Technical requirements for integration

Integrating your marketing technology stack into a unified digital ecosystem can be challenging and time-consuming. It can also be costly and frustrating without the right martech consultants to lead strategic and tactical road mapping and execution.

But failure to start with integration initiatives now presents many challenges, including technical debt. Technical debt in the case of marketing technology is similar to that of software development projects. 

In both instances, it’s primarily driven by the cost of rework caused by taking the easy road to a “now” outcome instead of using a more reasonable approach that might take longer.

Martech debt can be caused by:

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  • Lack of requirements and resources.
  • Inadequate vendor/product due diligence.
  • Rushing to solve the problem du jour.
  • Poor leadership. 

Martech integration is complex work that requires considerable technical acuity. Furthermore, organizational and martech skills and a culture of experimentation are at the core of successful integration initiatives. 

Let’s explore each of these areas in a bit more detail.

Organizational skills

When I say organizational skills, I’m not talking about time management, analytical thinking, attention to detail, etc. Although these skills are essential, I’m referring to the ability to build, lead, and work on cross-functional teams across an organization.

The most crucial organizational skill concerning martech is cross-departmental collaboration. Forging a mutually beneficial relationship between marketing and IT is vital.

The CMO and CTO didn’t have much in common a decade ago. Today, with the cloud, data-driven marketing, mobile, e-commerce, cybersecurity, and untethered consumers who move faster than every brand they engage with, CMOs often spend more on technology than their CTO colleagues. 

Some 25% of marketing budgets are spent on marketing technology, according to the 2022 Gartner CMO Spend Survey. That’s a considerable investment worthy of some risk management. In other words, developing the organizational skills necessary to build this relationship is an absolute must. 

For example, you may not need the IT department to directly develop or manage martech for your marketing organization. Still, your CTO partner likely has valuable experience and advice that can help you understand how to get it done and avoid the pitfalls that lead to 70% of projects failing to deliver on expectations.

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Martech skills

“74.18% of our participants reported that they already have someone explicitly in charge of marketing technology or plan to have someone within the next 12 months.” This is a key finding from a 2017 Chiefmartec survey of 275 marketing leaders.

Fast-forward some five years, and a Linkedin search of people in senior marketing technology roles returned over 5,000 people globally, with more than 3,000 in the United States alone. Some individuals carried multiple titles and were not fully dedicated to the martech role. Still, there’s no denying that the martech lead has become essential in businesses worldwide.

The martech leader is a critical resource for the CMO to help forge the marketing/IT relationship I mentioned above. Martech leaders need a solid mix of business, marketing, and technology skills. They are agents of change and know how to build relationships across the enterprise. 

Just as important, they have a vision of how business, marketing, and technology align to drive measurable business outcomes.

Read next: Aggregation is key to the new Martech Map

Culture of experimentation

Continuous innovation is rooted in experimentation

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Paraphrasing from Stefan Thomke’s excellent book, “Experimentation Works: The Surprising Power of Business Experiments,” the most successful companies: 

  • Ensure the entire organization is steeped in the value of experimentation.
  • Has the required capabilities to think experimentally.
  • Is empowered to proceed under the mentality that “everything is a test.”

Our experiences during the recent pandemic prove that this model is, at the very least, worthy of exploration. 

COVID-19 sped up technology adoption by at least one order of magnitude and spurred unprecedented e-commerce growth worldwide. E-commerce sales increased by 43% in 2020, the first year of the pandemic, according to the 2020 Annual Retail Trade Survey (ARTS). It’s safe to say this e-commerce explosion was partly due to a tremendous surge in experimentation.

Many corporations embrace team experimentation, especially in software development. Team experiments help you discover new strategies or validate existing ones. Google recommends the following practices to help you improve team experimentation:

Empower teams. Get out of the way, and allow teams to work on new ideas to surface and achieve business goals that solve significant problems.

Provide information and context. Measuring organizational outcomes provides information critical to making the best decisions so teams can achieve expected results and solve problems. Providing teams with information and context lets teams make informed decisions about the right work.

Leave the details to the team. In the highest-performing teams and organizations, teams are allowed to make informed decisions about the tools and technologies they use. Understand and acknowledge that they are the experts, and empower them to change stories, specifications, and technologies when they decide it’s appropriate. 

Wrapping up

Strategically integrating disconnected and divergent martech tools into a unified digital ecosystem should be prioritized. This allows organizations to take full advantage of marketing technology investments and help reduce the risk of martech debt. 

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But martech integration needs more than technical skills. Beyond the tech chops required to harness the power of APIs and other integration tools, additional skills and cultural considerations are necessary for success. 


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


About The Author

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Gene has been a Martech Healer for over three decades, inventing the future while helping organizations and leaders ‘Ride the Crest of Change.’ A serial entrepreneur since his first newspaper delivery start-up, Gene developed early innovations in social media networks, digital-out-of-home narrowcasting, and SMS mobile marketing. He currently serves as the president and chief strategy officer at GeekHive, a New York-based marketing technology consultancy helping clients maximize their investments 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.”

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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.”

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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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