Connect with us
Cloak And Track Your Affiliate Links With Our User-Friendly Link Cloaking Tool, Try It Free

MARKETING

AI driving an exponential increase in marketing technology solutions

Published

on

AI driving an exponential increase in marketing technology solutions

The martech landscape is expanding and AI is the prime driving force. That’s the topline news from the “Martech 2024” report released today. And, while that will get the headline, the report contains much more.

Since the release of the most recent Martech Landscape in May 2023, 2,042 new marketing technology tools have surfaced, bringing the total to 13,080 — an 18.5% increase. Of those, 1,498 (73%) were AI-based. 

Screenshot 2023 12 05 110428 800x553

“But where did it land?” said Frans Riemersma of Martech Tribe during a joint video conference call with Scott Brinker of ChiefMartec and HubSpot. “And the usual suspect, of course, is content. But the truth is you can build an empire with all the genAI that has been surfacing — and by an empire, I mean, of course, a business.”

Content tools accounted for 34% of all the new AI tools, far ahead of video, the second-place category, which had only 4.85%. U.S. companies were responsible for 61% of these tools — not surprising given that most of the generative AI dynamos, like OpenAI, are based here. Next up was the U.K. at 5.7%, but third place was a big surprise: Iceland — with a population of 373,000 — launched 4.6% of all AI martech tools. That’s significantly ahead of fourth place India (3.5%), whose population is 1.4 billion and which has a significant tech industry. 

Dig deeper: 3 ways email marketers should actually use AI

The global development of these tools shows the desire for solutions that natively understand the place they are being used. 

“These regional products in their particular country…they’re fantastic,” said Brinker. “They’re loved, and part of it is because they understand the culture, they’ve got the right thing in the language, the support is in that language.”

Now that we’ve looked at the headline stuff, let’s take a deep dive into the fascinating body of the report.

The report: A deeper dive

Marketing technology “is a study in contradictions,” according to Brinker and Riemersma. 

In the new report they embrace these contradictions, telling readers that, while they support “discipline and fiscal responsibility” in martech management, failure to innovate might mean “missing out on opportunities for competitive advantage.” By all means, edit your stack meticulously to ensure it meets business value use cases — but sure, spend 5-10% of your time playing with “cool” new tools that don’t yet have a use case. That seems like a lot of time.

Similarly, while you mustn’t be “carried away” by new technology hype cycles, you mustn’t ignore them either. You need to make “deliberate choices” in the realm of technological change, but be agile about implementing them. Be excited by martech innovation, in other words, but be sensible about it.

The growing landscape

Consolidation for the martech space is not in sight, Brinker and Riemersma say. Despite many mergers and acquisitions, and a steadily increasing number of bankruptcies and dissolutions, the exponentially increasing launch of new start-ups powers continuing growth.

It should be observed, of course, that this is almost entirely a cloud-based, subscription-based commercial space. To launch a martech start-up doesn’t require manufacturing, storage and distribution capabilities, or necessarily a workforce; it just requires uploading an app to the cloud. That is surely one reason new start-ups appear at such a startling rate. 

Dig deeper: AI ad spending has skyrocketed this year

As the authors admit, “(i)f we measure by revenue and/or install base, the graph of all martech companies is a ‘long tail’ distribution.” What’s more, focus on the 200 or so leading companies in the space and consolidation can certainly be seen.

Long-tail tools are certainly not under-utilized, however. Based on a survey of over 1,000 real-world stacks, the report finds long-tail tools constitute about half of the solutions portfolios — a proportion that has remained fairly consistent since 2017. The authors see long-tail adoption where users perceive feature gaps — or subpar feature performance — in their core solutions.

Composability and aggregation

The other two trends covered in detail in the report are composability and aggregation. In brief, a composable view of a martech stack means seeing it as a collection of features and functions rather than a collection of software products. A composable “architecture” is one where apps, workflows, customer experiences, etc., are developed using features of multiple products to serve a specific use case.

Indeed, some martech vendors are now describing their own offerings as composable, meaning that their proprietary features are designed to be used in tandem with third-party solutions that integrate with them. This is an evolution of the core-suite-plus-app-marketplace framework.

That framework is what Brinker and Riemersma refer to as “vertical aggregation.” “Horizontal aggregation,” they write, is “a newer model” where aggregation of software is seen not around certain business functions (marketing, sales, etc.) but around a layer of the tech stack. An obvious example is the data layer, fed from numerous sources and consumed by a range of applications. They correctly observe that this has been an important trend over the past year.

Build it yourself

Finally, and consistent with Brinker’s long-time advocacy for the citizen developer, the report detects a nascent trend towards teams creating their own software — a trend that will doubtless be accelerated by support from AI.

So far, the apps that are being created internally may be no more than “simple workflows and automations.” But come the day that app development is so democratized that it will be available to a wide range of users, the software will be a “reflection of the way they want their company to operate and the experiences they want to deliver to customers. This will be a powerful dimension for competitive advantage.”

Constantine von Hoffman contributed to this report.

Get MarTech! Daily. Free. In your inbox.

Source link

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address

MARKETING

YouTube Ad Specs, Sizes, and Examples [2024 Update]

Published

on

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!

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading

MARKETING

Why We Are Always ‘Clicking to Buy’, According to Psychologists

Published

on

Why We Are Always 'Clicking to Buy', According to Psychologists

Amazon pillows.

(more…)

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading

MARKETING

A deeper dive into data, personalization and Copilots

Published

on

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.”

Source link

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading

Trending