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Big data gives way to more flexibility

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Big data gives way to more flexibility

Big data gives way to more

The customer always comes first. This proverbial adage has been used countless times. Yet, it remains just as important today as before. Consumers continue to shift to digital-first lifestyles at unprecedented rates. In order to get in front of these audiences, brands began to realize that they needed to show they cared. That consumers deserved one-to-one experiences based on their own interests. They deserved individualization.  

The solution to this seemed simple. More data. The bigger the better. While the power and scalability of this Big Data was undeniable in terms of interacting with consumers, it also presented previously unforeseen issues like accessibility and actionability. With all this new data, how would a brand orchestrate and track touchpoints across solutions in order to form actionable insights and generate tangible revenue? The power is there, but harnessing it was still an unknown.  

The Big Data dilemma 

The acceleration to a digital-first culture has certainly increased our ability to be agile. But in this pursuit, we’ve also cultivated complexity. Multiple project management apps, an email vendor, landing page host, direct mail solution, the list goes on. Today, marketing teams work with dozens of different tools to make their lives easier. But as each solution is adopted, the tech web becomes harder to navigate. The data is at our fingertips, but it’s at the expense of cleanliness and effective orchestration between the different systems.  

For many, the problem lies in having too many apps—as many CTOs will tell you. But, if that were the case, things like your smartphone and your laptop would be nearly impossible to navigate. The real issue lies in the connectivity between the marketing tools that brands are using. Personalizing messages and marketing outreach across a consumer’s path to conversion is hard enough. But it’s exponentially compounded when tools, solutions, and data are not in conversation with each other. The goal should not be to shed weight but to create one integrated, dynamic, and accessible view of customer data.  

This desire for better and more actionable insights has led to the emergence of Customer Data Platforms (CDPs), which aim to unify customer and prospect data in one ecosystem. It provides a singular “data vault” to simplify how we reach customers, but it doesn’t solve all issues completely. The next step is to take this data and put it to work. To make it accessible and actionable. To use it to inform more calculated business decisions and craft more individualized marketing campaigns.  

Big Data should flow 

Agglomerating data is a big step. So big, in fact, that many marketers stop here. But this is only half the battle. At this stage, the data is essentially a black box, only seen or understood by a few. In order to unlock its true power, it’s important to make the data accessible and actionable everywhere. Unlocking this data not only makes it easier for teams to share, collaborate, and act on insights it also demystifies the process. This helps marketers learn to ask the right questions when figuring out how to optimize their efforts.  

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To achieve this, it’s important to work with a partner that can play well with the systems that are already in place. If the CDP isn’t able to recognize current solutions being used, then all of the benefits and effectively the point of a CDP are lost. The aim of the CDP is to scale and improve upon what internal teams are already doing while providing them with a uniform look at all customer data.  

How to unlock the value of your data 

We’ve discussed some issues that can arise from Big Data and why it’s important nonetheless. With that in mind, here are a few ways that marketers can use current marketing technologies and strategies in order to make the most of their data.  

Ensure low- or no-code functionality 

One of the more grueling aspects of data management is just that: the management itself. Exporting and importing data, creating charts, pulling specific accounts. It can be daunting. Work with tooling that enables marketers to drag, drop, and autofill many of these elements to empower them to dig into the data more readily.   

Pair first-party data with intent

Both first-party and third-party data can effectively engage and convert target audiences. But relying on one data set over the other would be a mistake. First-party data is an amazing way to find insights about the products or services consumers are most interested in once they come to your site, but it doesn’t extend much beyond that. Enrich your first-party data with third-party to get more insights into consumers’ overall online behaviors, actions, and interests.  

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Invest in cookie-less identity resolution practices

The deprecation of cookies isn’t quite here yet, but it’s an inevitability with Apple and Google’s announcements last year. Because of this, it’s important for marketers to develop a multifaceted approach to identify and engage with customers across all marketing channels and in each stage of the sales funnel. The most powerful way to do this is to continuously and automatically enhance your first-party data with rich, third-party data that tells you how consumer attitudes are shifting in real-time.   

Don’t separate learnings from action

In other words, don’t make things too complicated. CDPs are great at connecting data, but the next step is to get it into the hands of those who will actually be using it. Analytics tools are great for those who delve into them on a daily basis. But the real power comes from taking the data collected within the CDP and making it accessible to all users in a network through the apps and tools that they use most.  

Conclusion 

Data can and should be a great democratizer for marketing teams. Too often, marketers feel that data is inaccessible or confusing. As the shift to digital continues to intensify, a shift also needs to happen within marketing teams. Marketing automation and insights tools can work to “open the curtain” on the truth that the data are actually revealing. With this accessibility, insights can be obtained in a quick and independent way, enabling marketers to pivot and optimize more effectively than ever before. 

To read more about this topic, you can download The Business Case for Data Usability, written by Zeta.

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About The Author

1641995816 281 Big data gives way to more
Zeta Global Holdings Corp. (NYSE: ZETA) is a leading data-driven, cloud-based marketing technology company that empowers enterprises to acquire, grow and retain customers. The Company’s Zeta Marketing Platform (the “ZMP”) is the largest omnichannel marketing platform with identity data at its core. The ZMP analyzes billions of structured and unstructured data points to predict consumer intent by leveraging sophisticated artificial intelligence to personalize experiences at scale. Founded in 2007 by David A. Steinberg and John Sculley, the Company is headquartered in New York City. For more information, please go to www.zetaglobal.com.


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MARKETING

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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