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CRM data is messy, but it can clean itself up

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CRM data is messy, but it can clean itself up

CRM data is messy but it can clean itself up

Lately, there has been a fair number of headlines advocating for cleaner CRM data. It’s true that more flawless CRM data leads to successful personalization marketing. And while most would agree on why you should clean your CRM data, there are divergent views on the how. 

Too many of the articles recommend batch cleansing and periodic hygiene practices. But let’s be real. CRM data is and always will be messy. It’s a lot like your teenage son’s bedroom—you can clean that room from baseboards to the ceiling once a month, once a week, even once a day. The only sure thing is that it will be dirty again, and soon.

Flawless CRM data can’t be achieved by cleaning up database tables through one-time processes, no matter how many of those processes you employ, or how frequently. It can only be achieved by enforcing rigor in a system of record with integrated, repeatable, ongoing data services. Let’s discuss the characteristics that make up a reliable system of record, and why it’s a more effective path to ongoing efficiency and accuracy of CRM data.

Actionable data requires rigor

According to one article, “Getting rid of duplicate data values is the beginning of the cleanup process.” However, that is shortsighted. New customer data is onboarded all the time, which means deleting and merging duplicate data should be a constant value whenever new data is being integrated.  It’s one of the primary drivers when companies consider adding a customer data platform (CDP)—centralizing customer data and automating the rigorous requirements associated with data unification and hygiene. You want your customer data to be accurate and actionable at any given moment, not just the day after your last merge process. 

As an example, let’s imagine you are a women’s beauty retailer offering e-commerce sales as well as storefront locations. If you have customers who purchase online and in-store, they could appear as multiple individuals in your database, depending on how the data is collected. Unifying data across devices and locations ensures a better understanding of customer behavior, and regularly refreshing that data enables you to message based on their most recent purchases—as opposed to a purchase from last week or month when the data was last refreshed.

The most valuable data is data in motion

Most of the CRM data referenced in the article would be characterized as “data at rest” or PII (personal identifiable information) data. PII data is directly associated with a consumer and doesn’t change frequently (e.g., an email address, personal demographics), but when it does the data becomes unusable. The most valuable data in marketing activities isn’t necessarily PII data. It’s data in motion or non-PII data—constantly evolving and profiled in real-time. This can be transactional data, purchase behavior, content consumption and geo-location data. When it comes to understanding customer behavior and intent, non-PII data generates the most accurate signals to determine the next best action for marketers. 

Only a comprehensive system of record like a CDP can safely and effectively graph non-PII data against existing PII records for use in real-time. It’s also necessary if you want to move forward from baseline hygiene and enrich your CRM data with non-PII data that enables you to understand customer interest and intent. Let’s go back to our example. Your beauty brand may have reliable customer data related to purchases made online and in-store, but you don’t know about recent purchases from other beauty brands, or if a customer has also been searching content from other make-up brands or researching hair care products. Data enrichment can help you further tailor marketing content with deeper and more meaningful personalization.

Bridge the gap from data to opportunities

The nature of non-PII data and the complexity of linking it in real-time to a consumer’s profile is why CRM data is and always will be messy. We can’t change how data is generated, in fact, what’s happening most often is we’re creating more devices and customer engagement strategies that in turn generate even more customer data and well, you get the picture. Bringing it back to our teenager example, he’ll continue to accumulate dirty clothes every day. The most efficient solution? Teach him to do his own laundry. You can do the same with your data.

Maintaining the quality and accuracy of CRM data is important, but it is a baseline requirement when it comes to your marketing efforts. Consumer expectations are high when it comes to personalized marketing and the recommendations they receive from your brand. The ability to identify your customers across channels and devices will require highly reliable, real-time insights—insights that can also reveal opportunities to engage through sophisticated AI. This is what the future of customer data management looks like, and why marketers should be focusing their technology conversations on the next generation of data platforms.


About The Author

CRM data is messy but it can clean itself up
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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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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