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The myth of the single customer record

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Prepare for CDP implementation using a template for use cases

Sellers of Customer Data Platforms (CDP) promise their software will gather data from various applications, and assemble it into a single-source-of-truth “golden record” for each customer. 

It’s a lovely vision, but rarely achieved. And that’s perfectly okay. Most companies won’t achieve the goal of one record for each customer, but will find ways to cope with the limitations that prevent the creation of golden records.

Let’s use this common CDP use case to illustrate the complexity: Identifying customers among the hoards of anonymous visitors to your website. 

It’s a challenge. Anonymity was central to the internet’s design. And while there are lots of ways to identify anonymous website visitors, they all have their limitations. 

Imagine Robert Williams, our leading man and swing dance aficionado, interacts with Ella, publisher of (the fictitious, I believe) Ella’s Swing Dance Magazine.

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Robert meets Ella on his commute to work. She tells him he ought to read her magazine. On his lunch break, Robert searches for the magazine website on the desktop he uses at the office. When Robert’s web browser makes a request to Ella’s Swing Dance Magazine website, Ella’s CDP puts a cookie on that device and creates a user profile. The profile includes the following information: 

Profile 1

IP address: 25.23.108.5
User-Agent: Mozilla/5.0 (Linux NT 10.0)
Referrer: https://www.google.com

The record might also include what pages were visited, and what type of content the visitor seems to prefer. The visitor is still anonymous to Ella’s CDP. The profile is one of the millions of unknown visitors.

When Robert gets home that evening, he types the URL of Ella’s website into his iPad. Her CDP dutifully puts a cookie on that device and creates a new profile. But on this visit, Robert decides to sign up for Ella’s free e-newsletter with one of his junk email addresses. The CDP captures the email address from the form submission and creates a second profile, which has more information than the first. 

Profile 2

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IP address: 32.12.100.21
User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_6)
Referrer: [blank]Email: [email protected]
Name: Bob Williams

However, nothing in this second records enables Ella’s CDP to conclude the records are tied to the same individual. The records were created on different devices at different times, and share no information identifying Robert. 

Two weeks later, Robert and Ella are jitterbugging at Mobtown Ballroom. Ella has a few copies of her magazine, and Robert takes one home. He signs up for a print subscription using one of the blow-in cards. Ella’s fulfillment service dutifully records this new subscriber data, which is then imported into the CDP, creating Robert’s third profile with still more information:

Profile 3

Name: Robert Williams
Address: 123 Main Street
City: Bowie
State: Maryland
Zip: 20715
Phone: (301) 555-1212
Email: [email protected]

This profile has valuable information, including a new email address. But this profile has no data from online activity, so it doesn’t help with online ad targeting or customer journey data.  

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Robert now has three profiles in Ella’s CDP. There’s no way to merge any of them. We know they’re all Robert. The CDP doesn’t.

Fortunately, Ella’s magazine has the good sense to include some special online content for print subscribers as a way to link offline and online behavior. A QR code printed in the magazine allows Robert to view a video on the website about the Travelling Charleston. Robert scans the QR code with his iPad. That takes him to the website, where the CDP recognizes the cookie it put on that device earlier.

Bingo! Now Ella’s CDP can merge the iPad profile (#2) with the subscription information (#3). Several good things happen as a result: 

  1. Robert’s three profiles have been consolidated into two
  2. Robert has become a known user in Ella’s CDP
  3. Ella’s CDP knows that Robert uses two different email addresses
  4. Robert’s subscription information (offline behavior) and the profile created when he accessed Ella’s site from his iPad (online behavior) are now linked. 

The record created from Robert’s desktop remains anonymous.

Read next: 90% of marketers say their CDP doesn’t meet current business needs

Note that, in this scenario, Ella’s CDP has been configured to accept multiple emails in a customer’s profile. Some companies designate the email address as a unique field – allowing only one per profile. In that case, the records would not merge, and Robert’s subscription information would remain in its own profile, not connected to any online activity.

Will Ella’s CDP ever be able to attach Robert’s work computer to his online profile? Maybe. For example, if Robert opens one of Ella’s e-newsletters on his work computer, the CDP might (depending on how strict it is about such things) recognize that as Robert and merge the profiles. 

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Identifying individuals from their online and offline behaviors and creating single records may seem complicated, but it’s quite a bit less confusing than what happens in real life. Consider the complexity added when Robert’s smartphone and home desktop are added to the equation.

Merging records: deterministic vs. probabilistic method. Which is right for you? 

The “golden record” that the CDP salesman is waving in front of you assumes that all these different sources of information can be merged, but they need to have a field in the record to merge on. What’s that going to be?

Most companies opt for an email address as the best piece of personally identifiable information on which to merge records. But as we’ve seen, and as we all know, people have multiple email addresses. They also change over time. 

If you stick with a strictly deterministic matching method, you’ll need to match a unique field (like an email address or a social media account) across multiple profiles to create your “golden record,” and you’ll inevitably leave some information behind.

There are other options. Some CDPs use probabilistic methods to merge profiles. That method enables you to match records that might otherwise remain distinct. But you risk incorrectly merging profiles and creating a customer experience headache.

(Read this article for an in-depth comparison of deterministic and probabilistic matching.) 

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You can’t create a single record for each customer that covers all the chaos and weird realities of how people behave. What you can do, and what you must do, is decide where that matters.

There are use cases where improperly merged profiles yield very bad customer experience outcomes. Stick with deterministic matching in those cases, even though you’re going to lose some of the data on interactions with that customer. You’ll have multiple profiles for some individuals, many of which will remain “unknown.”

Other use cases are far more forgiving. If you want to create a segment of people who share a particular interest, you don’t need to get down to the individual. In these cases, probabilistic methods are sufficient. 

In any event, recognize that “golden records” are a nice idea, but you’ll never actually get there.


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


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About The Author

Greg’s decades-long career in B2B and B2C publishing has included lengthy gigs in editorial, marketing, product development, web development, management, and operations. He’s an expert at bridging the intellectual and cultural divide between technical and creative staff. Working as a consultant, Greg solves technology, strategy, operations, and process problems for publishers. His expertise includes Customer Data Platforms, acquisition and retention, e-commerce, RFPs, fulfillment, and project management. Learn more at krehbielgroup.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.”

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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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