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Prepare for CDP implementation using a template for use cases

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Prepare for CDP implementation using a template for use cases

When considering a customer data platform, many people ask questions like:

  • How do I pick the right one?
  • Do I need a data lake along with a CDP?
  • How much of my current tech stack can a CDP replace?

Those are all good questions, but there is no generic answer that will work for every company. It depends on your business model, your current tech stack, and what you want to do with your customer data. In other words, it depends on the use cases.

“But wait,” you say. “What exactly is a customer data platform?” 

The CDP Institute defines a CDP as “packaged software that creates a persistent, unified customer database that is accessible to other systems.” In a perfect world, it creates a single customer view from all your customer data, no matter where that comes from – your email service provider, your store, your fulfillment system, customer web behavior, etc. It stores this information in a format that enables you to act upon that enriched, comprehensive, single view of the customer.

That’s the goal, anyway. In reality, you’re never going to merge all your data. It’s a question of degrees. And that’s okay.

Read next: What is a CDP?

Why do you need a CDP?

A CDP can help you do some very useful stuff.

  • Find out which of your paying subscribers that frequent your website haven’t yet signed up for your email newsletter, allowing you to target them on your website to invite them to sign up.
  • Personalize messages based on interests, order history, or other characteristics.
  • Present better and more targeted cross- and upsell offers.
  • Create new options for advertisers by creating new segments of users based on common interests.

But before you pull out the checkbook, there’s some work to be done, and this applies whether you already have a CDP or are considering one.

Why you need to write CDP use cases

You need to define how you hope this technology is going to help you and your customers. That is, you need to write up your use cases, with as much specificity as possible.

By starting with use cases, you avoid the “bright shiny object” problem and focus on genuine business requirements. Through this process, you’ll discover what functions you need a CDP to perform, and therefore whether you really need one, and you’ll be able to get a better handle on whether it’s worth the investment.

If you already have a CDP, creating a disciplined structure for use case documentation and review will smooth your operations considerably.

There are many ways to write up use cases, but I’ve summarized my recommended structure below to help you get the process started. I say “to get started” because this document is only the beginning. You’ll write up your use cases in the format I outline below as a preliminary matter, to decide if they’re worth pursuing, or to decide if the CDP can meet your requirements. If you decide to go ahead with them, you’ll have to create a much more detailed document when it comes to implementation.

Generic use case template

You should create a form using the elements I list here and ask everyone in your organization to use this form when they propose a new use case. Thinking through each of these elements will help you define exactly what you want to do and how.

Summarize it with a story. Use a very structured format, like this: As a [role], I want to [action] so [result]. For example, “As an email marketing manager, I want to display a sign-up widget for our retirement e-newsletter to everyone who has recently viewed retirement content on the website so I can increase the reach of our retirement e-newsletter.”

Make the business case. Explain why this use case is necessary now, and how it will:

  • increase your revenue,
  • improve customer service,
  • help you discover new (possibly ancillary) product ideas,
  • create better reports, etc.

Refine the summary with specific details

The devil really is in the details when it comes to CDP use cases. If you ask the salesman, “Can your CDP do ____?” the answer will almost surely be yes. It’s only when you dig into the details that you find the limitations and potential problems. 

For the example in the story above, additional details might include the following.

  • What constitutes “retirement content,” and how will the CDP recognize it? (E.g., is it tagged?)
  • Can the CDP react immediately to a tag on the first-page view?
  • What are the specs on the widget?
    • How and where should it be displayed?
    • What words and images should be used?
    • How big is it?
  • Do you want to A/B test different versions of the widget?
  • What is the path for the data to get from the widget into the CDP and into your ESP? Which system is the “source of truth”?
  • How will you measure results?
  • How long should the campaign run?

The more details you provide, the better.

Identify relevant customer data and its source

For some use cases, all you’ll need is data from within the CDP. You might create segments of users based on favorite topics, when they visit the site, or how often. Other use cases will require data from your other systems. Explain that as carefully as you can.

  • If you want to target on-screen messages to people who have marked your e-newsletter as spam (some people do that by mistake), you’ll need data from your ESP.
  • If you want to target customers with high lifetime value, you may need data from your fulfillment system.
  • If you want to evaluate the behavior of prospects who have recently downloaded a whitepaper, you might need an import from Salesforce.

In any case, think about the systems that house the data you need to make this use case successful, and then look carefully at how that data is stored. Will it need to be cleaned up, or transformed in some way? Can you get it in real-time, or does it have to be batched? Does the system from which you want the data have an API, or will you need to build a connection?

Read next: M&T Bank’s use cases for their CDP implementation

Can you re-purpose existing segments?

If you already have a CDP, you may have already defined the right segment of users. If so, list it here.

But be cautious! It’s crucial that you carefully document how a segment is created and what it’s designed for. It’s very easy for a segment name to be misleading. For instance, does the category “all subscribers” include both paid and free e-newsletter signups? Does it include people who have attended webinars? Does the segment “active subscribers” include people in grace, undeliverable orders, or unpaid orders?

Those are simple examples of how messy it can get. Things quickly become far more complicated than that, so make sure segment definitions are well documented.

Success criteria for the CDP

How will you know that this use case did what you wanted it to do? How will you track the actions you are seeking? What reports are required? What metrics constitute a success?

If possible, you also want to specify revenue goals – especially if you’re in the consideration phase and want to determine the potential return on investment of a CDP.

You’ll need more later

That outline is for the consideration phase. That is, do you want to pursue this use case or not, and can the CDP deliver on what you need?

Once it comes to implementation, you’ll need a more extensive template that includes other details, like specific language for an offer, design specifications, internal approvals that are required, and possible follow-on uses.

By taking a disciplined approach to use cases, you will have a much easier time evaluating CDPs, or getting the most out of the one you already have.


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


About The Author

Prepare for CDP implementation using a template for use cases
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.”

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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