Connect with us

MARKETING

How to create a CDP worksheet from your use cases

Published

on

How to create a CDP worksheet from your use cases

The world of marketing technology is often a confusing mess. The services offered by customer data platforms, data management platforms, marketing automation platforms, and email service providers often overlap, and it can be difficult to decide what you need. 

If you’re considering a CDP, there are a lot to choose from, and they come in several different flavors. The unique quirks of any given CDP are usually determined by its origin story. Most CDPs started as something else and tacked on additional services to become full-fledged CDPs. The one that started as an email service provider (ESP) will be a different animal than the one that started as a recommendation engine. They also differ in whether they focus more on B2B, B2C, retail, publishing, etc. 

One way to cut through the fog is to distinguish these services by their back-end and front-end components. 

Back-end vs. front-end

Back-end components include the technical infrastructure and processes that are used to collect, store, harmonize, and manage customer data. This category typically includes data integration, warehousing, governance, and security features. The back-end component is responsible for ensuring that customer data is accurate, complete, and accessible, with the goal of merging disparate records from multiple sources to create a single customer view. 

The front-end component of a CDP can be divided into marketer-facing and customer-facing features. The marketer-facing side would include data visualization and reporting, while the customer-facing side might include recommendation engines, paywall management, and custom content displays. 

Some CDPs are almost exclusively back-end, with almost no customer-facing front-end features. Other CDPs include lots of front-end “activations.” To make it more complicated, all of these functions are available from stand-alone, dedicated services. 

The trick to evaluating a CDP is to figure out which components are necessary for your use cases, and which need to be part of the CDP itself. 

For example, a CDP might have a built-in ESP. That may or may not be a good thing for you. If one of your use cases requires you to send an email the moment a user takes an action on your website, you’ll either need the CDP to be able to send the email, or you’ll need a real-time connection to an external ESP. 

It’s helpful to think of a CDP the way you might think of a vacation resort. The resort owner wants to be able to say that the resort has some activity, like a water slide, so they build a token water slide on the property. It’s not going to be as good as the dedicated water slide down the road, but it’s also not down the road. It’s right there on the resort. 

In the same way, the ESP that’s built into a CDP is probably not going to have as many features as a dedicated ESP, but that doesn’t matter. What matters is which solution fulfills the requirements of your use cases. 

To make it even more complicated, there are a lot of “CDP-like” services that do some of the work of a CDP. 

To navigate this confusing mess, consider a few use cases and see how the back-end vs. front-end metric can help. 

Recommendation engines for content

Adding customized recommendations to an article on your website can enhance a visitor’s experience with your brand and increase page views. 

The functionality required by that use case depends on what data the recommendation engine will use. 

If you want to recommend articles based (at least in part) on which e-newsletters the customer receives, or which products the customer subscribes to, you’ll need a back-end connection with the ESP and/or the fulfillment system, and you’ll need the ability to merge the user’s online profile with that data. But if you only want to make recommendations based on the user’s web behavior, you don’t need that back-end function, and you might not even need a CDP. Many stand-alone recommendation engines can handle that. 

Questions to ask: 

  • Does this use case require back-end data management? 
  • Is the CDP’s front-end function good enough, or do I need a dedicated service? 
  • Does the CDP integrate with that dedicated service? 

“Customers who bought this…”

In the retail space, vendors want to provide product recommendations, which can increase the value of each order. 

If the recommendations are based (at least in part) on the customer’s order history, the recommendation engine needs that back-end data. If the recommendations are simply based on averages across all customers, specific information about the customer’s purchase history is irrelevant. 

Managing a paywall

Publishers who don’t wish to rely exclusively on ad revenue to fund the creation of their content may offer access to premium content for a fee. This requires the creation and maintenance of accounts to manage access to this content. 

In many cases, those accounts will need to be coordinated with other accounts, such as a magazine subscription. For example, a magazine subscriber might get through the paywall for free, or at a discounted rate. In that case, the paywall management system will have to integrate with back-end data from the magazine fulfillment system. 

Landing page optimization

A/B or multivariate landing page tests can dramatically increase the success of an online store, online forms, and e-newsletter sign-up pages. Services that facilitate the creation and deployment of such tests usually do not distinguish between customers and non-customers, and that seems to work for most situations. In those cases, you don’t need a CDP. 

However, if you have reason to believe that your customers are significantly different than the average web visitor, you might need your landing page optimization calculation to show different stats for different groups. 

For example, a website with medical content might have a split audience that includes medical professionals and ordinary citizens. You wouldn’t want the results of an A/B test on a landing page for a report written for doctors to include stats on how everyone else responded. In this case, back-end information on the audience might be crucial. 

Dig deeper: What is a CDP and how does it give marketers the coveted ‘single view’ of their customers?

Surveys

Surveys can help you understand your customers, which can help you provide better service. Many CDPs can manage surveys, but very few CDPs can compete with the functionality of a dedicated survey platform. How does this affect your evaluation of potential CDP vendors? 

Questions to ask: 

  • Will my surveys be enhanced by incorporating back-end customer data? (E.g., not asking things you already know, or asking different questions to different audiences.) 
  • Is it important to be able to extend the survey process over time through progressive profiling? 

Building a worksheet

I hope these examples have prompted you to imagine a worksheet somewhat like this. 

Use case Data  required / Back end functions Front end function / activation Alternative solutions
Display a message to subscribers who are about to expire Import subscriber dataCreate segments of expiring customers Display a message with a link to a custom renewal page only for subscribers who are about to expire.  No 3rd-party solutions will have the subscriber data. 
A/B test product offer pages None. The entire web audience will be split into test panels.  Dynamically change images and text on offer pages for statistical analysis of results.  Optimizely 

This is an overly simple example, but you can use this general idea to customize a worksheet for your specific requirements. 

The key is to start with use cases and think them through in terms of front-end and back-end functions, also considering 3rd-party alternatives. The more your use cases require back-end functions, the more you’re likely to need a CDP. And once you’ve created this document, it will make the RFP/discovery process much easier. 


Get MarTech! Daily. Free. In your inbox.


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

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