Connect with us

MARKETING

The right way to select a CDP

Published

on

The right way to select a CDP

At Real Story Group, we’ve been evaluating customer data platform (CDP) technology on behalf of enterprise buyers for more than four years now. Initially, we saw a wave of early adopters making hasty decisions both to license this type of technology and pick a vendor. As you can imagine, many of those early partnerships didn’t work out so well. Now I see a somewhat different phenomenon: enterprises wanting to select a CDP but feeling more cautious about it.

In general, that’s a good thing. You want to make sure you have the right technology, platform fit and supplier fit, especially for something foundational to your stack. So take your time, and do it right. Here’s how.


Get the daily newsletter digital marketers rely on.


Understand the marketplace

It’s an understatement to describe the CDP market as “fragmented.” RSG evaluates nearly three-dozen vendors, with more added each quarter. This breadth speaks to the rising tide of interest that’s floating so many boats and the breadth of capabilities that potentially fall under the CDP label.

The right way to select a CDP
CDP Vendors can be usefully grouped into categories. Source: RSG Vendor evaluations

There are, of course, many ways to slice and dice any tech marketplace. Perhaps the biggest distinction among CDPs is how a platform is more engagement-oriented than data processing-oriented. Or, roughly speaking, a business versus infrastructure platform (more about that below).

We separately break out the biggest “suite-dependent” vendors into a separate category because our research finds they carry the most significant risk for you. This is not unusual in martech.  

Understand your architecture

Indeed, that breadth of potentially available capabilities –  reaching from deep-tunnel data routing, cleaning and transformation, possibly to more front-end services like email messaging and real-time personalization – makes the CDP marketplace unusually wide.  

So what matters here is the specific set of services you want to render unto a CDP, and which you want to (or already) accommodate somewhere else. Consider this handy chart from my colleague Apoorv in his recent MarTech piece.  

The right way to select a CDP
Look at your broader customer data ecosystem to figure out where a packaged CDP will – and will not – fit into your overall portfolio of services. Source: Real Story Group

In that article, Apoorv analyzes various build vs. buy scenarios but concludes that your customer data modernization journey will include ample build and buy for the typical enterprise. Budget accordingly.

Prioritize your scenarios

Explicitly or not, vendors tend to focus on a limited set of use cases imprinted with new technology and then persist as true strengths. It’s not like you can’t stretch a platform to go where it doesn’t want to, but that takes time, money and scarce developer talent. So you want to make sure that your use case priorities align with your CDP vendor’s strengths.  

Below find ten canonical scenarios that RSG uses when critically evaluating CDP solutions.  Again, vendors will argue they’re good at many if not most of these. Pro tip: the typical CDP vendor is only good at three or four. 

1649903887 57 The right way to select a CDP
CDP technology can theoretically address up to ten different business scenarios, though vendors almost always excel at just three or four. Source: Real Story Group

And while you’re at it, make sure to catalog your true internal capacity to leverage this new platform – particularly around customer data unification and identity resolution, which in many cases will need to take place underneath any packaged CDP solution.  

Take an agile approach

CDPs represent modern technology, so you should take a modern, non-waterfall approach when selecting one. You can read more about this agile approach from an earlier column, but for now, I’d like to stress the importance of testing any platform before you go ahead and license it.  

Sometimes, when working with large enterprises, team members are surprised that they can test-drive martech platforms in general and CDPs in particular. Well, you can, and you should! If a vendor pushes back, drop them from your list. We like to structure week-long sprints with ample business and technical training, ideally separately with two finalists – a.k.a., a competitive bake-off.  

This is easier to do with a CDP than you might think, though it does take some work. Compare that level of effort with the cost of making a bad choice. Also, bake-offs are going to foreshadow the potentially ample organizational change you’ll need to effect for success in any CDP deployment.

Negotiate hard

In any martech procurement, you should negotiate early and often, and not just when you’re down to a single finalist (and have therefore lost much leverage). Unfortunately, over the past year at RSG we’ve seen substantial shifts in CDP vendor pricing. The short story is that it’s becoming more complicated and more usage-based.  

This can lead to strange conversations where vendors ask you to calculate fairly fine-grained usage projections long before you’re prepared. As usual, I’ll encourage you to push back. Ask for a menu of prices and flexible fee structures that align more with value than data throughput.  

This is a hot space, so vendors lead with more aggressive pricing. But even more so, in this young market, vendors want to grow quickly and gain share. Negotiate aggressively.
And let me know how it turns out!

Real Story on MarTech is presented through a partnership between MarTech and Real Story Group, a vendor-agnostic research and advisory organization that helps enterprises make better marketing technology stack and platform selection decisions.


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


About The Author

The right way to select a CDP

Tony Byrne is founder of Real Story Group, a technology analyst firm. RSG evaluates martech and CX technologies to assist enterprise tech stack owners. To maintain its strict independence, RSG only works with enterprise technology buyers and never advises vendors.


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