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Does your organization need a customer data platform or CDP?

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2022 Predictions: Data strategy and privacy

Understanding your current marketing processes, knowing how to measure success and being able to identify where you are looking for improvements, are all critical pieces of the CDP decision-making process. But before embarking on the purchase process, it’s important for your organization to decide if a customer data platform is really a good fit.

Start with a comprehensive self-assessment of your organization’s business needs, staff capabilities, management support and financial resources. The following questions should help you decide.

How do we currently manage customer data?

Fragmented pieces of customer data often reside in silos in marketing, sales, purchasing, customer support and other departments. Does your organization have a system that serves as the ultimate authority on customer profiles? Do you know what customer data it includes? Is third-party anonymous data mixed in? How many applications are in your martech stack? And how does data get from one application to another? Is it transferred in real-time? Every hour? Every day? These are all areas where a CDP can help to standardize and streamline data storage and processing. However, another tool you’re using may already handle some of the CDP functionality you’re seeking.

How efficient are our marketing data processes?

Marketing software applications are supposed to improve data and campaign efficiency. But many times, disparate systems lead to data duplication, lack of standardization and an increase in time-consuming manual tasks. If you find yourself spending more time normalizing data or de-duplicating contact records, and less time executing campaigns or evaluating campaign performance, it might be time to automate data integration.

How would a CDP address our business needs and what are our use cases for the technology?

Virtually all CDPs deliver several core capabilities around data management, but many also provide a wide range of data analytics and orchestration features that address diverse business goals. What would having a single view of your customers do for you? For example, do you want to reduce churn by targeting customers with more relevant offers? Or increase the profitability of customer acquisition efforts by creating more accurate lookalike audiences? Don’t invest in a CDP before developing use cases that demonstrate how adoption will improve marketing performance or reduce costs. The investment should more than pay for itself.

Is your organization ready for a CDP?

Do you have enough clarity on your use cases and customer journeys to enable you to choose the correct solution? How will centralizing your data and audience definition impact your organization? Are you confident that all of the teams that would need to be involved — from IT to marketing to customer service — can be educated on the potential value of a CDP as part of the adoption project? Have you chosen early adopters within the organization that can provide proof points to other users?

What systems would we integrate through the CDP?

The martech stack is getting bigger and more complex for many organizations. Streamlining integration is a core benefit of implementing a CDP, which can normalize data for easier importing and exporting into other systems. As more brands engage in omnichannel marketing through numerous martech apps, creating a unified view of the customer has become critical to marketing success.

How will we define and then benchmark CDP success?

What key performance indicators (KPIs) do you want to measure, and what decisions will you make based on CDP implementation? For example, do you want to decrease data redundancy and track how that impacts the velocity of campaign execution? Or do you want to decrease the time your marketing staff spends on manually transferring data from one system to another? Set business goals in advance to be able to benchmark success later on. More than ever before, businesses seek to quantify the ROI of their martech investments.

Do we have management buy-in?

As with any major organizational investment, management support is essential to CDP success. Begin with small, short-term goals that demonstrate how the CDP is benefiting the business, either through cost savings or revenue gains. The key is to convince senior executives that having a single, unified view of the customer will add to the organization’s bottom line.

Do we need self-serve, full-serve or something in between?

CDPs are typically built for marketing end-users. However, CDPs vary in the scope of their capabilities — and it is important to have some level of ongoing training to use them all. CDP vendors provide varying levels of onboarding, customer support and/or professional services. Make sure you understand what your marketing staff will need to know to effectively use the CDP, or if you lack internal resources, what type of managed services are available?

What is the total cost of ownership?

CDP vendors charge monthly license fees based on the number of data records, events (or customer actions) and applications integrated. There may be additional fees for onboarding, APIs/custom integrations or staff training. Make sure you know your business needs, data volume and how you will need to restructure your systems and staff to enable a CDP’s operations. Being aware of all of these aspects will help you understand the investment your organization will make. Keep in mind, too, that you may see cost savings if the system allows people to work more efficiently.

Customer data platforms: A snapshot

What they are. Customer data platforms, or CDPs, have become more prevalent than ever. These help marketers identify key data points from customers across a variety of platforms, which can help craft cohesive experiences. They are especially hot right now as marketers face increasing pressure to provide a unified experience to customers across many channels. 

Understanding the need. Cisco’s Annual Internet Report found that internet-connected devices are growing at a 10% compound annual growth rate (CAGR) from 2018 to 2023. COVID-19 has only sped up this marketing transformation. Technologies are evolving at a faster rate to connect with customers in an ever-changing world.

Each of these interactions has something important in common: they’re data-rich. Customers are telling brands a little bit about themselves at every touchpoint, which is invaluable data. What’s more, consumers expect companies to use this information to meet their needs.

Why we care. Meeting customer expectations, breaking up these segments, and bringing them together can be demanding for marketers. That’s where CDPs come in. By extracting data from all customer touchpoints — web analytics, CRMs, call analytics, email marketing platforms, and more — brands can overcome the challenges posed by multiple data platforms and use the information to improve customer experiences. 

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


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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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