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What is a customer data platform (CDP) and why do marketers need one?

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What is a customer data platform (CDP) and why do marketers need one?

A customer data platform, usually called a CDP, is a marketer-managed system designed to collect customer data from all sources, normalize it and build unique, unified profiles of each individual customer. The result is a persistent, unified customer database that shares data with other marketing technology systems.

The idea of a single view of the customer has been on marketers’ wish lists for years. But disruption caused by the global COVID-19 pandemic has raised interest in precisely the types of solutions that CDPs deliver, which includes that single-view of the customer. With pandemic concerns spurring the movement of customer interactions – both B2B and B2C – to digital channels, marketers are increasingly interested in technologies that collect data from those interactions, unify them, deliver insights and enable campaign orchestration.

CDPs enable marketers to create a single view of the customer by gathering data from software deployed
throughout the organization. High expectations, along with the proliferation of possible customer touchpoints, make cross-device IDs and identity resolution — the ability to consolidate and normalize disparate sets of data collected across multiple touchpoints into an individual profile that represents the customer or prospect — critical for helping marketers, sales and service professionals deliver the ideal total customer experience. CDPs offer this consolidation and normalization and also make the data profiles freely available to other systems that deliver campaigns, webpages and other interactions.



What is a customer data platform (CDPs)?

As the marketer appetite for CDPs has grown, existing companies with various backgrounds — from tag management to analytics to data management — have seen the opportunity and refashioned themselves in the CDP mold. Meanwhile, others have started up with the CDP category in mind from the start, and some well-established players have responded to market pressure and developed a CDP capability.

A CDP is not a CRM, DMP or marketing automation platform. A CDP provides a unified, persistent customer database that provides data transparency and granularity at the known, individual level. A CDP can identify customers from many different data sources by stitching together information under a unique, individual identifier. The CDP then stores its own copy of the data.

CDPs also give marketers control over customer data collection, segmentation and orchestration through native (out-of-the-box) integration that minimizes the need for IT or developer involvement. And lastly, CDPs offers data integration of both known and anonymous customer data with any external source or platform, including CRM, point of sale (POS), mobile, transactional, website, email and marketing automation.

We support the CDP Institute’s definition of a “RealCDP,” which requires it be able to do the following five things:

  • Ingest data from any source.
  • Capture full detail of ingested data.
  • Store ingested data indefinitely (subject to privacy constraints).
  • Create unified profiles of identified individuals.
  • Share data with any system that needs it.

Virtually all of the CDP vendors that meet that criteria provide the following core capabilities:

  • Data management (collect, normalize and unify customer data in a persistent database),
    often after IDs have been matched by other systems.
  • Features designed for use by the marketing organization and other departments, without the
    aid of IT or data science resources. (Though some functions, like building connections to other
    platforms and performing sophisticated data modeling, still require additional resources.)
  • Connections to and from all external systems on a vendor-neutral basis.
  • Structured and unstructured data management.
  • Online and offline data management.

CDP vendors differentiate by offering more advanced capabilities that include, but are not limited to, the following:

  • Native identity resolution to stitch customer data snippets from disparate sources.
  • The number and breadth of robust pre-built connectors to other martech systems. The near-universal availability of APIs means connections are always possible (with more or less developer involvement), but offering pre-built, tested integrations adds value.
  • User interface (UI). The vendors differ in the user-friendliness of their interfaces and the methods people use to do things like create segments, view profiles, etc.
  • Analytics, including those powered by machine learning and artificial intelligence, that surface insights, enable journey mapping, audience segmentation and predictive modeling.
  • Orchestration for personalized messaging, dynamic interactions and product/content recommendations.
  • Compliance with vertical industry and international data regulations.

Now, let’s look at the key considerations involved in choosing a CDP.

Customer data management

Data collection and maintenance is a core CDP customer data management platform function. All CDPs provide a central database that collects and integrates personally identifiable customer data across the enterprise.cFrom there, however, CDPs vary in their abilities to manage the following:

  • Data ingestion capabilities: CDPs use various mechanisms to ingest the data that goes into the unified customer profile — mobile SDKs, APIs, Webhooks or built-in connectors to other platforms. Identity resolution: The platform “stitches” together customer data points, such as email addresses, phone numbers, first-party cookies and purchase data, from various channels matching them to create a single customer profile.
  • Identity resolution: The platform “stitches” together customer data points, such as email addresses, phone numbers, first-party cookies and purchase data, from various channels matching them to create a single customer profile. Some players partner with other providers for this capability, while others have their own systems.
  • Online/offline data: The platform leverages identity resolution or an identity graph to stitch together behaviors in order to create a unified profile.
  • Data hygiene: The platform enables users to clean and standardize customer records.
  • Structured/unstructured data: CDPs differ in their capabilities to manage unstructured data (i.e., social media feeds, product photos, barcodes), which may comprise up to 80% of all data by 2025, according to IDG.

The importance of each of these data management capabilities will depend on a particular organization’s business goals, and whether it has a significant mobile presence, direct mail budget or brick-and-mortar stores and/or agents.

Analytics

CDP vendors offer data analytics capabilities that can do some or all of the following: allow marketing end-users to define and create customer segments, track customers across channels and glean insights into customer interest and intent from customer behavior and trends.

The functionality provided can include predictive models, revenue attribution and journey mapping. To one extent or another, many of these capabilities may utilize machine learning or artificial intelligence to surface insights about audiences and proactively offer suggestions about the best next step to move a prospect through their purchase journey.

Orchestration

A select group of CDPs provide campaign management and customer journey orchestration features that enable personalized messaging, dynamic web and email content recommendations, as well as campaigns that trigger targeted ads across multiple channels.

The customer data platform often automates the distribution of marketer-created customer segments on a user-defined schedule to external martech systems such as marketing automation platforms, email service providers (ESPs), or web content management systems for campaign execution.

For example, the CDP could deliver targeted content to a web visitor during a live interaction. To do this, the CDP must accept input about visitor behavior from the customer-facing system, find the customer profile within its database, select the appropriate content and send the results back to the customer-facing system. A customer data platform may also facilitate digital advertising through an audience API that sends customer lists from the CDP to systems (i.e., DMP, DSP, ad exchange) that will use them as advertising audiences.

Data regulation compliance

CDP vendors vary in the support they provide for compliance with the wide range of vertical market and international regulations that safeguard customer data privacy. Some build compliance features into their platforms, while others rely on outside systems. The European Union’s GDPR was implemented in May 2018 and impacts all U.S. marketers and data firms handling European data or serving customers in the EU. Brands marketing to Canadian consumers through email must also comply with the country’s CASL (Canada Anti-Spam
Legislation). Meanwhile, the California Consumer Privacy Act (CCPA) went into effect in January of 2020.

Marketers in the highly regulated healthcare market must follow HIPAA and HITECH regulations. In addition, all organizations that accept, process, store or transmit credit card information must maintain a secure environment that meets Payment Card Industry Data Security Standards (PCI DSS), as well.

Third-party systems integration

CDPs streamline integration of customer data by providing out-of-the-box (or native) connectors for many martech systems, including CRMs, DMPs, marketing automation platforms, DSPs, and campaign analytics and testing tools. Most marketing organizations have assembled a marketing stack that contains many of these types of platforms. But integrating the data that resides in the martech ecosystem is a huge challenge — one that costs U.S. brands millions of dollars annually. The majority of CDPs profiled in this report also provide at least a basic API to enable custom integrations.


What is a customer data platform CDP and why do

Explore platform capabilities from vendors like Blueconic, Tealium, Treasure Data and more in the full MarTech Intelligence Report on customer data platforms.

Click here to download!


What are the benefits of using a CDP?

Marketing executives today are in charge of dozens of martech applications to manage, analyze and act on a growing volume of first-party customer data. But despite increasing efficiency, the emerging martech ecosystem has created problems with data redundancy, accuracy and integration.

Automating customer data accuracy and integration through a CDP can provide numerous benefits to marketers and to other functions across the enterprise.

These include the following:

Expanded enterprise collaboration. A CDP fosters cooperation among siloed groups because it gathers data from throughout the enterprise and supports customer interactions across many touchpoints. The unification of data allows enterprises to see how strategies for audience, customer experience and execution all fit together – and enables audience portability to ensure a more consistent, informed customer experience.

Improved data accessibility. A CDP is a centralized hub that collects and houses customer data from every corner of the enterprise. Pieces of data are normalized and stitched together to build unique, unified profiles of each individual customer. The result is a persistent customer database whose main purpose is to gather and share data more easily and efficiently across the organization

Streamlined systems integration. A CDP unifies data systems across the enterprise, from marketing and customer service, to call centers and payment systems. By creating a single “system of record” for first-party customer data, data redundancies and errors can be minimized, and data can flow more quickly into — and out of — marketing automation platforms, email service providers (ESPs), CRMs and other martech systems.

Increased marketing efficiency. A CDP unifies individual data with unique IDs that create more robust customer records. Many manual tasks are also automated by the CDP, allowing marketers to focus on the creative and analytical tasks they are trained for. The result is more accurate modeling, targeting and personalization in marketing campaigns, and more relevant customer experiences with the brand across channels.

Faster marketing velocity. In many cases, CDPs are “owned” by marketing, minimizing the need for IT or developer intervention to collect, analyze and act upon data. With control in marketers’ hands, the time to segment and build audiences, execute campaigns and analyze results significantly decreases. That said, engineers may still be needed to perform deep data analysis and facilitate integrations. This is especially true as CDPs extend beyond marketing and into sales and service functions.

Stronger regulatory compliance. A CDP creates greater internal control over customer data, streamlining data governance to comply with the many regulations now impacting brands worldwide. Marketers in the healthcare industry must comply with both HIPAA and HITECH regulations. Businesses that handle European data or serve customers in the EU must also comply with GDPR and those dealing with Californians must deal with CCPA
(California Consumer Privacy Act). The majority of CDP vendors are both ISO and SOC certified for best practices in handling personally identifiable information (PII).


About The Author

1641869137 309 Does your marketing team need an SEO platform

Pamela Parker is Research Director at Third Door Media’s Content Studio, where she produces MarTech Intelligence Reports and other in-depth content for digital marketers in conjunction with Search Engine Land and MarTech. Prior to taking on this role at TDM, she served as Content Manager, Senior Editor and Executive Features Editor. Parker is a well-respected authority on digital marketing, having reported and written on the subject since its beginning. She’s a former managing editor of ClickZ and has also worked on the business side helping independent publishers monetize their sites at Federated Media Publishing. Parker earned a master’s degree in journalism from Columbia University.


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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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