Every company is investing in data, and the reasoning is simple: the more you know about your customers, the more you can build and deliver personalized experiences. Personalization is the key to driving business growth, but actually building and delivering personalized experiences requires a complete view of your customer.
This is the exact reason why every company either has or is in the process of implementing a Customer Data Platform (CDP).
This blog post will cover:
- What is a Customer Data Platform (CDP)?
- What’s the Difference Between a CDP, DMP, DSP, and CRM?
- How Do CDPs Work?
- Benefits of CDPs
- The Different Types of CDPs
- CDP Use Cases
- How Much Do CDPs Cost?
- How Do You Implement a CDP?
- The Best Enterprise Customer Data Platforms
- How to Choose a Customer Data Platform?
What is a Customer Data Platform (CDP)?
A Customer Data Platform, or CDP, is a solution or architecture that enables you to collect, store, model, and activate your customer data. The entire purpose of a CDP is to provide a centralized platform where you can create unified customer profiles and build personalized experiences for your customers.
How Data Flows Through a Customer Data Platform
These platforms are built to help you collect and consolidate your first-party data into a central database, acting as the bridge between data and marketing teams to help solve two key functions:
- Helping your data teams collect, unify, and move data between systems more efficiently
- Enabling your marketing teams to build self-serve audiences for marketing campaigns
2024 CDP Landscape Guide
Evaluating CDPs? Download our 2024 CDP Landscape Guide to learn how the top ten enterprise CDPs compare across the following:
- Products & Features
- Core Capabilities
- CDP Type
- Key Differentiator
- Company Direction
What’s the Difference Between a CDP, DMP, DSP, and CRM?
There are more MarTech tools available on the market today than ever before, and trying to understand the difference between them can be very challenging, so here’s a quick breakdown:
| Platform | Use Case |
|---|---|
| Customer Data Platform (CDP) | Collects and unifies first-party customer data for personalized marketing. |
| Data Management Platform (DMP) | Focuses on third-party data enrichment and data onboarding to ad platforms for better targeting. |
| Demand-Side Platform (DSP) | Automates the buying process of ad space to power programmatic bidding across different media networks and publishers. |
| Customer Relationship Platform (CRM) | Manages customer interactions across various teams like sales, support, service, marketing etc. |
How Do CDPs Work?
At a high level, CDPs provide a central platform or interface where you can easily connect behavioral events, bring in data from other sources, and then route that data to other marketing applications. Architecturally every CDP has four basic components: event tracking, identity resolution, audience management, and data activation.
Event Tracking
Event tracking is the backbone of any CDP. All CDPs provide out-of-the-box software development kits (SDKs) or code snippets that you can implement on your website or mobile app to track user-completed events and traits about your customers. Once you’ve deployed an SDK on your website or mobile app, every time a user takes an action (e.g., add-to-cart), that behavioral data is fired and stored in your CDP or forwarded directly to a downstream destination of your choosing.
How Event Tracking Works
Identity Resolution
Identity resolution is a critical feature of any Customer Data Platform because it allows you to unify different customer datasets and touchpoints across different data sources. This capability ties all of your customer data together, both offline and online, so you can link every interaction and action back to a specific user profile to understand the entire customer journey. CDPs help solve this problem by helping you merge and de-duplicate customer data into rich identity graphs, which you can use to stitch sessions and unique identifiers back to specific individuals or other custom entities like households or subscriptions. For example, if a user visits your website and then returns later and purchases a product, you can use identity resolution to stitch those two sessions together under one unified profile.
How Identity Resolution Works
Audience Management
Without an audience manager, a CDP is just “Customer Data Infrastructure,” so CDPs come equipped with a visual user interface and audience builder. This interface allows you to build and define customer segments and personas without writing SQL, using filters and boolean logic to aggregate customer records based on criteria that you define (e.g., users who abandoned their shopping cart in the last seven days). The entire purpose of this capability is to give your marketing teams self-serve access to customer data so they can create hyper-specific audience segments for marketing campaigns.
How Audience Management Works
Data Activation
CDPs wouldn’t be useful if the data solely stayed in the platform, so CDPs are designed to integrate with various operational tools to power data activation. For many marketers, this includes ad platforms, lifecycle marketing tools, or even CRMs (basically any platform where you interact directly with your customers). The value here is that CDPs automatically integrate with various third-party APIs, so your data team doesn’t have to build and maintain brittle pipelines to try and move data. This means all you have to do is define what data points or attributes you want to sync to your destination, and the CDP will handle the rest.
How Data Activation Works
Benefits of CDPs
Most people don’t realize that many Customer Data platforms were created by accident. Basically, every major CDP vendor available on the market today evolved into the category. Most of the platforms started as CRMs, infrastructure tools, databases, tag managers, email tools, marketing automation systems, or even Reverse ETL platforms. Eventually, all of these SaaS platforms realized the same thing: building and maintaining a persistent customer record is difficult. Subsequently, every platform developed a very similar suite of features, and the CDP category was born.
Before CDPs existed, managing customer data was really difficult. Not only did you have to set up your own internal processes to collect your data, but you also had to ask your data team to build and maintain custom integrations and pipelines to your operational tools to ensure that data was available for your operational use cases.
CDPs are immensely valuable because they solve several challenges:
- Data Unification: Centralizing and unifying your customer data in one platform
- Self-Serve Audiences: Giving your marketers the ability to dynamically build audiences with a no-code UI
- Data Activation: Automatically syncing data to marketing applications (e.g., ad platforms, lifecycle marketing tools, etc.)
- Personalization: Enabling your marketers to build and power personalized customer experiences across any marketing channel
- Team Alignment: Driving better coordination between your data and marketing teams
- Speed and Agility: Allowing your marketers to rapidly launch and test campaigns across different channels
The Diffferent Types of CDPs
Every CDP platform will have some bias or nuance toward a certain industry or use case, but generally, there are five types of CDPs: Traditional CDPs, Composable CDPs, Hybrid CDPs, Infrastructure CDPs, and Marketing Clouds.
| Platform | Description |
|---|---|
| Traditional CDPs | A traditional CDP is a packaged solution that operates by hosting and managing data within its own proprietary system(s). |
| Composable CDPs | A Composable CDP is an unbundled solution that collects, models, and activates customer data from your existing infrastructure. It operates without ever storing your data and integrates with your existing data assets, making it much more flexible and faster to implement. |
| Hybrid CDPs | A hybrid CDP is a mix of the previous two solutions. All of the features of a CDP are bundled into one packaged platform, but the architecture is designed to have some backward compatibility with your data warehouse. |
| Infrastructure CDPs | An Infrastructure CDP acts more like a data management platform. Whereas other CDPs are built to be marketer-friendly, these solutions are built to help data teams solve more upstream use cases like event collection and identity resolution. |
| Marketing Clouds | A Marketing Cloud is an extensive product suite offered by a large software company (e.g., Salesforce, Adobe, Oracle, etc.) These companies bundle various data and marketing products into larger product suites, but they often have limited usability and fail to integrate with tools outside of their specific ecosystem. |
CDP Use Cases
While the lofty promise of Customer 360 is one of the main driving forces for all CDP adoption, mpanies usually choose to implement a CDP for one of the following use cases:
| Use Case | Overview |
|---|---|
| Event Tracking | Capturing and storing behavioral events like page views, purchase events, signups, subscriptions, logins, etc. |
| Identity Resolution | Creating unified customer profiles to better understand how your customers are interacting with your brand via an identity graph. |
| Audience Management | Building audience segments and grouping users based on various attributes like purchase history or specific user traits like age, gender, location, etc. |
| Data Onboarding | Uploading audiences to ad platforms like Google or Facebook so you can retarget, suppress, or identify lookalike audiences. |
| Lifecycle Marketing | Building personalized customer journeys across multiple marketing channels like SMS, email, push, etc. |
| Analytics | Measuring campaign performance across channels by analyzing customer behavior, holdout groups, audience overlaps, or user traits. |
| Journey Building | Building and orchestrating multi-touch marketing campaigns across channels and triggering automated workflows based on specific customer interactions. |
| Data Activation | Sending customer data to your downstream operational tools to empower your business teams like marketing, sales, support, finance, etc. |
How Much Do CDPs Cost?
For the most basic version of a CDP, you can expect to pay between $50,000 and $150,000 annually. For larger companies with more data, this can quickly get into hundreds of thousands or millions of dollars per year. The overall cost is directly linked to two factors:
- Features: the number of features you need/want within your CDP (e.g., event tracking, identity resolution, audiences, journeys, analytics, etc.)
- Data Volume: the number of monthly tracked users (MTUs) stored in your CDP and the number of destinations you send that data to.
If you're an enterprise organization with millions of users, you can expect to pay much more than a small-to-mid-sized business with a few hundred thousand users. This steep cost is one of the main reasons that companies are choosing to adopt a more modular Composable CDP architecture, assembling individual components like event collection or identity resolution around their existing infrastructure rather than buying into an all-in-one platform.
How Do You Implement a CDP?
The “black-box” nature of traditional CDPs makes it difficult to implement because you can’t actually use or test the technology without undergoing a lengthy sales process to scope out your needs and requirements. The actual implementation process of a traditional CDP can take anywhere between 6-12 months, and undergoing a proof-of-concept (POC) is nearly impossible for most CDP vendors because there is quite a lot of engineering work involved in getting these platforms up and running.
To implement and start using a CDP, there are four key steps:
- Define your data collection strategy
- Configure your identity resolution processes
- Build and define the audiences for your marketing campaigns
- Sync your data to your downstream destinations
Keep in mind that traditional CDP architecture makes adapting to dynamic use cases very difficult because they’re designed with a strict event spec that you have to follow, and they have no way of guaranteeing event delivery to your downstream tools if your events fall outside of their spec. Storing data can be equally challenging because most traditional CDPs come with preconceived notions about how you can collect and store data because they each have a unique schema that doesn’t necessarily conform to your specific use cases.
The only solution that’s flexible enough to integrate with your existing data infrastructure and leverage your existing schema is a Composable CDP because you can take advantage of the existing schema that lives in your data warehouse, and you don’t have to re-conform your data to another platform. With technologies like Reverse ETL, you can basically circumvent the entire implementation process and start activating your data immediately.
How to Choose a Customer Data Platform?
To actually select a CDP, you should create a use CDP case roadmap and identify the following:
- Use cases: What is the key problem you’re trying to solve?
- Existing Technologies: What technology do you currently have in place?
- Stakeholders: Who will be using the CDP?
- Existing data assets: What customer data do you already have access to, and what customer data do you need available in your CDP?
- Marketing applications: What marketing applications will your CDP be powering (e.g., where do you need to send data to?)
- KPIs: How will you be measuring the success of your CDP deployment?
Doing this will help you align the technology to your specific business needs.
Choosing a CDP should come down to your specific use case, and you should never buy technology just for the sake of technology. One of the fundamental problems with traditional CDPs is that they come with preconceived notions that inform how you collect and store data.
For example, if you’re a video streaming company, you have to follow the event tracking spec and the schema structure provided by that vendor. Most CDPs only support objects like users and accounts, so if you have custom data science models or other entities like playlists, subscriptions, workspaces, etc., you’ll quickly run into trouble. Anything custom that falls out of the norm is not natively supported, and trying to configure your CDP to enable that type of custom use case is almost impossible.
Traditional CDP vs. Composable CDP (Comparision Guide)
Download our comparison guide to understand exactly where traditional and Composable CDPs differ.
- Event Collection
- Real-Time
- Identity Resolution
- Audience Management
- and more!
The Best Enterprise Customer Data Platforms
There are a lot of enterprise customer data platforms available on the market today, and trying to understand the differences between them is extremely challenging. With that in mind, here’s a quick summation of the top ten CDPs:
| Vendor | CDP Category | Overview |
|---|---|---|
| Hightouch | Composable CDP | Hightouch was founded in 2019 by former Segment engineers. Unlike traditional CDPs, the platform doesn’t store any data, and it integrates with your existing data warehouse. It’s designed to dynamically adapt to unique data and business-specific use cases. |
| Salesforce Data Cloud | Marketing Cloud | Salesforce Data Cloud is the newest offering by Salesforce to tap into the customer data world. This product was launched in 2021 to act as an underlying data layer for the entire Salesforce ecosystem. |
| Adobe Real-Time CDP | Marketing Cloud | Adobe Real-Time CDP was launched in 2021 to pair with Adobe’s existing customer data offerings and to give marketing teams already heavily invested in the Adobe ecosystem an easy way to unify and manage customer profiles. |
| Segment | Traditional CDP | Founded in 2012, Segment is one of the earliest and most well-known CDPs. The platform has a robust event collection framework but has struggled to meet the demands for other marketing-related features. |
| mParticle | Traditional CDP | mParticle specializes in mobile app use cases. Founded in 2012 as a competitor to Segment, it has recently invested heavily in warehouse-centric capabilities like Reverse ETL. |
| Amperity | Traditional CDP | Amperity was founded in 2016 with a focus on identity resolution. The platform has since expanded, and the company is now pushing their new lakehouse architecture. |
| Treasure Data | Traditional CDP | Treasure Data was created as an analytics and engineering platform for data teams in 2011, but pivoted into the CDP space after realizing many of their customers' use cases were marketing-related. |
| RudderStack | Infrastructure CDP | Rudderstack was founded in 2019 as an open-source alternative to Segment’s event collection framework. It has since expanded to help data teams create a solid data foundation for powering downstream operational use cases. |
| SimonData | Composable CDP | Simon Data was founded in 2014 originally as a messaging platform for marketing teams but pivoted into a Composable CDP solution built to run on top of Snowflake. |
| ActionIQ | Hybrid CDP | ActionIQ was founded in 2014 as an alternative to Segment and mParticle. Recently, with the demand for more warehouse-native capabilities, the company has pivoted into a hybrid CDP. |
Closing Thoughts
Every company is converging to a point where they know they need a centralized platform to manage and act on their customer data. However, many companies don’t realize that they already have a data warehouse that is already acting as a single source of truth. This is why leading companies like Bol.com, Zebra, and Chime are turning to the Composable CDP. If you’re looking into CDPs, you should thoroughly evaluate traditional CDPs vs. Composable CDPs.
If you’re interested in learning more about the Composable CDP, book a demo with one of our solution engineers or check out our Composable CDP Hub.
