What is a Customer Data Platform (CDP)?
The CDP market can be confusing mix of capabilities and claims. Here we break through the noise, empowering you with the knowledge needed to make the best decisions in this dynamic landscape.
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Overview
A Customer Data Platform (CDP) helps brands unify all available customer data to power personalized experiences.
Today’s interactions are increasingly complex, both in-store and online, and adjusting the right message for the right customer is critical to the success of the customer experience as a whole.
Marketers tend to view the marketplace based on the channel of interaction (email, in-person, SMS, Facebook, etc.) but from the consumer standpoint (the purchaser, the patient, the consumer) it becomes obvious that the entire experience is as important as any of the channels where an interaction takes place. Throughout that process, a consumer has researched multiple avenues, and taken steps to decide on a purchase or course of action. If the brand delivers the customer experience well at each step in the journey, consumers feel the message is more targeted to them on a personal level. He or she may wonder how personalization is achieved in a complex landscape for millions of consumers.
By unifying all available data, building a Golden Record and creating and activating dynamic segments at scale, a CDP powers omnichannel personalization at scale.
Additional Resources
Elevate Your Personalization Efforts
10 Questions to Ask a CDP Vendor
Solving Marketing Challenges with a Customer Data Platform
Defining a Customer Data Platform
What is a Customer Data Platform?
A customer data platform (CDP) is a software application that supports marketing, customer experience (CX) and AI use cases by unifying a company’s customer data from marketing and other channels. The CDP Institute defines a customer data platform as packaged software that creates a persistent, unified customer database that is accessible to other systems. Unlike a data warehouse or data lake, a CDP is usually bought and controlled by marketers or business users.
A customer data platform creates a comprehensive view of each customer by capturing data from multiple systems, linking information related to each customer and storing this information to track customer behavior over time. Data stored in the CDP can be used by other systems for analysis and to manage customer interactions. The CDP restructures the data, adds calculated values and shares the results in formats other systems can accept. These features distinguish a customer data platform from other systems that work primarily with their own data, store limited details for limited periods, do not maintain a permanent database or systems that directly interact with customers.
A CDP can manage any type of customer data. It enables an operational data environment that ingests an enterprise’s data from all sources – whether batch or streaming, internal or external, structured or unstructured, transactional or demographic, personal or general – that provides an always on, always updated unified customer profile (also known as a Golden Record) and makes it continually available at low latency to all touchpoints and users across the enterprise.
A complete, robust customer data platform will satisfy any use case for interacting with a customer, household or business entity with high ROI and low TCO. Cleansed and ready-to-use real-time data and flexible, re-usable segmentation support real-time interactions, personalization, product recommendations and other last-mile interactions with the customer. A complete CDP may be used as a standalone, closed-loop system, and in concert with an existing organization’s MarTech stack, integrating with any customer-facing technology such as CRMs, ESPs, DXPs, DMPs and MMHs.
At its core a customer data platform should:
- Ingest all messy customer data and fix it, as data is ingested into the system;
- Resolve identities at individual and relationship levels to create a unified customer profile that is accurate and always ready for business use;
- Empower business users to visualize customer data, build reusable segments without code, and activate it in different channels and CX programs; and
- Make the unified profile available to drive any enterprise application.
A CDP ingests data from any source, creates a unified customer profile, and makes it accessible to any channel.
What Capabilities to Look for in a CDP
A complete, robust CDP is the foundation for great customer experience across all customer journeys – marketing, service and support – and an indispensable solution for building long-lasting brand loyalty and driving tangible revenue growth. Here are the core capabilites that should be included in all CDPs.
Automated Data Ingestion & Data Quality
Seamless data integration and superior data quality are must-haves for a customer data platform. A CDP must welcome any and all data; data from every source and every type all hold important clues about a customer’s behaviors, preference and patterns – which paints a complete picture of an individual customer.
Volume alone, however, is not enough to ensure an accurate unified customer profile, which is where automated tasks come into play for managing even the most complex data. Cleansing, matching, advanced identity resolution, persistent keys and enrichment are all vital for returning a unified profile that combines every customer identifier with complete behavioral and transactional data.
Unification & Identity Resolution
Once a CDP pulls together customer data, it is important to resolve the identities of individuals across devices and channels. Advanced, tunable identity resolution using a combination of deterministic and probabilistic matching should also resolve identities in the context of an individual or household level, business grouping or other entity.
Identity resolution is the process of finding, cleansing, matching, merging and relating all the disparate signals (martech touchpoints, enterprise systems, databases/data lakes) about a customer to produce an accurate, complete and up-to-date view. Identity resolution is instrumental for building a Golden Record which, when updated in real time, provides a deep understanding of a customer’s preferences and behaviors and is the basis for marketers to orchestrate relevant, personalized omnichannel experiences.
Data Observability and Measurement
Managing data quality is one of the most challenging issues facing IT organizations today. At every step of the data lifecycle – entering, storing, associating and managing data – there is a risk of introducing errors. Data quality is paramount to ensure relevant, personalized experiences across an omnichannel customer journey. Ensuring that data is perfected and ready for use is a function of data observability.
Data observability provides marketers confidence that a CDP’s data is in good health, alerting them to any data issue before it impacts customer experience. By alerting marketers and business users to data issues, it gives them the opportunity to tune or fix campaigns and analytics with trusted data.
Dynamic, AI-driven segmentation in a no-code environment is an important customer data platform function for creating meaningful audiences, even amid a constantly changing customer journey and updated Golden Record. Dynamic segmentation maximizes customer lifetime value through insight-driven targeting based on customer behaviors, choices and actions and informed by audience profiles, visualizations and propensity models.
By leveraging generative AI to build segments, marketers have the needed tools at their fingertips to power both proactive and reactive customer engagement across dynamic customer journeys.
A CDP should offer 4 core capabilities: Automated data ingestion and data quality, unification and identity resolution, segmentation and activation, and data observability.
More on CDP Capabilities
What is a Customer 360?
One of the most powerful aspects of a customer data platform is the ability to build and maintain a Customer 360 at the speed of the consumer. The Customer 360 (also known as a Golden Record or Single Customer View) that a customer data platform provides will give a deeper understanding of a customer’s preferences and behaviors. It should include every touchpoint or proxy identity the consumer presents, such as a cookie, a social media handle, device ID, smartwatch, nicknames and all known email and physical addresses.
Each Customer 360 should also include transactional and behavioral data, as well as other triggers. This single central source of customer golden records unifies data across typically siloed internal and external sources, and handles data of every conceivable type, cadence and source. This is radically different from other data sources and elements in the Martech Stack, which are usually channel-specific and narrowly focused, and often are batch-driven or at least much slower to update than customers’ journeys and actions. By having real-time access to customer data, brands can engage in clientelling and create more personalized experiences that meet the customer’s unique needs and preferences.
The transactional data should include the history of all transactions and interactions that a person represented by a proxy identity has had with the brand. Behavioral and transactional data should be integrated into the golden record in real time to ensure that analytics, personalization and engagement are relevant and up to date.
What Are the Benefits of a Customer Data Platform?
We live in an era of extremely high customer expectations. Today, most modern brands know that they need to put the customer at the center. However, despite have having more data and technology, CX is on the decline, according to the Forrester CX Index. There is a huge opportunity for improvement. In fact, brands that get it right can earn $10s to $100s millions for every one-point increase in Forrester’s CX index.
The CDP is the foundation for understanding your customer and increasingly, for powering the AI systems that act on that understanding. It brings together data from all of a brand’s sources, cleanses and resolves it, and makes it available across the enterprise. The result: an actionable, AI-ready view of each customer that enables truly personalized experiences across any channel or touchpoint, whenever and wherever the customer shows up. Companies that achieve personalization at scale see higher satisfaction rates, higher sales conversion, and lower marketing costs. And as agentic AI takes hold — with AI systems autonomously executing across customer journeys — the quality of that data foundation determines whether AI works in your favor or against it.
Redpoint CDP users experience these benefits.
Additional benefits include:
- Increased Efficiency: By bringing together relevant information from a variety of disparate sources vs. managing in silos you can have centralized control of your data. The ability to access and utilize accurate customer information across all areas of the business will save time and require less ongoing maintenance and management from IT.
- AI-Ready Insights and Analytics: A CDP puts unified customer data in the hands of AI and the quality of that data determines the quality of every model, prediction, and action downstream. With AI and machine learning applied to clean, unified profiles, teams get audience segments, predictive recommendations, next-best actions, and real-time insights at a scale no traditional tool can match. No data scientist required.
- Agility and Flexibility: Markets shift, customers change, and AI models need fresh data to stay accurate, requiring a system of record that can adapt with the needs of consumers in real time. A CDP that can work directly with the systems you have in place can take the data in your system and make it actionable, flexible and ready to meet the needs of the market.
- Privacy, Consent, and Compliance: In the era of GDPR, HIPAA, and CCPA, data governance isn’t optional — and it’s foundational to responsible AI. A CDP that embeds consent and preference details into a unified customer profile ensures that personalization, AI-driven or otherwise, respects what customers have actually agreed to.
What Are the Challenges of a Customer Data Platform?
Poor data quality can lead to misunderstandings about customers, households, or businesses. This lack of completeness, accuracy, and trustworthiness undermines everything built on top of it: personalization, AI models, CX initiatives. Blindly assuming that your data is ready for all your use cases because you have a CDP is a costly mistake. Most CDPs don’t handle data quality. They collect and unify data, but they don’t ensure it’s accurate, consistent, or fit for the use cases you’re trying to power.
Data readiness is what closes that gap and it should be a core capability of any CDP worth deploying. Data readiness means more than ingestion. It means continuously validating that data is accurate and fit-for-purpose: standardizing records, correcting errors, resolving identity across sources, and unifying profiles into a customer view you can actually trust. When data readiness is built in, every downstream process — campaigns, AI models, agentic workflows, real-time decisioning — runs on something solid. When it isn’t, the failures compound until they’re impossible to ignore.
What is a Composable Customer Data Platform?
Often when people talk about a composable CDP they’re referring to what’s called a zero-copy data or a data-in-place CDP, which means that your customer data sits in a data cloud. This is a distinction from the CDP having a built-in database in a traditional “packaged” CDP which is typically offered as software-as-a-service (SaaS) where the vendor hosts and maintains your data and builds a Golden Record on your behalf.
A composable CDP that runs in a data cloud and performs core CDP functionality without having to replicate data allows you to control your customer data behind your own security perimeter, while still connecting to all your MarTech touchpoints and enterprise data sources. A composable CDP is about choice, allowing you to bring together best-of-breed capabilities from one or more vendors to create a purpose-built, cohesive platform through API integrations to give you more flexibility and speed-to-value to achieve your unique CX and business use cases. This approach balances control with the flexibility to add or change components as your business and use cases evolve.
Yet composability as a concept has been over-marketed to the point of confusion. When choosing a composable system, it’s important to take a balanced approach. Consider whether the various components you choose will meet your requirements, and not become an operational nightmare. You may end up with one interface to pull in your data, another for identity resolution, a third to create a segment — likely using SQL — a fourth for your reverse ETL and to activate your segment, etc. Not a single application encompasses the whole business function of bringing in your customer data, creating a trusted unified profile and activating it to your end channels in a controlled way.
Some composable CDP vendors focus on one area, versus the entire spectrum of CDP capabilities.
More on Composability
AI, Real-Time Data, and CDPs
How a CDP Powers AI Decision-Making
Every AI decision is only as good as the data behind it. A model that predicts next-best action, a recommendation engine that surfaces the right product, an AI agent that autonomously engages a customer — all of them depend on a complete, accurate, current view of who that customer is. The CDP is where that view is built and maintained.
The fundamental building block is first-party data. Perfecting it through advanced identity resolution, automated data quality, and continuous cleansing at the point of ingestion ensures that AI systems are working with real signals, not noise. When a customer presents across channels with different identifiers, a CDP with robust identity resolution connects those signals into a single, persistent customer context. That’s what AI needs to make a confident decision.
Without it, AI doesn’t fail loudly. It fails in ways that are hard to trace, producing recommendations that miss, predictions that drift, and agentic workflows that act on an incomplete picture of the customer. A CDP that treats data readiness as a core capability, not an afterthought, is what keeps AI systems calibrated as customers change and data volumes grow.
How a CDP Keeps AI Models Accurate and Current
AI models are not static. They degrade when the data they run on falls behind the customers they’re trying to understand. A customer who churned last month, changed addresses last week, or made a high-value purchase yesterday is a different customer than the one your model was trained on. If your CDP isn’t keeping pace, your AI isn’t either.
This is where data freshness becomes a competitive variable. A CDP that continuously ingests, cleanses, and resolves customer data ensures that the profiles feeding your models reflect who customers actually are right now, not who they were at the last batch update. Persistent identity keys maintain continuity across interactions, so behavioral signals accumulate accurately over time rather than fragmenting across duplicate or outdated records.
More Customer Stories
How a CDP Enables Agentic AI Workflows
Agentic AI changes the stakes for customer data. When AI systems are making decisions and taking actions autonomously across customer journeys, the data foundation they run on isn’t just a backend concern. It’s an operational one. An AI agent handling a service interaction, triggering a next-best offer, or routing a customer through an onboarding flow needs accurate, unified, real-time customer context to do it right.
A CDP provides that context. By maintaining a persistent, always-current customer profile, it gives AI agents a reliable source of truth to act on at the moment of interaction. Identity resolution ensures the agent is working with a complete picture of the customer, not a fragmented or duplicate record. Data quality disciplines built into the CDP mean the agent isn’t inheriting upstream errors and amplifying them at scale.
The organizations getting the most out of agentic AI aren’t the ones with the most sophisticated models. They’re the ones whose customer data is clean, unified, and ready to use. The CDP is what makes that possible.
Real-Time Data: Why Speed is a CDP Requirement
Customer context expires fast. A customer browsing a product, opening a service ticket, or responding to an offer is sending a signal that is only actionable in the moment. By the time a batch process catches up, the opportunity has passed or the interaction has already happened without the right context behind it.
Real-time data isn’t a feature. It’s a requirement for any CDP expected to power modern customer engagement and AI-driven decisioning. A CDP that ingests and processes data continuously ensures that every interaction, whether handled by a human, an automated campaign, or an AI agent, is informed by what the customer did moments ago, not days ago.
Many organizations have real-time data within a channel: web personalization that reacts to clicks, email systems that track opens, service platforms that log tickets. What most don’t have is real-time data across the entire customer journey. Without a CDP connecting those signals into a unified, continuously updated profile, each channel is operating with a partial picture. That’s where relevance breaks down, and where AI agents are most likely to act on incomplete context.