What Is a Customer Data Platform (CDP)?

Creating a good customer experience isn't easy, but if a brand gets it wrong, it can cost them dearly. In fact, nearly three in four consumers are likely to switch to a competitor after one bad experience. This makes it more important than ever for brands to listen to and learn from consumers to create the customer journey their market desires. 

To deliver this customer-centric, end-to-end experience, brands need a single source of truth for their customer data. That’s what a customer data platform, or CDP, is designed for. A CDP unlocks and unifies customer data to generate rich insights that marketers can use to drive engagement and return on investment (ROI).

Many large businesses use sales tools, like a customer relationship management (CRM) platform, to manage all of their customer data. While the principles of CRM technology are foundational for a CDP, a CDP goes beyond the operational functions of a CRM to bring together and analyze customer data. 

Let’s take a look at the benefits, key features, and other important details to consider about CDPs.

What is a CDP?

When selecting a CDP, it’s important to consider the capabilities each solution offers. Enterprise CDPs unify all customer data, use machine learning to perform sophisticated analysis, and provide meaningful reporting to help teams make informed decisions. These capabilities also help teams predict everything from product recommendations to optimal email send times and next-best channel engagement tactics. CDPs are unique in their full-spectrum ability to uncover actionable insights that drive marketing strategy; most CMOs have one on their technology wishlist.

But before we explore CDP features and benefits, let's take a closer look at what exactly qualifies as customer data.

What is customer data?

For many large organizations, the struggle to capture and understand customer data — and then use it to build omnichannel experiences — is real. Our 2019 Customer Experience Trends Report reveals that 83% of marketers feel their customer data lives in unconnected silos, with data from different channels and systems stored in separate servers, clouds, and databases scattered across multiple departments.

To help organizations create personalized customer experiences, a CDP manages a range of customer data including: 

• Demographics 
• Geography 
• Device preference 
• Channel preference 
• Purchase history 
• Most recent browsing and/or email behavior 
• Customer service history 
• Lifetime value 
• Propensity to engage 
• Likelihood to buy 
• Next best product recommendation 
• Next best channel 
• Likelihood to churn
   

A CDP solution employs a unique feature set to unify this customer data and unlock insights that can be used to improve every step of the customer journey.

Customer data platform features

While there are a range of CDP types, they can generally be organized into a few core categories — each with a distinct set of customer data platform features. Organizations that are considering a CDP investment should define their required capabilities and then evaluate each solution based on their unique needs.

CDP functions often cited as crucial for enterprise marketing include: 

• Identity resolution
• Analytics
• Machine learning
• Cohort analysis
• Interactive queries
• Data sharing
   

Let’s take a closer look at each of these features.

Identity resolution

Sales and marketing teams need to attach customer behavior to a single record or profile. But this isn’t possible when there are multiple customer IDs across siloed systems. A CDP uses API-based data connectors to continuously monitor every customer interaction and accurately resolve customer identities. This feature ensures that any relevant behavior is attributed to the correct identity, and teams aren’t sending inconsistent messages to the same customer because of duplicate customer IDs.

Analytics

Out-of-the-box analytics, reporting, and dashboards are crucial tools for both technical and non-technical users. Robust CDP analytics make it possible to gain insights at a glance and simplify complex decisions into actions. Customized analytics can be shaped to meet organizational needs and tie engagement metrics back to what really matters: customer lifetime value.

Machine learning

Machine learning (ML) powers likelihood models, advanced clustering, and recommendations, which enables marketers to drive the right experience for each customer at scale. It helps teams predict when customers will pay full price, segment audiences based on new behaviors, anticipate each customer’s next step, and more. CDP machine learning gives both data scientists and non-technical users the tools they need to deliver on their goals.

Cohort analysis

Cohort analysis makes it possible to gain insights into campaign performance at the cohort level across channels and campaigns. It’s used to focus on selected and previously selected audiences, as well as identify emerging audiences (e.g., Instagram users who sign up for an email newsletter). Cohort analysis can be also useful over time to report on sales, campaign performance, and profile analysis.

Interactive queries

Interactive queries provide full SQL access to cleansed, processed, and enriched data in the CDP. This includes customer summaries, transaction details at the line-level, atomic events, and much more. All data is automatically refreshed daily with zero setup and maintenance.

Data sharing

Data sharing tools allow data from a CDP to be integrated into other organizational processes. This eliminates the costs, headaches, and delays associated with legacy data sharing methods that deliver only slices of old data. Instead, data is immediately available for use by all teams – no transformation, data movement, loading, or reconstruction required.

Integrated data

Most companies have a number of existing databases that need to be integrated into a CDP. Integrated data features combine and organize existing customer data from multiple sources within an organization into a single system.

Real-time connectors

CDPs use real-time connectors to ingest data from different systems and make it usable in the 360º customer profile. Now, when a customer makes a purchase their customer profile is updated immediately, and the next marketing message can be personalized for their relevant channel and future actions.

Benefits of a CDP

In 2022, global online retail sales are projected to grow to 5.4 trillion U.S. dollars. Only brands with advanced customer data capabilities can capture more of this online revenue by creating the personalized digital experiences that customers expect. With a CDP, enterprise organizations can quickly adapt to the changing behavioral landscape and acquire more customers, reduce churn, optimize the digital experience, increase conversion, and grow customer lifetime value.

Providing marketers the opportunity to work with a 360º view of their customers based on data from all relevant sources gives brands many competitive advantages including:

• Improved customer acquisition
• Decreased customer churn
• Reduced total cost of ownership for technology
• Optimized customer experience
• Increased engagement and conversion
• Enhanced customer lifetime value
   

Like many enterprise software spaces, the marketing technology (martech) landscape is vast . . . and growing! With so many solutions, there’s bound to be overlap, making it confusing to know where one stops and the next begins.

Let’s dig into what a CDP is not and how it overlaps with other commonly used tools.

What a CDP is not

CRM, DMP, data lake, CDP . . . it can be hard to tell the difference between all the technology solutions used for managing customer data. Doesn’t a CRM do everything a marketing organization needs? Not necessarily on an enterprise scale. CRMs and other solutions each address a piece of the larger data problem that CMOs need to solve but not always all of them. This is why many brands that have a CRM, data management platform (DMP), and data lake still need a CDP. 

Here are the most common data solutions that brands confuse with a CDP in their decision-making process.

CRM

CRMs have largely been operational tools used by B2B sales teams. As CRMs were reclaimed by B2C businesses in recent years, the need for additional capabilities became clear. That’s when CDPs were born. When it comes down to it, a traditional CRM can’t collect information from multiple channels in real time, as well as use it to inform and deliver relevant content across the customer lifecycle.

CRMs are still a powerful sales tool, but they aren’t designed to unlock and unify data in a way that creates comprehensive customer profiles that businesses need to deliver omnichannel experiences. They also tend to lack the kind of machine learning capabilities necessary to analyze the data they contain. Most brands that use a CRM still find that they need a CDP to support their marketing strategy. As one of the most common solutions confused with a CDP, it’s important to know where a CRM ends and a CDP begins.

DMP

Data management platforms, or DMPs, are another type of enterprise software commonly confused with CDPs. They were originally designed to inform digital ad campaigns, but customer engagement today is about much more than digital ads. DMPs work well with anonymous customer data gathered from cookies, but they aren’t capable of integrating the first-party data needed for the level of identity resolution a CDP provides. 

Because DMPs work with cookies and other anonymous usage data, they also face evolving challenges in the rise of consumer data privacy regulations. For these reasons, many marketers who still choose to use DMPs enhance their ad campaigns with the comprehensive data provided by a CDP.

Data lakes

A data lake is a lot like it sounds: a big body of raw and unstructured data. Data lakes are a great place to stream all available data into one place, but they just don’t make that data usable for marketing teams. Many companies use data lakes to feed, not replace, their CDP. 

In a data lake, customer data is mixed with other complicated datasets a company depends on – like the labyrinth of product data at an e-commerce giant. Organizations with a data lake still need a CDP to make their customer data easy to access, analyze, and act on.

Customer data platform FAQs

Now that the main differences between a CDP and other data solutions are clear, below are common questions that marketers ask about CDPs.

How does a CDP work?

A CDP works a lot like the human brain. It collects and unifies raw data from all available sources, channels, and systems to translate a massive amount of random information into useful insights, predictions, and possible actions. In enterprise software terms, a CDP unlocks and unifies all available customer data to create comprehensive customer profiles that inform 1:1 personalization across every touchpoint of the digital customer journey. 

When a CDP is added to a martech stack, it integrates with all available data sources to create a single source of truth for customer data. This source is organized into 360º customer profiles that are updated in real time and analyzed by machine learning to find deeper insight into customer behaviors that marketers can use to drive engagement and ROI. Ultimately, a CDP uses customer data to give marketing organizations the intelligence they need to deliver consistent and relevant messages across all channels and devices.

Who needs a CDP?

CMOs, SVPs, and marketing directors who want to create personalized omnichannel customer experiences need a CDP. B2C and B2B brands across all industries, especially online retailers, can use a CDP to increase customer engagement, reduce total cost of ownership for marketing technology, and grow their customer lifetime value metrics.

The capabilities of a CDP give every member of the marketing team better insights into how they can improve their part of the customer journey or maximize campaign performance. Any brand that wants clear and actionable insight into their customers, the ability to personalize messaging and offers, or to make better predictions about customer behavior needs a CDP.

How to choose a CDP

When it comes to choosing a CDP, it’s best to consider any essential business requirements, including the needs of a CMO, that can help narrow down the search. Many vendors who call their platform a “CDP” only offer a fraction of the capabilities an enterprise CDP can deliver. They might have some advanced capabilities like tag management or web personalization, but they often don’t support the full feature list that sets enterprise systems apart. One way to avoid these limited systems and find a scalable CDP solution that meets specific objectives is to ask questions like: 

• Does this CDP unify all data — online and offline — to create a comprehensive customer profile? 
• Does this CDP include machine learning and data modeling to make customer data actionable?
• Does this CDP provide robust reporting?
• Does this CDP ensure data quality and identity resolution?
   

If the answer to all of these questions is “yes,” then it’s an enterprise-worthy CDP. If any of them is a “no,” the search must continue.

Interested in learning more about how an enterprise CDP can support your brand as market conditions and customer behaviors change? See why you’ll love Acquia’s customer data platform. 

Additional resources

CDP Use Cases for Retail
Get to Know the 5 Types of CDPs
The Advantages of Identity Resolution
How to Evaluate if You Need a CDP
4 Reasons to Ditch Your Marketing Service Provider for a CDP
Why Customer Data Platforms Will Drive Retail Strategy in 2021