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What Is a Customer Data Platform? A Guide to CDPs

Technology   |   Paul Warburg   |   Oct 26, 2020

Today’s customers leave digital footprints behind just about every purchase. Any given buyer may start by searching on Google, visiting an eCommerce store, cross-referencing on Amazon or Google Shopping, reviewing the company’s social media channels—and several times back again—before finally making a purchase. 

Gathering this kind of data is certainly helpful. But being able to assign it to a specific prospective or actual customer—or in other words, creating unique customer profiles—tells a much more detailed story. Take the company blog, for example. Instead of simply being able to report the total number of unique visits to the blog, individual customer profiles allow marketers to track what preceded or followed each visit, which can shed light on how impactful the blog might be. 

Building unique customer profiles is no easy feat; customer data platforms (CDP) were designed to make it easier. Read on to learn more about what a customer data platform is, why it matters, and the importance of integrating the system with data preparation. 

What Is a Customer Data Platform?

A customer data platform is software designed to unify and organize customer data so that it can be used to build what modern marketers call the “360º customer profile,” or a complete snapshot of any one customer’s purchase history, preferences, value, etc.

customer data platform

Customer data platforms integrate with other organizational systems, such as customer relationship management (CRM) software or customer support software in order to enrich the customer database with sales cycle data, product usage data, or customer satisfaction data. The more information that marketers can layer in and assign to particular customer profiles, the more nuanced conclusions they can make about customer behavior.

Some of the key capabilities of a customer data platform include: 

  • The ability to collect data on anonymous visitors. Using cookies, organizations can anonymously track which webpages a visitor visit, how much time they spend on each page, etc. Then, once the visitor converts to a customer, all of that information continues to be stored in their profile.
  • The ability to ingest data from a wide variety of sources. A customer data platform should be able to connect to a wide variety of sources, such as transactional and order data, product data, online and offline data, etc. 
  • Features that help construct a customer buying journey. Based on the data, customer data platforms can map the journey of potential customers to understand what makes them convert to customers, or what makes their journey end—both are valuable information.
  • Accessibility for marketers. Under traditional data warehouses for customer data storage, marketers largely relied on IT to access and manage data. A customer data platform should be user-friendly, affording marketers more control in ingesting and overseeing data. 

What Is CDP Data?

Generally, you can find four categories of customer data in a CDP. But that doesn’t mean that there are only four data types to contend with—each category contains data that may come in a variety of different formats and stem from a variety of different sources. The four categories are as follows: 

  • Identification Data. This type of data is typically the foundation of customer profiles because it includes essential demographic and account information. Identification data is important because it can prevent replications, which are often costly mistakes. Identification data can include: 
    • First and last name
    • Address
    • Age
    • Gender
    • Phone number
    • Email address
    • Twitter handle
    • Job title and company name
    • Account numbers/other company information
  • Descriptive Data. This category of data expands the customer profile and builds on the basic identification data. Descriptive data can be company and industry-specific, as well. The goal of descriptive data is to give marketers a more comprehensive picture of the customer by showing them lifestyle information. Descriptive data can include: 
    • Employer 
    • Previous employers
    • Industry information
    • Job level
    • Income
    • Type of home
    • Type of vehicle
    • Marital status
    • Number of children
    • Magazine subscriptions
  • Quantitative Data. This data category includes the numbers and statistics that can be used in analytics and draw conclusions about populations. The goal with quantitative data is to show how a customer interacts with an organization. Quantitative can include: 
    • Number of products purchased
    • Type of products purchased
    • Emails opened
    • Email responses
    • Clicks on ads
    • Website visits
    • Communication dates
    • Customer service details
  • Qualitative Data. Qualitative data goes more in-depth into customer personality, motivations, and opinions. The goal of qualitative data is to develop the customer profile, whether the information is directly related to the organization or not. Qualitative data can include:
    • Why customers choose a product
    • How customers heard about a product or service
    • How customers would rate the product or service
    • Unique consumer habits or preferences  

How a CDP Compares to Similar Systems

At first glance, a customer data platform may sound similar to existing platforms that you have in your organization. Why invest in another software? 

In truth, while a CDP does have overlapping qualities with other platforms, it serves a unique function that can’t be replicated with existing software. Let’s look at side-by-side comparisons of customer data platforms vs. similar solutions:

  • CDP vs. CRM.
    Similar to a CDP, customer relationship management (CRM) software also stores prospective and actual customer data. But whereas the goal of a CDP is analyzing customer behavior, a CRM revolves around the sales cycle. To that end, a CRM only collects information on known customers or leads, whereas a CDP will store data on anonymous visitors. A CRM also can’t ingest offline data like a CDP—in order to include offline data in a CRM, it has to be manually inputted. Finally, users must input individual data entries into a CRM, while a CDP collects multiple data points from a variety of sources.
  • CDP vs. DMP.
    A data management platform (DMP) also collects anonymous visitor data for analysis and organization purposes. But a DMP specializes in different types of customer data. DMPs use cookies, IP addresses, and device information. A DMP is mostly used for advertising information and creating better ads while a CDP influences all aspects of marketing and goes beyond just the marketing team. DMPs also only hold data for a short time, but a CDP can retain data over a long period of time.
  • CDP vs. Data Warehouse.
    Data lakes and enterprise data warehouses may work well for data storage, but they aren’t accessible for marketing teams—data warehouses require technical expertise to set up and maintain. Most notably, a CDP is designed to create actionable insights from customer data while a data warehouse often requires other systems or processes to pull actionable insights. 

The Benefits of a CDP

We’ve already walked through some of the benefits of a customer data platform—the fact that they can build a 360º customer profile or track anonymous visitors, for example—but let’s get more specific. Why are organizations choosing to adopt CDPs? Turns out, it’s not only for the benefit of marketing teams. 

  • A CDP provides a more comprehensive and unique view of a customer.        The data for a CDP comes from a variety of sources, and the software unifies it in an organized manner. The unified data not only helps marketers understand their customers better, but also other customer-focused departments such as sales, product, or customer success.
  • A CDP can democratize data.
    Prior to a CDP, each department across the organization would collect their own versions of customer data to serve their own specific customer analysis. Not only would that lead to repeat work across different departments, but one department could unknowingly discover data that would benefit another without any efficient way to share that information. A CDP democratizes the collection and analytic use of customer data for more widespread customer analytics.
  • A CDP can improve efficiency.
    Without a CDP, some organizations have attempted to solve the same problem using a variety of tools and resources and have spent valuable time maintaining and training employees on this complex system. A CDP is a more efficient solution to build the same unified database. 

The Importance of Data Preparation with CDPs

Data preparation is the process of cleaning and transforming data for analytic use. It’s an essential part of every analytics project—and a CDP is no exception. A CDP gathers data from many different sources, each with its own specific data format and data quality issues. If that data is not properly cleansed, enriched, or standardized, it can lead to faulty customer analytics.

Data must be properly prepared both upon storage in a CDP and, often, once more as analysts transform the data to fit their specific analysis needs. The difficulty is that data preparation is widely known as the most time-consuming part of any analytics project, often requiring up to 80% of total analytic time. In other words, if an analyst’s method of preparing data is inefficient, their entire analytics project won’t be completed quickly, either. 

Designer Cloud and a CDP 

Today’s organizations are turning to modern data preparation platforms to accelerate the data preparation needed for their CDP. Data preparation platforms cater to non-technical users, such as marketers, by giving them all the power of coding languages to transform data at scale, but by way of a visual platform and simple points and clicks. 

Designer Cloud is the industry-leading data preparation platform. Its machine-learning powered platform acts as an invisible hand during the data preparation process, guiding users towards the best possible transformation. Its visual interface automatically surfaces errors, outliers, and missing data, and it allows users to quickly edit or redo any transformation. Finally, it integrates with essential customer applications and can pull in data from anywhere within the organization. 

To see how Designer Cloud can improve data preparation for a CDP, request a demo or get started with Designer Cloud today.