What’s a Customer Data Platform?                       The Comprehensive Guide

All your questions about Customer Data Platforms (CDPs) answered along with common CDP use cases, measuring return-on-investment, and strategies for implementing the technology across your organization.

The Customer Data Platform industry is an evolving space. There are over 100 vendors with varied offerings: some were purpose-built, some were built through acquisitions, and some are part of larger marketing clouds. That’s why you’ll find a lot of different definitions of what the technology is, and you’ll discover a lot of overlapping value propositions achieved by wildly different means. This has made it challenging for buyers to get a clear sense of the industry.

The CDP Institute, which defines a Customer Data Platform as “a packaged software that creates a persistent, unified customer database that is accessible to other systems” suffers from a similar need for vagueness. Why are we spending so much time on the (lack of a) definition for a CDP? Taking a macro approach of the industry shows just how important it is for you and your team to know what you need to achieve with your customer data before you get into requests for proposals and proofs of concepts. There’s a wealth of possibilities that come from unifying customer data in real time, and a simple definition masks that potential.

Based on these definitions, you’d be forgiven for thinking a CDP is merely another type of structured database, and it may tempt crafty IT teams to try to build it in house. So let’s talk about what we think a CDP should be.

Here’s our definition of a Customer Data Platform:

“A CDP is a technology that collects data in a governed way from sources like web, mobile, in store, call center, and IoT, unifies it to create accurate customer profiles in real time, then makes it accessible to and actionable for other tools and technology.”

A Customer Data Platform offers a balance of speed and agility— unifying customer data into profiles in real time— and stability— providing a single source of truth for your customer engagement tools.

A critical element of investing in a CDP is understanding and documenting how you plan to use it.  Defining use cases upfront will help your organization align around a goal, process, and outcomes.  From getting buy-in from executives early on and helping stage proofs-of-concept with outside teams to dividing up responsibilities and delivering quick ROI, your first use cases will shape the perception of your CDP initiative across the organization.  There’s no single “correct” first use case; where you begin depends on the specific business challenges your company is trying to solve. 

Understanding the expected outcomes and how you’ll get there from an early stage is important for a product with as much potential and flexibility as a Customer Data Platform. 

Below we show a maturity curve and use cases that are common at each of the stages. Again, your organization may look different – but this is a helpful framework to get started if you need help. 

Maturity Curve Use Case Framework:

As customer experience continues to evolve and becomes more digitally fragmented, achieving a single view of your buyer will only become more difficult. If customer experience matters for your business, in addition to becoming more efficient, and improving revenue – a CDP is probably going to be a helpful addition.  Now, a small mom-and-pop store with just a Facebook page aren’t going to need (or have the ability to manage) a CDP, but many others—from SMBs to the largest enterprises—can benefit from a CDP.

So, what does the future hold for the Customer Data Platform— and, thus, for the companies who invest in this technology?

Perhaps the most exciting thing to come for the CDP industry is that CDPs will become the de facto standard. By this we mean CDPs are so transformational that, like other cornerstones of the Martech stack, they will be an expected element for many companies in the years to come. Right now, there are people still trying to figure out what the technology is— hopefully this guide has helped in that. Many people still see CDPs as a replacement for a DMP, a personalization tool, or a customer journey tool, but we’re already seeing a shift towards buyers becoming more sophisticated and the technology becoming a standard line item in budgets. In the near future, having a CDP as a foundational part of your technology stack will be no more unique than having a CRM or CMS.

But that doesn’t mean that you should wait to kick off your CDP initiative. Now is the time to act.

Let’s talk about the top reasons that CDPs are no longer just nice-to-have.

The Transformation of Third-Party Cookies

The death of the cookie has been coming for years, but when Chrome ends third-party cookies in 2022, digital marketing will be changed forever. Advertisers are losing the ability to track consumers across multiple domains and measure the ROI of ad spend. It’s a win for consumer privacy but will limit the effectiveness of current-day digital marketing strategies that don’t use a CDP. With a CDP in your tech stack, you’ll have the flexibility to meet the rapid tech and consumer changes around the death of the third-party cookie. Currently, CDPs help provide a more complete view of the data coming from and going into your ad tech. But in the third-party cookie-less future, you’ll want to acquire your current customers in your paid media channels— that is, finding lookalikes. As there will be a lower volume of consumers in these channels, it will raise the bid costs. A CDP can help you find better lookalikes to raise conversion rates in this top-of-funnel channel.

The Emergence of Machine Learning

The era of machine learning is upon us. If you’re not on the path to machine learning insights, you’re falling behind your competitors. As more companies invest in powerful analytical models, the value of those models will depend on the integrity of the data being fed into them. Machine learning will be used to predict behaviors and deliver best-possible next interactions. CDPs will (and some like Tealium AudienceStream™ already do) come with machine learning built-in, allowing marketers to make use of predictive insights with minimal added effort. But beyond built-in models, CDPs will democratize the building of machine learning models by providing an accurate and reliable data foundation to build upon

Privacy and Trust Are Becoming Powerful Differentiators

The consumer backlash against bad actors with data has been fierce the last few years. Companies will need to be more explicit with how data is used, and they’ll need to be better at providing a value exchange. In other words, companies need to instill trust in their customers and make it worth their while to hand over that information. Whether it’s financial incentives like discounts, exclusive offers for “members,” or better personalized experiences, the value of privacy to consumers can’t be overstated.

With the introduction of the California Consumer Privacy Act giving consumers privacy standards like the right to be forgotten, the single view of the customer enabled by a CDP gives you a key point of reference to handle those requests. Think of it as a broker to understand where all of that customer data is being held. As consumers start to put these rights to use, brands that value trust and privacy will prioritize the ability to respond to these requests quickly and professionally.

The Power of Owning Data

Most businesses are just waking up to the power of the data they already have. When combined with the changes to privacy regulations, companies are increasingly looking to take control of their data. Some are already moving in this direction by only working with media agencies and technologies that allow them to own their data in-house. A combination of data-savvy CMOs and CIOs, along with the evolution of data privacy laws, will drive more companies to look to CDPs as a critical component in the initiative to own their data.

That may mean some changes to the CDP industry. Right now, the SaaS model doesn’t always lend itself to vendor neutrality or data ownership. Avoiding vendor lock-in will be key for foundational technologies like the CDP, and simple APIs won’t be enough.

Companies are already feeling the pressure of big Marketing Clouds that want to own everything. While these may be more of a one-way street, CDPs will diverge to develop an interchange that allows for companies to own their data even more fully — perhaps an open data and schema will become standard for the CDP industry?

Adtech any system used to support advertising activities; in particular, systems that work with digital media

Application Program Interface (API) a method for communicating between systems (or between components of the same system) that makes requests (“calls”) for the other system to send data or take an action (cf Webhook)

Artificial Intelligence computer processes that mimic human thought processes

Attributes attributes allow you to define the important characteristics that represent a visitor’s habits, preferences, actions, and engagement with your brand

Attribution the process of estimating the revenue (or other measure) caused by a particular marketing contact (or other interaction with a customer)

Audiences a group of visitor profiles that share a set of attribute conditions. The more conditions you use to create an audience, the more specific your audience.

Batch Processing processing a set of data that is accumulated over time and fed into the system at once, such as a file containing all transactions during the previous day. This precludes immediate response to events reflected in the data, such as someone visiting a website

Behavioral Data data describing individual actions, such as purchases, web page views, and customer service calls; one person be associated with many behaviors of the same type

California Consumer Protection Act (CCPA) a California regulation that restricts how personal data is collected and used; it gives individuals rights to reject commercial use of their data

Client-side Tracking delivery of data is commonly accomplished through tags, one of the most popular ways to transmit data from web pages. This type of tracking involves the user’s browser (client) directly sending data to a server. The method is used for collecting and sharing data from your website to your marketing technology vendors and is referred to as tag management

Consent Management the process of collecting, classifying, retaining, accessing, and updating individual consent for data use under privacy regulations

Consent Management System/Platform software that manages the consent management process. May be a standalone system or part of a larger product such as a CDP

Customer Data Platform a CDP is a technology that collects data in a governed way from sources like web, mobile, in store, call center, and IoT sources, unifies it to create accurate customer profiles in real time, then makes it accessible to and actionable for other tools and technology

Customer Data Supply Chain the collection of tools and strategies that handle customer data standardization and collection, vendor integration and optimization, omnichannel profile enrichment, campaign action triggers, and data management for business intelligence teams

Data Activation making use of data; specifically, sharing customer data with systems that will use it for analytics, personalization, or marketing campaigns

Data Cleansing the process of making data more usable through error correction, standardization, transformations, and other processes. Exact steps will depend on the intended purpose.

Data Enrichment the process of adding new information to customer data, most often by importing third-party data and appending it to existing customer profiles

Data Governance the process of controlling how data is collected and used in a system, with particular focus on ensuring data quality

Data Standardization the process of placing data in a consistent format so that all instances of the same item are the same; can be achieved through rules or a standardized data layer

Data Transformation the process of converting data from one format to another. Enables disparate data to be combined.

Data Warehouse a collection of data copied from company systems, reorganized and often summarized for analysis

Display Advertising web advertising that appears on website or social media pages and is purchased by contract or by bidding on impressions. May be targeted by web sites or by individuals.

First-Party Cookie a web browser cookie set by the domain of the website that sets the cookie

First-Party Data personal data that an organization has acquired directly from an individual

General Data Protection Regulation a European Union regulation that restricts how personal data is collected, used, and protected; it gives individuals rights to consent, review, and demand deletion of personal data

Geofencing targeting of marketing and advertising messages based on the recipient’s passage into or out of a specific physical location, such as entry to a retail store. Sometimes used in combination with data known about an individual.

Geotargeting targeting of marketing and advertising messages based on the recipient’s location, often in combination with other data known about the individual.

Ideal Customer Profile the set of personal data associated with a company’s best customers. Used to define targets for sales and marketing efforts.

Identity Resolution refers to the various ways that customers can engage with your brand anonymously, then associating that behavior back to a known customer. Most sites and apps attempt to keep track of unknown users, such as using cookies, until the user identifies themselves, via logging in or completing a purchase. (for example)

Identity Stitching the process of connecting a personal identifier to an individual through an intermediary personal identifier (e.g., new device linked to an email address provided by a customer; the device is associated with the customer even though the customer has not herself reported the connection).

Ingestion the process of gathering data from one system and loading it into another

Intent Data data that indicates how likely a person is to purchase a particular product. Generally based on behaviors such as store visits, social media comments, and consumption of related web content.

Key Performance Indicator a measure that correlates with achievement of specific business goals. Separate KPIs are often defined for each business project or objective.

Machine Learning automated processes that build predictive models with little human assistance

Multi-Channel Marketing a marketing program where separate campaigns run in different channels (email, web, etc.)

Next Best Action the treatment that a business believes will produce the most desirable result for an individual customer; typically based on a combination of rules and predictive analytics; requires specification of the measure that is desired

Offline Data data collected by physical interaction such as retail purchases, local events, shipments, etc.

Omnichannel Marketing a marketing program where the same campaign lets customers interact in whichever channels they choose

Owned Media marketing messages delivered through a company’s own channels, such as email or website

Paid Media marketing messages that are purchased, such as paid advertising

Persistent ID a personal identifier that does not change over time and thus can be used as a permanent “master” ID. It is linked to other personal identifiers which may change (e.g. postal address).

Personalization creating communications that are tailored to a specific individual based on data about that individual

Personally Identifiable Information (PII) information that can be used to identify a specific individual; same as personal identifier

Real Time responding to an event so quickly that there is no perceptible delay; may refer to the speed of data ingestion, access or decisions

Second-Party Data personal data that an organization has acquired through a direct relationship with the organization that collected it as firstparty data

Server-side Tracking server-side data management, also known as cloud delivery, is when a pixel or tag sends data into your web server (or a different type of server), then your web server passes that data to the destination system/server. This data could be used by a marketing automation platform, analytics provider, personalization tool or another type of execution system.

Single View of the Customer an aggregated, holistic view of the data an organization retains on its customers discernible at the individual level

Structured Data data that is presented and stored in a fixed format where each element is in a specified location, such as the columns of a relational database table or the fields of a data file

Tag Management System a technology that makes it simple for users to implement, manage, and maintain tags on their digital properties with an easy to use web interface.

Third-Party Data personal data that an organization has acquired through a marketplace relationship with an organization that acquired it directly or indirectly

Unstructured Data data that is presented and stored in a format where the elements are not defined, such as a block of text, video, or audio files

Use Case a description of the steps that an agent takes to complete a business task. Used to illustrate the capabilities a system needs to support a task and to illustrate the tasks a system may support.

Visitor Stitching when a CDP automatically combines the attributes from related profiles from different channels into a new master profile that replaces the others

Zero-Party Data any data that a customer proactively and deliberately shares, such as privacy or contact preferences