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Frequently Asked Questions

Our CDP FAQs Will Help You Get Started

What is a Customer Data Platform (CDP)?

A CDP is an off-the-shelf technology that functions as the brain of an organization’s marketing technology stack. It starts by collecting customer identities and interaction data from all first- and third-party sources (through prebuilt, real-time data connectors), and stitches them together into a single, persistent profile for each customer. It then empowers marketers and other business teams with intelligence by providing a user-friendly interface for analyzing customers, segmenting audiences, and predicting next-best-actions. Finally, the user interface enables automated orchestration of 1:1 personalized journeys across all marketing, CX, and commerce channels, including test design and measurement.

How do CDPs leverage artificial intelligence (AI), machine learning (ML), and other predictive analytics?

CDPs use advanced analytics in four ways:

  1. Offering algorithmic identity resolution methods to deduplicate customer profiles
  2. Offering marketers prebuilt analytic models for improved speed-to-market (e.g. propensity models, churn models, look-alike models, clustering models)
  3. Offering integration of an organization’s homemade model scores for marketers to derive insights, perform segmentation, and scale personalization
  4. Offering algorithmic performance measurement methods that use statistical methodologies to measure campaign lift

CDPs do most or all of the hard work of preparing data for predictive analytics by gathering raw, disparate customer data into a single customer view. Marketers can instantly apply prebuilt predictive algorithms and data scientists can concentrate on building new algorithms, rather than 50+% of their time spent on wrangling data.

How do CDPs enable omnichannel customer experiences?

CDPs support omnichannel experiences in four ways:

  1. Data democracy. The CDP creates operational efficiency by ensuring customer experience stakeholders always have access to clean, up-to-date profiles.
  2. Data-driven insights. The CDP serves as the helpful brain, collecting customer interactions from all marketing, CX, and commerce systems for analyzing customers’ needs and predicting their next-best-actions.
  3. Journey design, orchestration, and measurement. The CDP empowers stakeholders to design omnichannel, fully-automated journeys that dynamically adjust the channel, content, and time that messages are sent based on customer preferences and campaign performance.
  4. Omnichannel execution. Lastly, the CDP brings hundreds of real-time data connectors to activate these journeys, audiences, and offers to final-mile execution tools within marketing, commerce, and CX channels.

How do CDPs support personalization at scale?

CDPs provide marketers with a “personalization brain” that centralizes, democratizes, and automates the personalization process, thanks to the following capabilities: 

  1. Automated data unification. Prebuilt data connectors ingest data from any channel and stitch it together into a persistent, single profile for each customer.
  2. Democratized data access. Business users gain self-service access to data for unimpeded customer analytics, predictive intelligence, and journey design for any and all channels. 
  3. 1:1 offers and recommendations. CDPs include out-of-the-box analytic models to predict products, content, and offers that maximize business outcomes. 
  4. Automated syndication. Prebuilt data connectors are provided (and vendor-maintained) for automated campaign execution across any and all channels.  
  5. Automated measurement and reporting. Easy, business-friendly testing and measurement is included for rapid understanding of which offers resonate most with customers. 

What are the main use cases of a CDP?

CDP use cases can be divided into three categories based on the value they deliver to the business: 

  • Incremental revenue generation. By providing meaningful insight into customer behaviors and preferences, CDPs enable marketers to discover net-new opportunities and act on them quickly. Two common examples include: 1) Detecting at-risk customers and delivering automated messages to prevent churn; and 2) Identifying premier prospects via lookalike models and targeting them via channels like direct mail, digital advertising, or social. 
  • Saving marketing costs. By calculating customer propensities via CDP marketing analytics, you can reduce marketing spend by targeting only customers with a high likelihood of conversion. Two common examples include: 1) Measuring discount sensitivity and minimize sending coupons to customers that don't display a need for them in order to buy; and 2) Driving more efficient prospecting by suppressing existing customers from prospecting campaigns across paid media and direct mail channels. 
  • Increased operational efficiency. By automating data unification and empowering self-service operations, CDPs eliminate operational bottlenecks and reliance on expensive technical professionals. Two common examples include: 1) Creating a single-source-of-truth for customer data where employees can gain accurate customer insights without fears of data inaccuracies, and 2) Providing a user interface that enables scaled customer analytics, audience discovery, campaign list creation, campaign journey design, and campaign measurement.

Should I build or buy a CDP?

Brands with large IT budgets, talented strategists, and masses of engineers may consider building a CDP in-house. If one’s planning and execution phases are successful, an organization may produce a bespoke solution that fits the exact needs of their business and potentially delivers a competitive advantage. 

However, building an enterprise-grade CDP can take 1-2 years (given the complexity of features). And because of the constantly changing nature of customer data, stakeholder needs, and the increasing quantity of execution channels, an in-house option is likely to result in: 

  • Slower time-to-market (given the complexity of solution)
  • Higher cost of ownership (given needed resources for maintenance and enhancement)
  • Higher risks of latency (given need to build and maintain APIs in-house)
  • Sub-optimal data democracy (unless a full-scale, non-technical UI is created)

By contrast, an organization can install a pre-built CDP in just two to three months, and at significantly lower costs and risks. While organizations cannot customize every specification, enterprise CDPs are engineered with flexibility of data storage, data structure, and data syndication. In addition, premium CDP vendors add value by constantly innovating new analytics and personalization features. These solutions can future-proof your investment, by ensuring that the CDP grows and adapts to changing technological and business challenges. 

How do I build a business case for a CDP?

Besides marketing, CDPs have significant impact on IT, analytics, and finance organizations. Buy-in from all parties is essential to success. Thankfully, the right CDP can be positioned as a win-win for all parties. 

Here are five key steps to getting buy-in for a CDP: 

  • Prioritize 7-10 specific use cases. Collaborate with stakeholders to identify the specific objectives they want a CDP to help achieve. Prioritize them based on potential business impact.
  • Demonstrate a CDP’s capabilities. Build familiarity and excitement for the solution by having two or three premier vendors demo their product against key use cases. 
  • Estimate ROI. Estimate the ROI of each use case by asking vendors about their typical return on investment for each case.  
  • Create a strong pitch. Build a pitch deck that is financially-focused and conveys how the CDP bridges the gap to achieving stakeholders’ goals. Include an appendix with supplemental info. 
  • Seek consensus. Present the deck to stakeholders one at a time so you can understand their perspectives, address any concerns, and optimize the pitch deck before presenting to the final decision makers for sign-off.

What is the difference between a CDP and a marketing cloud?

Marketing clouds are essentially a collection of point solutions—e.g. CRM, content management, email service provider, DMP, workflow management, etc.—brought together to form a martech ecosystem (often via acquisitions). Their capabilities are considered ‘final-mile’ since they deliver the final campaigns and messages to the end consumer (and they’re adept at doing so). Where they fall short is in lacking a common infrastructure to unify data across all channels, resolve duplicate identities, and serve as the one-stop shop for marketers to analyze customers and orchestrate experiences across all marketing, CX, and commerce channels. 

By contrast, CDPs are engineered to be the brain of the martech stack that creates the organization’s single customer view and generates operational efficiency by enabling data democracy for business users. CDPs ingest and organize data from all kinds of disparate systems and are vendor-agnostic by design, thereby enabling a best-of-breed technology strategy. Finally, while marketing clouds often enable personalization within a channel, CDPs collect data and automate personalization across all channels for a seamless customer experience.

What is the difference between a CDP and a data management platform (DMP)?

A DMP does not take the place of CDP, or vice versa. Instead, CDPs and DMPs are complementary technologies that fulfill different use cases within the marketing journey. More specifically, DMPs are designed to plan and manage paid digital advertising that is conducted on 3rd party websites (e.g., search platforms (e.g. and social platforms (e.g. for anonymous users. 

By contrast, CDPs are designed to create a single customer view across an organization’s first- and third-party data (for both known and anonymous users) and serve as the central command center (or brain) of the martech stack. The CDP provides users with a rich profile for each customer, including all their historical interactions as well as intelligence on their affinities (e.g. preferred channels, content, products). Finally, the CDP orchestrates an organization’s personalization efforts across all channels (marketing, CX, or commerce) by providing prebuilt data connectors, predictive recommendations, and journey automation.

DMPs and CDPs often work together to optimize digital advertising efficiency. For example, a CDP would send its existing customers to a DMP, and the DMP then suppresses such customers from prospecting campaigns to save money (since they’re already customers).

What is the difference between a CDP and a Master Data Management (MDM) solution?

While CDPs and MDMs (master data management solutions) are both hubs that unify, store, and syndicate customer identities to disparate systems, the two technologies work in a partnership instead of a competition. More specifically, IT organizations often use an MDM to serve as a system of record that deduplicates known identities (i.e. PII customer data, such as name, address, phone). In a partnership with a CDP, the MDM often pushes its resolved identity data (including the master/golden value for each attribute) to CDPs where additional data is then stitched to it (e.g. clickstream, behavioral, transactional, marketing, demographics) for marketers to perform analysis and personalization at scale.

What is the difference between a CDP and data infrastructure such as an Enterprise data warehouse (EDW), data lake, and/or big data platform?

In simple terms, an enterprise data warehouse (EDW), data lake, or big data platform is a repository for data, whereas a CDP is a widely-connected hub with a data repository at the center of it.

Many organizations with best-of-breed technology stacks have a data lake (inclusive of a big data platform) for enterprise data storage and analytics, while having a CDP to create a single customer view for marketing and for serving as the ommichannel brain across all touchpoints. As far as data sharing goes, EDWs, data lakes and big data platforms often send transaction data to CDPs. And in the same manner, CDPs often send their comprehensive customer profiles to EDWs/DLs/BDPs to enable greater precision and accuracy when performing enterprise-themed analyses.

How does a CDP fit into the rest of the marketing technology (martech) stack?

CDPs are designed to improve the effectiveness of one’s martech stack by providing three new capabilities: 1) a single-customer view across a customer’s entire interaction history; 2) a user-friendly interface for data democracy and maximized operational efficiency; 3) business control of multi-step journeys (incl. design, testing, measurement and optimization) that yield personalization at scale.

In short, the CDP is designed to connect one’s existing marketing technologies and serve as the brain that enables and optimizes 1:1 personalization. In technical terms, CDPs often sit in the middle of one’s martech stack, acting as the intelligent command center that receives information from siloed systems (e.g. EDW, MDM, loyalty, marketing cloud, website, POS, etc.) and connects to downstream, customer-facing systems that deliver personalized messages (e.g. CRM, call center, POS, website, ESP, DSP, direct mail, apps, etc.). Critically, CDPs enable a best-of-breed stack by being agnostic to the upstream and downstream technologies that provide and deliver information. This means organizations can integrate innovative, top-tier point solutions (such as a premier ESP or an innovative DSP) in place of expensive “marketing/experience cloud” packages.

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