Customer data platforms (CDPs) first gained popularity among marketing users as the technology tackled the marketing function’s complex customer data management challenges. These challenges included centralising data collection and unifying customer data from disparate sources into profiles that could then be segmented and activated across the marketing technology (martech) ecosystem.
Gartner defines CDPs as software applications that support marketing and customer experience use cases by unifying a company’s customer data from marketing and other channels. Customer data platforms optimise the timing and targeting of messages, offers and customer engagement activities, and enable the analysis of individual-level customer behaviour over time.
The customer data platform purchasing process involves multiple stakeholders. According to a 2025 survey of business buyers by Gartner, the average number of groups providing funding for CDP purchases is five, with two to three groups typically contributing to the requirements and objectives.
CDPs orchestrate a variety of business applications and customer relationship management (CRM) systems, enabling businesses to enhance and better coordinate go-to-market (GTM) execution – for example, unified commercial motions for business-to-business (B2B) and customer journey orchestration for business-to-consumer (B2C) business.
Since their inception, CDPs have garnered significant adoption, with 68% of respondents to a 2024 Gartner survey of marketing analytics and technology indicating their organisation has a CDP, and another 18% responding that they are in the process of deploying one.
Pulling together data
The purpose of a customer data platform is to centralise data collection and unify customer data from disparate sources into profiles. CDPs enable analysts and data engineers to perform data management tasks that support higher-order business processes, primarily marketing, but increasingly cross-CRM functions, as well as finance and product management.
A CDP has the potential to provide significant value to marketing and adjacent functions, unifying customer data and enabling business users to activate rich data and insights across channels, devices and enterprise data applications by governing the bidirectional flow of data between the front office and back office.
However, while it is not a substitute for enterprise master data management, a CDP can ensure that customer profile data, transactional events and analytic attributes are available to marketing and other customer-facing functions for coordinating interactions.
Enterprise software with CDP capabilities
There is a significant divide in the CDP market between enterprise application providers (EAPs), particularly those in CRM or ERP, that offer CDPs as part of a platform approach, and providers with more standalone offerings. Chief marketing officers (CMOs) must make the strategic decision between choosing a platform provider versus a standalone CDP offering.
Gartner has identified a number of critical capabilities that a CDP needs to provide. These include data collection, customer profile unification, integrations, segmentation, experimentation, and data science and privacy, among others.
Data collection is the process of ingesting (extracting) first-party, individual-level customer data from multiple sources and formats, both online and offline. Some CDPs may offer software development kits and/or tags/pixels to facilitate data collection and serve as a central point of ingestion for data processing. Data should persist for as long as needed for processing, and is typically left unchanged in its original source. This includes both anonymous and known first-party identifiers, behaviours and attributes.
Along with data collection, a CDP should also provide the ability to connect to and exchange data, instructions or segments with other tools, programs, apps and channels to enable marketing channel execution. This may include integrations to cross-functional tools supporting customer experience (CX). Data interoperability with cloud data warehouses and lakehouse designs are emerging differentiating features of CDPs.
Data collaboration is identified by Gartner as another feature of a CDP, which offers productised access to second and/or third-party commercial datasets, such as demographic, intent, firmographic or technographic data. Advanced capabilities may include a named partnership with a data clean room or the ability to access it within the CDP environment.
Customer profile unification is where the CDP consolidates profiles at the person level – sometimes at the household level – and connects attributes to identities. This must include linking multiple devices to a single individual, once that person has been identified, and deduplicating customer records. The process for identity resolution varies. Solutions may use deterministic matching and/or probabilistic identity graphs, and may have additional partnerships with third-party identity resolution providers to further advance this capability.
Segmentation enables marketers to create and manage segments or audiences. Basic offerings support rule-based segment creation, often with out-of-the-box predictive models, while advanced offerings support dynamic segmentation, leveraging real-time data to move individual customer profiles in and out of segments as their actions meet previously defined criteria. Advanced features may also include automated segment discovery and the use of artificial intelligence (AI), machine learning (ML) and/or large language models (LLMs).
A CDP also needs to provide performance analysis for various levels of customer data, such as at the attribute level, profile level or segment level, often with out-of-the-box dashboards and templates for reports. Offerings may include data monitoring and data quality assessments. Experimentation and data science basic offerings include testing, such as A/B or multivariate tests, to monitor and self-optimise the customer experience based on the winning campaign segment.
Advanced offerings may include additional features, such as real-time experimentation and the ability to import and manage ML models within the CDP using R or Python. They may include integrations with data science or LLM solutions, and provide the ability to carry out granular configuration of scoring and prediction.
Adoption outside marketing
Demand for CDPs rose out of marketing’s need to address complex customer data management challenges without long wait times for IT ticket resolutions, but the scope of this technology has expanded significantly. CDP providers began to add more personalisation, customer journey orchestration and predictive capabilities to differentiate in a busy market. Since then, there has been further differentiation through innovations in data sharing, AI model development and LLM-based functionality.
So, while CDPs originated to serve marketing use cases, interest from data management roles, IT and other customer-facing business user roles, such as sales, service and support, is on the rise. Overall, Gartner recommends closely evaluating CDP providers’ capabilities as marketing technology utilisation is trending downwards, dropping from 58% in 2020 to just 33% in 2023. In fact, in 2024, marketers only used 53% of CDP capabilities, on average.
This article is an excerpt of Gartner’s “Critical capabilities for customer data platforms” report by Rachel Smith, a senior principal analyst at Gartner.
