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A CDP is a solution that connects your data and AI directly to your customer experiences, with segmentation, orchestration and analytics tools.
CDPs connect teams across the business, to enable each functional group to operate more efficiently, provide superior customer experiences (CX) and make smarter, data-driven decisions. At their core, CDPs are built to help business teams resolve the bottlenecks and technical challenges that prevent them from becoming self-serving and customer-centric.
CDPs help marketers, salespeople, customer service representatives and other non-technical business users design better customer experiences, better understand their audience and deliver personalized experiences at scale across all online and offline channels.
The right CDP helps data and IT teams maintain a strong, scalable data architecture with full visibility, seamless integrations with the data warehouse and destination channels—while supporting marketing and customer experience teams to design, launch, and measure customer experiences.
This empowers business teams to ensure all customer engagement is highly relevant and impactful while reducing the burden on technology and data teams.
Want to learn more about the ins and outs of customer data platforms? Dive into the information below to answer all your CDP questions.
A Customer Data Platform (CDP) is a technology used by marketers and customer experience professionals to unify, analyze, and activate customer data across all channels.
It creates a single, persistent, 360-degree customer profile by activating a customer’s dataset through either a composable or traditional deployment to enable personalized experiences at scale to increase your performance goals, from lifetime value and customer acquisition costs to improving ROI and efficiency goals.
The CDP landscape has grown quickly, offering cutting-edge solutions that meet the complex data challenges of modern enterprises. The focus has shifted from siloed systems to more flexible, composable architectures that allow businesses to adapt quickly to changing needs and scale, increasing ROI in a matter of months.
At its core, a CDP remains a powerful tool for unifying customer data and enabling personalized experiences at scale — but CDPs today have gone from data repositories to integration environments, offering advanced AI-driven insights, real-time activation capabilities, and seamless integration with a wide array of martech and customer experience tools.
Next-generation CDPs (like Uniphore’s) help businesses design a modern data and marketing tech stack, helping those businesses scale faster, operate easily and perform better.
In short, enterprise companies need technology that:
While the technical capabilities of CDPs have advanced significantly over the past few years, their core purpose remains unchanged: to help businesses understand their customers better and deliver superior, personalized experiences at scale.
Whether you’re looking to upgrade your existing CDP or implement one for the first time, be sure to consider these five critical capabilities:
Modern CDPs offer a composable approach — allowing businesses to maximize their existing data investments through warehouses like Databricks, Snowflake, Teradata VantageCloud, Amazon Redshift, or Google BigQuery to compute on data directly in the warehouse. This approach provides greater flexibility, faster time-to-value, and easier integration with existing tech stacks.
Leveraging machine learning and artificial intelligence, today’s CDPs provide deeper, more actionable insights across the customer lifecycle and different channels where your customers engage with your brand. They can predict customer behavior, recommend next-best actions, and even automate decision-making processes for more efficient operations.
CDPs help marketers reach their audience in real-time — combining streaming and historical data to inform real-time experiences across online and offline channels – even from data in the data warehouse. With triggered actions, marketers can push personalized real-time communications to outbound customer touchpoints.
As data privacy regulations continue to evolve, modern CDPs like Uniphore’s include robust features for consent management, data governance, and compliance with global privacy laws. Composable deployments support the highest standards of security by requiring no data copy out of the warehouse to activate customer experiences.
Today’s CDPs are designed to break down silos between departments. They provide intuitive interfaces that empower marketing, sales, customer service, and even product teams to leverage customer data effectively without constant reliance on IT and technology teams. With a secure data architecture, this frees up all teams to focus on their strengths and deliver better work faster.
Enterprise CDPs have many benefits, but there are three main benefits that a CDP brings to the enterprise:
CDPs provide organizations with the customer insights they need to capitalize on growth opportunities and increase speed to market. This could include driving more revenue by optimizing customer acquisition or increasing customer lifetime value (CLV), as well as cutting marketing costs via improved audience targeting and shortened conversion times.
CDPs provide organizations with the tools they need to increase team productivity and streamline processes. This could include enabling business teams to self-serve insights so technical teams can focus on higher-value projects, updating a marketing technology stack to eliminate redundancies and optimize spending, as well as providing teams with greater agility for strategy iteration.
CDPs provide organizations with the capabilities they need to gain greater visibility into their customers across channels and deliver compelling CX at the right time and place. This could include reducing churn by identifying and engaging at-risk customers, personalizing experiences to build brand loyalty and optimizing omnichannel engagement to increase customer satisfaction.
With their focus on customer data and personalizing customer experiences, CDPs are sometimes confused with other technologies. Some of the most common examples are:
Customer Data Platforms remove data silos, create a 360-degree customer view, and democratize access to customer data to personalize interactions across marketing, sales, and service channels. CDP data gives marketers and customer experience professionals the insights they need to make every customer engagement count. These data and AI-driven insights empower functional teams throughout the enterprise.
Customer data platforms resolve common pain points, enabling enterprise companies to ensure their customer data is accurate, accessible, and actionable. Specifically, CPDs help executive and functional teams minimize:
The return on investment of a CDP is both quantitative and qualitative. With the right solution, enterprises can achieve greater scalability, technological flexibility and agility and speed to innovation. They can also improve core outcomes with measurable results. For example, based on a Forrester Total Economic Impact™ (TEI) report, enterprise brands that invest in Uniphore’s CDP can achieve:
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CDPs prepare data for artificial intelligence by gathering raw, disparate customer data and combining it into a single customer view. Enterprise users can then use this “single source of truth” to build, deploy and fine-tune AI agents and applications for a variety of use cases.
CDPs enable brands to hone their AI marketing efforts in four ways:
Enterprises can deduplicate customer profiles using machine learning-powered deterministic and probabilistic matching techniques.
Enterprises can use a library of native, machine learning-powered models to quickly forecast the likelihood of customer churn, uncover ideal send times, create lookalike audiences and more.
Enterprises can integrate model scores built in-house into a machine learning framework for business-specific objectives, such as audience segmentation.
Enterprises can use algorithmic performance measurement that uses statistical methodologies to track the progress of campaigns and evaluate results.
Exceptional CX depends on a brand’s ability to deliver consistent experiences across all online and offline channels. AI can drive consistency across channels by connecting multiple touchpoints and automating common tasks and actions. How seamless it does this, however, depends on the data. By providing AI with a comprehensive source of high-quality, AI-ready data, CDPs facilitate omnichannel customer experiences in four ways:
CDPs tap into real-time customer interactions from all systems, enabling enterprise brands to understand and predict their customers’ behaviors and preferences across different channels.
CDPs ensure the business teams responsible for providing CX across different channels are kept up to date on customers’ wants and needs via complete, accurate profiles.
CDPs empower business teams to create sophisticated, multi-step customer journeys that can be fully automated and dynamically adjust channels, content and timing based on customer behaviors and preferences.
CDPs combine hundreds of real-time data connectors to activate segmented audiences and customer journeys on final-mile execution tools across all marketing, sales and customer service channels.
Personalization is the cornerstone of superior CX, but delivering tailored, highly relevant experiences to millions of customers is easier said than done. While AI can support—and generative AI can produce—personalized marketing content, its accuracy depends on the data it uses. By gathering data into a complete customer picture for AI use, CDPs support personalization at scale in five ways:
CDPs ingest data from every channel using prebuilt data connectors and automatically stitch it together into a persistent, single profile for each customer.
CDPs provide business teams with self-service access to accurate, unified customer insights for data analysis, data modeling and journey orchestration across all channels.
CDPs use both out-of-the-box predictive analytic models and hosted models to predict which products, offers, content, audiences and more will drive the best results.
CDPs leverage prebuilt data connectors to automate campaign execution across all channels.
CDPs provide easy-to-use testing and measurement to automatically identify which campaigns, initiatives and customer journeys maximize desired business outcomes.
CDP use cases can be divided into three categories based on the value they deliver to the business:
By providing meaningful insight into customer behaviors and preferences, CDPs enable marketers to discover net-new opportunities and act on them quickly
Detecting at-risk customers and delivering automated messages to prevent churn
Identifying high-potential prospects through lookalike models and targeting them via channels like direct mail, digital advertising, or social
By calculating customer propensities via CDP marketing analytics, you can reduce marketing spend by targeting only customers with a high likelihood of conversion.
Measuring discount sensitivity and minimize sending coupons to customers that don't display a need for them in order to buy
Driving more efficient prospecting by suppressing existing customers from prospecting campaigns across paid media and direct mail channels
By automating data unification and empowering self-service operations, CDPs eliminate operational bottlenecks and reliance on expensive technical professionals.
Creating a single-source-of-truth for customer data where employees can gain accurate customer insights easily
Providing a user interface that enables scaled customer analytics, audience discovery, campaign list creation, campaign journey design, and campaign measurement
Curious how other enterprise companies are using their CDPs?
Here are eight of the most common enterprise customer data platform use cases:
Use lookalike modeling to identify prospects who resemble your ideal customers. Target them across the channels (e.g., direct mail, digital advertising, social media, etc.) shown to drive the best results for these audiences.
Use customer interaction and purchase data to understand which channels, communications and products resonate most with customers. Inform content creation and customer journey orchestration with this information to speed up buying cycles.
Use both streaming and historical data to develop customized, highly relevant customer experiences. Create multi-step customer journeys—as well as triggered and scheduled real-time experiences—that are tailored to customers' behaviors and preferences across all online and offline channels.
Use lookalike modeling to identify customers who resemble your churned customers. Automate communications designed to re-engage at-risk customers and build brand loyalty.
Use customer data to suppress specific audiences from unnecessary paid advertising campaigns. Measure sensitivity to discounts among different segments to avoid wasting promotions on customers who don't wait for discounts or special offers to make a purchase.
Use customer data to determine which channels—and the frequency of engagement on those channels—drive the best results. Prioritize high-value channels and refine the cadence of customer outreach using insights from your customer data platform.
Use a single source of truth to help teams across marketing, sales and customer service access the full customer profile. Increase cross-team visibility to identify and take action on opportunities without advanced technical knowledge or assistance.
Use a business-friendly user interface to empower business teams to adjust data definitions and formats without having to submit requests to IT. Test and measure campaigns to optimize performance and innovate new strategies without having to wait for technical assistance.
Customer data platforms provide significant benefits to all parts of an organization, including marketing, IT, analytics and more. Every team plays an important role in successfully implementing and maximizing the value of a CDP.
This makes buy-in from all business stakeholders essential. It’s important to position a CDP as a win-win for all parties.
Here are five key steps to building a business case for a CDP:
Collaborate with stakeholders to understand their main goals and how they'd like a CDP to help them reach them. Prioritize the use cases a CDP should support based on business impact.
Build familiarity and excitement for a CDP with a vendor-led demonstration. Make sure to communicate your prioritized business use case(s) for a more relevant and tailored demo.
Calculate the return on investment for your business by asking vendors about typical outcomes for each business use case. Request references and proof of outcomes from existing customers.
Create a presentation to show how a CDP will bridge the gap between different stakeholders' primary objectives. Ensure the presentation is financially focused and aligned with the most important business use cases.
Share your presentation with the stakeholders who make up your buying committee one at a time to understand their perspectives, address any concerns and optimize your pitch. Once your presentation is complete, offer it to the final decision-makers.
CDPs are designed to improve the performance of your entire martech stack.
They’re transforming from simple data orchestrators into the command centers of customer experience, creating a two-way street between your data stores, AI models (both the traditional kind and of course, GenAI), and all your customer-facing systems.
What makes this evolution so powerful is how it democratizes access to customer insights. The CDP becomes a central nervous system, making customer data available to both human operators and AI systems, while connecting seamlessly to CX endpoints for comprehensive activation of this hybrid intelligence.
With a Composable architecture, bringing a CDP into your stack is even easier. Composable CDPs tap directly into your data warehouse or Lakehouse, like Databricks, Snowflake, Google BigQuery, Teradata VantageCloud and Amazon RedShift to securely activate customer data.
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Enterprise companies have very different technology needs than smaller organizations. They have far more customers and are generally active across a wider range of channels, necessitating greater data flexibility.
Additionally, their technology stacks are typically more complex, requiring higher levels of interoperability. And, because of the scale of their customer bases and their ability to collect massive amounts of customer data, they’re often held to higher privacy and data security standards.
When choosing the best CDP for your brand, be sure to consider core capabilities and enterprise-grade features, such as:
With millions (if not hundreds of millions) of customers—and a focus on engaging customers across multiple online and offline channels—enterprise organizations require a CDP that can compute huge amounts of data quickly and easily.
Only enterprise CDPs enable brands to process petabytes of incoming data (a single petabyte is equivalent to 20,000,000 four-drawer filing cabinets full of files) and make it available for segmentation, predictive analytics and activation activities.
Additionally, by empowering business teams to configure data themselves, enterprise CDPs eliminate the need for IT to preconfigure data for insights and activations, reducing bottlenecks.
Because of the wide variety of external systems and data sources enterprise organizations work with, they require a CDP that can ingest data in any format.
Only enterprise CDPs enable brands to avoid costly and time-consuming data modeling exercises every time data needs to be added.
Flexibility extends to how enterprise CDPs help resolve customer identities, as companies with massive customer bases are likely to have many customers who represent themselves in varying ways across brand interactions, such as using different phone numbers or email addresses.
Since enterprise organizations are active across multiple channels for marketing, sales and customer service activities, they require a CDP that will connect to any and all existing systems.
Only enterprise CDPs enable brands to cultivate a true best-of-breed technology strategy that allows for best-in-class solutions to be used for specific purposes while minimizing technology overlap to reduce costs and boost efficiency.
With greater quantities of customer data to protect, enterprise organizations require a CDP that goes above and beyond in maintaining data security and privacy.
Only enterprise CDPs enable brands to not only meet compliance requirements for regulations such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), but also the Health Insurance Portability and Accountability Act (HIPAA) and others.
Enterprise brands should also seek out technologies that have SOC 2 Type 2 certification.
CDPs are foundational technologies in your martech stack, but they shouldn’t take enormous effort to implement. In general, it should take enterprise companies between four and 12 weeks to begin leveraging a CDP. Advanced CDPs, like Uniphore’s composable CDP, can slash that time even further. By eliminating complex, time-consuming data migration and transformation tasks, a composable CDP makes it easy to get started—with connector setup with cloud data warehouses in just minutes.
To ensure implementation success, enterprise organizations should prioritize:
There are several distinct advantages and disadvantages to consider when deciding whether to build or buy a CDP. While each organization has its own unique business needs, there are six key factors every enterprise should keep in mind:
For most business use cases, buying a CDP provides the fastest time to value. For example, Pandora Media was able to start generating ROI in as few as 90 days with the composable CDP offered by Uniphore.
Because of the internal engineering efforts required to get a homemade CDP off the ground, brands can expect a slower time to value if they decide to build.
Building a CDP may make sense for brands with highly specialized use cases, but only if they have the skilled internal resources necessary to create and deploy their own technology.
Buying a CDP generally comes with a higher initial cost of ownership due to software licensing and subscription costs, but TCO drops significantly thanks to vendor-provided enhancements, maintenance and support over time.
In contrast, while building a CDP may initially have a lower TCO due to limited specifications and usage, it will rise substantially if usage increases.
Additionally, technology enhancements, maintenance and support will steadily drive up cost of ownership.
With faster time to value—and the ability to easily expand use cases—brands will see ROI sooner when buying a CDP. And since internal IT resources will be freed up to focus on the highest-value initiatives, brands will see increases in ROI across their entire organizations.
The ROI of building a CDP is dependent on a brand’s ability to develop and support new capabilities quickly and cost-effectively.
Organizational ROI is hampered by IT teams being forced to focus on platform-level engineering projects instead of higher-value initiatives.
By buying a CDP from a vendor with proven technology and implementation experience, risks associated with change management are minimized.
The risks associated with building a CDP tend to be higher depending on the state of a brand’s martech stack and the expertise of its internal engineering teams.
Additionally, since IT teams may develop a solution that lacks a user-friendly interface, brands run the risk of hindering user adoption by going this route.
Best-in-class vendor-provided CDPs will house customer data in a secure cloud environment, such as Amazon Web Services, and provide granular role-based and user-level permissions for data governance.
Additionally, enterprise-grade CDPs will have SOC 2 Type 2 certification and support compliance with regulations such as GDPR, CCPA, HIPAA and others.
For homemade CDPs, security will depend on internal expertise. Brands can build solutions behind a firewall or in the cloud, but security, privacy and governance will need to be designed, implemented and managed by internal IT teams or contracted service partners.
Vendor-provided CDPs have widely tested user interfaces that are periodically updated for greater ease of use, as well as built-in technical support and helpful user communities.
Together with an ever-growing library of prebuilt integrations—and leading vendors’ ability to ingest and utilize data from nearly every source without requiring upfront data modeling or transformation—buying a CDP ensures non-technical teams can easily access and acton customer insights.
In comparison, the user experience of CDPs built in house is dependent on internal engineering teams’ proficiency in designing user interfaces for non-technical users and rapidly iterating on their work to create an intuitive, effective workflow.
A lack of prebuilt integrations and restrictive data ingestion capabilities are likely to increase reliance on technical teams.
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A customer data platform (CDP) is a technology used by marketers and customer experience professionals to analyze and activate customer data across all channels. It creates a single, persistent, 360-degree customer profile by understanding and activating data , enabling personalized experiences at scale to meet your customer where they are and help businesses accomplish their business goals — from increased lifetime value and customer acquisition costs to optimized ROI and efficiency goals.
Unlike CRMs or DMPs, CDPs tap into the complete dataset and are built on first-party data to resolve customer identities and activate data across all channels. They provide a comprehensive view of the customer and enable more sophisticated, omnichannel personalization than other marketing technologies—and they can do it in real time.
The main benefits of using a CDP include enhancing revenue streams, increasing operational efficiency across different teams (notably, marketing, technology, data, and advertising teams), and improving customer experience. CDPs help businesses understand their customers better, personalize experiences at scale, reduce marketing costs, and streamline data management processes so you can get exponentially better results from your different efforts.
CDPs support AI and machine learning by activating a unified, clean dataset for analysis. They often include built-in predictive analytics capabilities, can host custom models, and use AI for tasks like identity resolution and performance measurement.
CDPs can manage all types of customer data, including first-party, second-party, third-party and anonymous user data with the data warehouse and enrichment from partners. This includes personal information (PII), campaign and engagement data, transaction information, customer metrics and scores, demographic or firmographic data, and customer satisfaction (CSAT) data.
CDPs enable omnichannel experiences by providing a single view of the customer across all touchpoints, allowing for consistent messaging and personalization. This means you can cut out duplicate data and avoid mistakes and wasted ad spend. They also facilitate journey design, orchestration, and measurement across multiple channels.
Building a CDP typically offers more customization but requires significant time, resources, and ongoing maintenance. Buying a CDP generally provides faster time-to-value, lower total cost of ownership, and ongoing vendor support and updates. You can download our Build vs Buy Guide and then learn more about Uniphore’s composable CDP here.
For enterprise companies, CDP implementation typically takes between 4 to 12 weeks. However, this can vary based on the complexity of existing systems and the chosen CDP’s architecture.
A CDP acts as a central hub in a martech stack, collecting data from various sources, unifying and enriching it, and then pushing it out to downstream execution tools. It supports a best-of-breed strategy by enabling seamless connections between technologies.
Key features of an enterprise-grade CDP like Uniphore’s include scalability to handle large data volumes, flexibility in data ingestion and identity resolution, extensive connectivity with other systems, and robust privacy and security measures.
Enterprise-grade CDPs like Uniphore’s offer features to support compliance with regulations like GDPR, CCPA, and HIPAA. They provide granular control over data access, consent management capabilities, and data governance tools.
Yes, CDPs like Uniphore’s (which is hybrid composable and flexible, rather than a standard out-of-the-box solution) provide user-friendly interfaces that allow business users to access and analyze customer data without extensive technical knowledge. This reduces the need for constant IT and tech support for data-related tasks, saving marketing teams months of time when they need new audiences pulled, and allowing technology teams to focus on more impactful tasks.
While data lakes and warehouses are primarily data storage solutions, a CDP is a widely-connected hub that not only stores customer data but also makes it accessible and actionable for business users across various channels.
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 omnichannel brain across all touchpoints. EDWs, data lakes and big data platforms often send transaction data to CDPs; and CDPs often send their comprehensive customer profiles to EDWs/DLs/BDPs to enable greater precision and accuracy when performing enterprise-themed analyses.