CDP, CRM or Data Lakehouse: Understanding the key differences for effective customer data management

With the rise of digital products and services, the amount of customer data collected by brands and companies has increased significantly. But this data is often trapped in different information systems. Today's increasingly connected customers expect unique, intuitive experiences with brands based on their preferences and behavior. A Customer Data Platform (CDP) will centralize and unify this disparate data, offering companies the ability to create personalized, consistent experiences for everyone, meeting growing expectations for hyper-personalization and satisfaction.

"It's just another wart in our infrastructure, when we already have a CRM that's supposed to consolidate customer information, and a data lakehouse capable of meeting the data consumption needs of different departments..."

... we often hear from IT teams. This article aims to disprove this assertion and clarify the distinctions in order to better understand the strategic role that this key (and often missing) component plays in supporting sales and marketing teams.

A CDP is at the service of the customer experience

CDP, the misunderstood and yet ...

CDP, CRM and data lakehouse are often misunderstood and confused, even though each of these systems has specific objectives, unique structures and distinct uses. Confusing them can lead to significant inefficiencies and loss of value for brands. While the data lakehouse is designed to manage massive volumes of structured and unstructured data for advanced analytics, the CDP specifically centralizes customer data for personalized sales and marketing campaigns. CRM, on the other hand, focuses on managing customer interactions initiated by the sales process.

The differences explained

It is crucial for companies to understand the differences between the various data management solutions available. The following definitions illustrates the main differences between a CDP, a CRM and a Data Lakehouse to help clarify their respective roles and facilitate informed decision-making.

A Customer Data Platform (CDP) specifically focuses on centralizing and managing customer data from various sources to create unified customer profiles. These profiles are enriched with demographic, behavioral, transactional, and preferential data, offering a comprehensive view of each customer. CDPs excel in advanced segmentation, real-time data activation, and personalization of marketing campaigns, making them indispensable for marketing teams and customer relationship managers aiming to enhance customer engagement and satisfaction.

A Customer Relationship Management (CRM) system, however, is centered around managing direct interactions with customers and handling sales processes. It records all interactions, tracks sales activities, and manages support tickets. CRMs are essential for sales and support teams to monitor and optimize customer interactions and maintain detailed records of customer relationships. Unlike CDPs, CRMs are more focused on the operational aspects of customer relationships and sales management rather than on data unification and advanced segmentation.

A Data Lakehouse is designed to manage and analyze large volumes of both structured and unstructured data. It combines the flexibility and scalability of a data lake with the performance and reliability of a data warehouse. This system is primarily used for advanced analytics, machine learning, and business intelligence operations. It enables data scientists and analysts to perform deep data exploration and generate insights without worrying about data format constraints. While a data lakehouse provides broad and deep data storage and analysis capabilities necessary for high-level insights and complex data manipulation, it does not offer the real-time, actionable insights and customer personalization capabilities that CDPs provide.

A CDP for whom and why?

A CDP centralizes, unifies and activates customer data from multiple sources, providing a complete, real-time view of each customer. This ability to collect and analyze data exhaustively, and very often in real time, radically transforms the way brands interact with their customers, optimize their marketing campaigns and make data-driven decisions.

Modern CDPs feature extensive tools to facilitate the creation of this view, including sophisticated data unification, suggested deduplication, extended marketing segmentation, and centralized consent management.

It plays a crucial role for various users within the company. For marketers, it enables the creation of personalized campaigns by leveraging unified customer profiles, thereby increasing engagement and conversions. Marketers dedicated to customer journey analysis, use centralized data for advanced analytics and predictive models, offering valuable insights into customer behavior. Customer relationship managers benefit from a complete view of interactions, enabling them to improve customer satisfaction and loyalty. Sales teams can leverage customer segments to identify sales opportunities and personalize their approach. Customer support can offer faster, more personalized service by accessing interaction history. Compliance officers ensure that customer preferences and consents are managed effectively, guaranteeing compliance with data protection regulations. Finally, Chief Data Officers (CDOs) oversee data quality and consistency across the enterprise, ensuring effective governance of customer data.

What feeds a CDP?

The data sources that enrich a CDP are many and varied, and are generally captured during the customer journey. They are often heterogeneous, combining structured and unstructured data from numerous systems and channels, both digital and physical:

• Purchase intent: demographic, behavioral, preference data, etc.
• Purchase act: transactional data, sales, subscriptions, wish lists, financial information, etc.
• Services used: satisfaction data, complaints, guarantees, etc.
• Consumption: services activated, services consumed, devices connected, etc.
• Loyalty: loyalty program data, recommendations, surveys, NPS score, etc.

The CDP can also be enriched by third-party sources such as public databases and data acquired via commercial partners. For example, major brands very often purchase anonymized data to model personas with similarities to certain prospects, in order to better anticipate the best next interactions.

Integrating a CDP into your data ecosystem

Most CDPs have standard connectors that interface with the market-leading solutions found in the Martech and Adtech worlds.The contribution of an ESB-type integration solution not only guarantees data orchestration between systems, but also ensures an advanced level of transformation in real time, optimizing data consistency, quality, traceability and availability for efficient use by the CDP.

The limits of a CRM and the convergence of the two

As explained above, a CRM focuses primarily on managing direct customer interactions and sales processes (managing leads, opportunities and sales agreements), while a CDP centralizes data from multiple channels (web, mobile, social, POS, ERP, etc.) to create unified customer profiles. This enables advanced segmentation and real-time personalization that CRMs cannot offer in such a comprehensive way. On the other hand, a CRM, like marketing tools, will themselves be able to visualize the extended 360° view of the customer, providing sales and marketing teams with an exhaustive source of data enabling them to make the best decisions at the right time.

A data lakehouse doesn't replace CDP, it complements it

Although a data lakehouse offers many advantages, including the ability to store and analyze large quantities of structured and unstructured data, it is not a substitute for a CDP. In fact, a data lakehouse enhances a CDP by providing raw and refined data that can be used to create detailed and complete customer profiles. Data lakehouses are often developed as part of data mesh-type data management strategies, aimed at creating decentralized, interconnected data assets. However, despite their strengths in advanced analysis and data asset creation, they are not designed to directly support the customer experience. CDPs, on the other hand, are specifically designed to centralize, unify and activate customer data in real time, offering advanced personalization and segmentation of marketing campaigns. Thus, integrating a data lakehouse with a CDP maximizes analytical and operational capabilities, while optimizing the customer experience.

Train AI models with CDP to predict experience

The unified, enriched data of a CDP plays a crucial role in training artificial intelligence (AI). By centralizing data from multiple sources, creating extended customer profiles and offering real-time data, CDP provides AI models with accurate and consistent information for learning. This data can be used to personalize interactions, optimize sales and enhance the customer experience. For example, AIs can provide personalized product recommendations, predict customer needs and automate complex tasks, thereby increasing conversion rates and customer satisfaction.

Who is the CDP leader?

Salesforce Data Cloud is recognized by Gartner's Magic Quadrant as the leader in Customer Data Platforms (CDP) due to its ability to unify and activate customer data across all Salesforce applications and third-party systems without the need for complex ETL processes. As the Salesforce platform's native hyperscale data engine, it provides real-time access to customer data, enabling companies to optimize the timing and targeting of their customer engagement activities.

Conclusion

A CDP fits seamlessly into the complex ecosystem of enterprise data management, providing sales and marketing teams with the tools they need to increase sales, customer loyalty and engagement. By centralizing and unifying data from all systems, CDP enables a complete and consistent view of the customer, facilitating personalized and effective interactions. Combined with solutions such as data lakehouses and CRM, CDP transforms this data into actionable insights, while ensuring well-orchestrated, traceable and regulatory-compliant integration. This synergy between different systems and the optimized use of data represents a considerable asset for companies, boosting their competitiveness and their ability to respond to customer needs in a proactive, personalized way.