To activate a RCU, it must first be understood as a Data Product

Since the emergence of CRM in the 1990s, the “360-degree view of the customer” has captivated marketers. The advent of digital technologies and big data in the 2000s reinforced the hope for an omniscient understanding of the customer. With the arrival of cloud computing and AI in the 2010s, this vision has come closer to reality.

In the face of increasingly complex customer journeys, new solutions such as data lakes, DMPs, and subsequently CDPs have emerged to address challenges related to privacy and data management. Thus, the promise of a Unique Customer Reference (RCU) is beginning to materialize. However, despite massive investments, the results can sometimes be mixed.

Damien Depuiset, Manager Data Technology, and Nicolas Gregson, Data Product Manager, explain why the RCU should be considered a standalone product to fulfill its promises.

To begin with, could you explain what an RCU is and why it is important for Converteo’s clients?

Damien: An RCU is a database that aggregates customer data from various sources to provide a unified view of a company’s customers.

Once collected and transformed, this data can be used to personalize customer interactions and optimize marketing campaigns. The RCU is an essential enabler for enhancing the user experience, analyzing customer journeys, personalizing interactions, anticipating decisions in real time, and meeting regulatory requirements through centralized consent management.

Nicolas: Beyond technological challenges, it is crucial to view the RCU as a “data product,” meaning a digital product resulting from a business vision, with a true value proposition, but fundamentally based on data to deliver beneficial functionalities to its users. It is essential to understand its evolving nature and its capacity for continuous improvement.

Each data product consists of three components. At the core is the raw data. In the case of the RCU, this includes all data from various sources. Next, this data is enriched and transformed to make it usable. Finally, we provide users with access to this data, which can take the form of SQL queries, APIs, or interfaces like dashboards.

To fulfill the promise of the RCU, we must look beyond the linear implementation of a project and especially consider it as a product designed to address changing user needs. 

You speak of users; who are they in the context of an RCU?

Nicolas: The users of the RCU are not external clients but internal employees or consultants. The direct users are often the Data & Analytics teams who access the database via queries, while the indirect users include the Marketing and Data Science teams. The latter leverage the wealth of the RCU through platforms like CDPs, which are technical solutions aimed at addressing customer knowledge issues that the RCU seeks to solve.

Damien: RCU projects are often associated with CDP implementations. The RCU allows for capitalizing on data assets and ensures that the CDP has consolidated and unified data. This improves audience segmentation and personalizes communications according to customers’ preferred channels, thereby optimizing marketing campaigns and various other uses. A CDP remains a tool whose full value depends on how and for what purposes it is utilized.

Nicolas: The ambition of a data product is primarily to deliver value and solve a user problem. In our example, the RCU addresses issues of fragmentation, inaccuracy, and data accessibility. When data is at best disparate and ununified, or at worst inaccessible, marketing teams cannot fully utilize their tools; the opportunity cost of unexposed insights can be considerable. With the RCU, raw data is transformed into structured information, becoming a significant asset for companies that know how to implement and leverage CDPs. It is the role of the Data Product Manager to ensure that the RCU meets the needs of its direct and indirect users.

Damien: With the RCU, we essentially empower business teams by providing them with the data they need. The RCU aims to be a solution to the challenges of omnichannel engagement.

How do you measure the success of RCU implementation?

Nicolas: To measure the success of RCU implementation, we define specific KPIs. These KPIs include, for example, user engagement, which is based on the adoption rate of the platform. The data product must be well-designed and effectively “marketed” internally to ensure adoption.

It is also possible to analyze the impact on performance, such as marketing campaign effectiveness, in terms of conversion rates and return on investment. Finally, as with any product development, feedback from end users is crucial: we incorporate their insights to continuously refine and optimize the RCU, primarily focusing on two areas: addressing new use cases and integrating new data sources.

In practical terms, the RCU fulfills its promises when it enables the harmonization of customer journeys, smoothing out the silo effects of an organization by providing unified information to various managers.

From a technical standpoint, how do you ensure the accuracy and updating of data in the RCU?

Damien: To ensure data quality, each source must have its own quality control processes. At the RCU level, normalization rules can be applied. When determining the unique customer identifier, governance rules ensure data completeness and updating. The updating of data depends on the use cases defined by the client and the technology used (API, files, etc.).

Data security is a major concern. How do you ensure the protection of sensitive information in the RCU?

Damien: The centralization of data facilitates its management. We can implement specific policies for sensitive information, encryption solutions (both in transit and at rest), and ensure traceability of interactions and modifications. This helps to detect any suspicious activity and maintain data security.

Thank you for these detailed explanations. To conclude, how do you see the future of the RCU in upcoming projects for Converteo’s clients?

Damien: The RCU has a promising future, increasingly integrating features related to AI and machine learning to analyze customer data more deeply, anticipate behaviors, and adapt actions. The RCU will become the main source of data to feed machine learning models or algorithms.

In a world oriented towards omnichannel, the RCU plays a crucial role in unifying customer interactions and ensuring compliance, particularly by consolidating and disseminating consents to marketing tools. Moreover, the RCU is becoming increasingly interoperable with existing ecosystems in our client companies. It often serves as the first building block of a modern “data stack.”

Nicolas: Absolutely. I would add that the challenge lies in adopting a “product” approach for the future development of RCUs. An iterative approach will allow us to quickly adapt an RCU to technological advancements and user needs, ensuring its relevance and effectiveness.

The product approach combines agility, analytics (to analyze RCU usage), and user feedback to continuously improve the product. In the case of data products aimed at external users (for example, the clients of a SaaS data product), the product approach includes contributions from Product Marketing. The same applies to the management of an RCU; to maximize its impact, it is essential to communicate the RCU’s developments effectively to its direct and indirect users.

This is why it is crucial to involve a Data Product Manager in RCU initiatives and to consider the implementation of a CDP as the starting point in the lifecycle of the RCU data product.