Customer 360: definition and use cases

What a Customer 360 is

What are the benefits of Customer 360

How a Customer 360 works

What are the tools that enable obtaining and managing customer data

It provides enhanced customer knowledge, accessible to all teams (marketing, sales, support, etc.), ensuring that everyone is working from a single source of truth.

This allows for effective customer segmentation, predicting their behaviors, anticipating customer loss risk (to limit the churn rate), as well as accurately measuring their satisfaction and loyalty.

It ensures a seamless and personalized omnichannel customer experience. Customer 360 ensures harmony and continuity across various touchpoints, digital or physical. Companies can offer services and products tailored to individual customer expectations, based on their history, context, and preferences, consistently across all activated channels.

It boosts operational efficiency by automating and optimizing customer processes (marketing, sales, customer service, billing, etc.), leading to time and cost savings. Internal collaboration is improved by eliminating redundancies, errors, and conflicts.

It enhances customer service management. With a 360-degree view of information and customer query history, companies can provide fast and high-quality service. Personalizing service based on each customer's profile, journey, and value allows for more adequate responses to their needs.

It allows for effective data management, ensuring constant data updates for real-time decisions based on accurate data. Customer 360 promotes centralized, cleaned, standardized, enriched, and secure customer data. It avoids information silos and facilitates compliance, while allowing in-depth analysis and the creation of relevant dashboards to guide business strategy.

Data Collection: Crucial first step aiming to collect and consolidate customer data from multiple sources, including but not limited to CRM systems, marketing automation tools, sales systems, financial transactions, customer interactions, and social media presence. It is essential to identify all the data that needs to be collected to address all identified CDP-like use cases (e.g. event data, clickstream data or general data from SaaS applications).

Data Ingestion: This step involves incorporating the collected data into a unified database or central warehouse, for instance, a data warehouse. This base acts as a convergence point for all customer information, ensuring simplified accessibility to their interactions, preferences, and histories. It essentially eliminates data silos to provide a 360-degree customer view.

Data Preparation: Next comes the data cleaning and validation phase to ensure accuracy and consistency. The process usually includes removing duplicates, correcting errors, and standardizing formats. It’s about making the customer data usable for anyone. At this stage, the customer profile is often enriched with external data, such as additional demographic information or social media activities, providing a deeper perspective on each customer. It is also possible to add predictive attributes through the use of artificial intelligence. At DinMo, we add various metrics, such as expected additional LTV, churn risk, or the Next Best Offer.

🛍 See our case study with Galeries Lafayette to learn more about our predictive attributes.

Identity Resolution: Finally, the last major step identifies and links customer identities across different platforms to offer a unified customer view (usually through matching different identifiers), encompassing all touch points with the company.

Option 1: Adopt a traditional CDP or SaaS tool. Customer Data Platforms (CDPs) centralize, clean, and analyze customer data from multiple sources. The market offers a multitude of traditional CDPs. Despite their benefits, they can incur significant costs, and especially limit customization, as the data model behind their "Customer 360" is not flexible. CDPs also raise questions about integration with other systems and constraints related to security, compliance, or performance. These “black box” operate within their own realm and restrict you on how you can manipulate your own data.

⚠️ CRM is not recommended either, because it does not contain all the information (transactional, product, behavioral, etc.) and, above all, it imposes rigid data schemas.

Option 2: Create your own Customer 360. This path involves using available tools and technologies, including databases, ETL, APIs, or open-source frameworks, to build a custom Customer 360. While it offers great control over data and processes, this option requires a considerable investment in time, skills, and resources. It also exposes challenges such as data quality management, security, and scalability.

Option 3: Opt for a Composable CDP solution, known as "data warehouse native." These solutions enable the development of your Customer 360 by leveraging your cloud data warehouse, the company's centralized database. They combine the operational simplicity of CDPs and the customization of bespoke solutions, relying on the robustness, security, and performance of the data warehouse.

Additionally, they facilitate key features such as collecting, integrating, preparing, and identifying customer data. By building a custom and centralized repository for their data, operational teams can gain deeper insights into their customers, improve their collaboration with other departments, improve their ROI, and improve their overall efficiency. In today's hyper-competitive business environment, these benefits are more important than ever. By turning their own cloud data warehouse into a real Customer 360, marketing teams can position themselves for success in the years to come.