What is Customer-Facing Analytics?
Customer-facing analytics (or client-facing analytics) refers to the presentation of data, insights, and performance metrics directly to end users, typically through a user-friendly interface. Unlike traditional analytics (BI), which are primarily used internally by organizations to monitor business operations, customer-facing analytics empower customers by giving them access to key data relevant to their needs or interactions with a product or service.
Customer-facing analytics can come in various forms, depending on the business model and the user experience goals of the organization:
Dashboards:
These are comprehensive tools that allow customers to visualize and interact with their data in real-time. These dashboards can either be developed from scratch by a company's internal development teams or built using specialized third-party solutions designed specifically for customer-facing analytics. Tools like CustomerDashboard.io or similar platforms offer purpose built tools that can be customized to a company's needs, reducing development time and maintenance significantly, and improving functionality.
Embedded Widgets:
Many businesses choose to offer analytics in the form of small diagrams speaded across the app, easily embedded widgets. These are often modular pieces of data visualizations or performance metrics that can be added to a broader user interface, such as a website or a customer portal. Widgets provide a lightweight, customizable option for businesses to deliver actionable insights to users without overwhelming them with complex dashboards.
In-App Metrics:
In many consumer apps, customer-facing analytics are seamlessly integrated into the user interface. For example, food delivery apps may include real-time metrics like estimated wait times or the number of items in an order queue. These numbers provide users with transparency and improve the overall experience by setting clear expectations. Most people think of these as just features and not as customer-facing analytics; however, producing them in a large scale consumer app can require quite a bit of machinery.
How Does Customer-Facing Analytics Differ from Internal Analytics (Typical BI)?
Different Use Cases
Internal analytics (traditional BI) is designed to optimize business processes and aid in decision-making for internal stakeholders like managers and analysts. It focuses on improving operational efficiency, strategy, and long-term planning. On the other hand, customer-facing analytics is built for external users, such as customers. These analytics aim to provide insights that enhance the user experience, build trust through transparency, or help customers make informed decisions, such as monitoring their own usage or service performance. These are often a core part of the product. For example an online ad network needs to report analytics to it's customers about how the campaigns perform.
Real-Time Data vs. Historical Data
Internal BI often works with historical or batch-processed data, with updates occurring at regular intervals such as daily (or nowadays even hourly). This is sufficient for most internal reporting and trend analysis. However, some customer-facing analytics in consumer apps typically requires real-time or near-real-time data to meet user expectations. For example, in consumer apps like food delivery or ride-sharing services, customers rely on live data, such as estimated delivery times, to make decisions. Any delays or inaccuracies can negatively affect the user experience. On the other hand many B2B SaaS apps can get away with longer delays in their customer-facing analytics.
Scale of Data Requests
Internal analytics serves a relatively small number of users, such as data analysts or department heads, meaning that the frequency and volume of data requests are manageable. Customer-facing analytics, by contrast, may need to handle thousands or even millions of users simultaneously. This could mean customers accessing dashboards, real-time metrics, or usage statistics all at once. As a result, the infrastructure must be robust, scalable, and capable of handling large volumes of requests while maintaining fast response times and high availability.
Access Rights and Account Management
Internal BI often involves more relaxed access controls, as many teams or departments within a company may share data access for collaboration. In customer-facing analytics, strict access management is essential. Each customer must only be able to view their own data, making robust user authentication, role-based access control (or in fact complete user account management, and data security extremely important. A failure in this area could lead to data breaches or loss of trust, making it far more sensitive than typical internal BI setups.
Emphasis on Visual Appeal and Branding
For internal BI, visual design is usually secondary to functionality. Analysts and decision-makers prioritize the depth and accuracy of insights over aesthetics. However, in customer-facing analytics, the design and user interface play a much bigger role. The dashboard or widget is often a public-facing tool, acting as a touchpoint for customers. It needs to be visually appealing, easy to navigate, and aligned with the company’s branding. A well-designed interface can improve customer satisfaction and even serve as a competitive advantage, while a poor design could frustrate users and damage the company’s image.
How the Tools and Technologies Differ
When comparing the tools and technologies used in customer-facing analytics versus internal BI, the differences often come down to scalability, data processing, and real-time requirements. The underlying technology stack for customer-facing analytics needs to handle high volumes of concurrent requests, deliver real-time insights, and ensure data security, which leads to different choices in terms of infrastructure. Also the use case is different, if you choose as ready-made solution.
Lower Layers of the Tech Stack (When Doing it Yourself)
In customer-facing analytics, particularly for large platforms with many users, directly querying the data warehouse is often not feasible due to connection limits and query complexity. For example, Amazon Redshift has connection limits, making it unsuitable for handling thousands or millions of simultaneous customer queries. Instead, data is typically pre-processed into the correct format using distributed data processing systems like Apache Spark, and made available for quick access.
Real-time analytics is often achieved by introducing a speed layer, as seen in the Lambda architecture. In this model, the speed layer handles real-time data processing for the recent events, while a batch layer processes larger datasets over time for accuracy. Another approach is to use technologies like Apache Pinot, a real-time distributed OLAP datastore. These are specifically designed for high-speed analytics at scale, providing low-latency query responses.
Most B2B SaaS services do not need to worry about any of these above, since the scalability requirements are far from this magnitude. On the consumer side or in ad tech services it's different.
Ready-Made Tools
For internal BI, the focus is typically on ease of use and deep data exploration, so tools like Qlik, Tableau, and Power BI are popular. These tools excel at providing rich visualizations and deep analytics, but they are not designed to handle the massive scalability and real-time needs of customer-facing applications. While Power BI does offer embeddable widgets, its licensing model is often not feasible for customer-facing use cases involving large numbers of users.
In contrast, purpose-built tools like CustomerDashboard.io offer more scalable and cost-effective solutions for serving multiple customers with a dashboard. These platforms are designed to handle customer-facing use cases and often come with features for managing user access and ensuring performance at scale. Even if you only have 100 customers, managing the accounts with the wrong tool can be a burden.
Summary and Conclusion
Customer-facing analytics is essential for enhancing user experience by providing real-time insights through dashboards, widgets, or in-app metrics. Unlike internal BI, it requires real-time data, scalability, and strict access control, making it a core part of customer engagement.
For most consumer apps, especially those requiring high scalability, it's often best to implement the analytics infrastructure in-house. Technologies like Apache Spark or Apache Pinot are ideal for handling real-time data and high volumes of concurrent users.
However, for B2B SaaS companies, where scalability needs are typically lower, using a purpose-built solution like CustomerDashboard.io is often the most efficient approach. These tools allow for faster implementation, reducing development costs and ongoing maintenance, while still providing a high-quality customer-facing analytics experience.
If you’d like help discovering the right solution for your business, we're happy to assist! Contact us at sales@customercashboard.io to book a free consultancy meeting, where we can explore your needs and recommend the best approach.