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What is customer facing analytics?

Hemanth RajuMar 17, 2 min read

Imagine you are an E-commerce seller on Amazon who sells sneakers. Though the seller reporting dashboard is provided, it is built for more generalist purpose. Since your need was to understand some typical questions related to the sneaker industry and sales, like the one below. How do sales of men’s sneakers compare to women’s sneakers in terms of revenue and units sold?

How do sales of men’s sneakers compare to women’s sneakers in terms of revenue and units sold?

Traditional reporting dashboards give a general picture, but customer-facing analytics gives users a personalized and deep look at data. It lets people interact with the data directly. With customer-facing analytics, users see pre-made data models and visuals, which they can explore interactively. This could be through dashboards, apps, or even chat, making it easier for users to understand and engage with the information.
The image illustrates the process through which an online seller can create a personalized dashboard effortlessly using EnqDB's natural language capabilities.

Customer-facing analytics offer several benefits:

Enhanced User Engagement: By providing a personalized and interactive experience, customer-facing analytics captivates users and encourages them to explore and interact with the data, fostering deeper engagement.
Improved Decision Making: Empowering end users with access to relevant data and insights enables them to make informed decisions quickly, leading to more effective and efficient outcomes.
Faster Insights: By eliminating the need for intermediaries or manual processes, customer-facing analytics accelerates the delivery of insights, enabling users to access real-time or near-real-time information.Increased
Transparency: Providing users with direct access to data promotes transparency within the organization, fostering trust and accountability among stakeholders.
Supports Self-Service: With user-friendly interfaces and intuitive tools, customer-facing analytics empowers users to access and analyze data independently, reducing the burden on IT or data teams.
Drives Innovation: By facilitating experimentation and exploration with data, customer-facing analytics encourages innovation and discovery, potentially uncovering new opportunities or insights.

Customer-facing analytics features:

Interactive Dashboards: Choose a platform that offers interactive dashboards, allowing end-users to explore data, visualize trends, and derive insights through interactive elements such as filters, drill-downs, and tooltips. Interactive dashboards enhance user engagement and facilitate data-driven decision-making.
Natural Language Querying: Prioritize solutions that support natural language querying, enabling users to interact with data using everyday language. This feature simplifies the data exploration process, empowering users to ask questions and receive instant insights without the need for complex queries or technical expertise.
Customizable Visualizations: Opt for a solution that offers a wide range of customizable visualizations, including charts, graphs, maps, and tables. Customizable visualizations allow users to tailor the presentation of data to their specific needs and preferences, enhancing the effectiveness of data communication and analysis.
Embedded Analytics Capabilities: Look for a platform that seamlessly integrates with existing applications and workflows, offering embedded analytics capabilities. Embedded analytics allow organizations to embed analytics directly into customer-facing applications, providing users with access to insights within familiar environments, such as websites or mobile apps.

Strategies for delivering Customer-facing analytics

Delivering customer-facing analytics requires a strategic approach to ensure that insights are not only accurate and relevant but also easily accessible and actionable for end-users. Here are some key strategies for effectively delivering customer-facing analytics:
Understand User Needs: Begin by understanding the specific needs and preferences of your target audience. Conduct user research and gather feedback to identify the types of insights and analytics that are most valuable to them. This will help you tailor your analytics solutions to meet their requirements effectively.
Simplify Data Access: Make it easy for users to access the data and analytics they need. Implement intuitive interfaces and navigation systems that allow users to quickly find the information they are looking for. Consider offering self-service options that enable users to generate their own reports and dashboards without requiring assistance from IT or data specialists.
Personalize Insights: Personalization is key to delivering customer-facing analytics that resonate with users. Customize the content and presentation of analytics based on user roles, preferences, and behaviors. Provide recommendations and suggestions that are relevant to each user's unique needs and objectives.
Ensure Data Accuracy and Consistency: Data quality is essential for building trust and credibility with users. Implement robust data governance processes to ensure that data is accurate, reliable, and consistent across all analytics solutions. Regularly monitor and validate data to identify and address any issues or discrepancies.
Visualize Data Effectively: Use data visualization techniques to communicate insights in a clear, concise, and compelling manner. Choose visualizations that are easy to understand and interpret, such as charts, graphs, and heatmaps. Provide interactive features that allow users to explore data further and gain deeper insights.

Use Cases for Customer facing Analytics

Customer-facing analytics can be applied across various aspects of a SaaS business to drive customer satisfaction, retention, and growth. Here are some key use cases:
Interactive Data Exploration: Customer-facing analytics empower customers to explore and analyze data interactively within the application interface. By providing intuitive tools for data visualization, filtering, and drill-down, customers can gain deeper insights into their data without needing to rely on external tools or support.
Performance Benchmarking: SaaS applications can incorporate customer-facing analytics to benchmark customers' performance against industry standards or peer benchmarks. This helps customers understand how their performance compares to others and identify areas for improvement or optimization.
Predictive Analytics and Recommendations: Customer-facing analytics can leverage predictive modeling techniques to provide customers with personalized recommendations or insights based on their historical data and usage patterns. This proactive approach helps customers anticipate future trends or opportunities and make informed decisions accordingly.
Data Collaboration and Sharing: Customer-facing analytics platforms often include features for data collaboration and sharing, allowing customers to collaborate with teammates, stakeholders, or clients on data analysis projects.
Custom Reporting and Dashboards: Customer-facing analytics platforms may offer customizable reporting and dashboarding capabilities that enable customers to create, customize, and share their own reports and dashboards tailored to their specific needs and preferences. This self-service approach empowers customers to build and explore data views that are relevant to their unique requirements.

Customer facing Analytics: Build or buy?

Determining whether to build or buy customer-facing analytics presents a pivotal decision in the realm of embedded analytics. Many organizations lean towards procuring an established solution to seamlessly integrate with their existing software. This choice often emerges as the most straightforward and cost-effective route, saving both time and resources.
Before committing to a purchase, however, it's crucial to ascertain if the chosen analytics solution aligns with your specific business requirements. Opting to buy not only streamlines the integration process but also frees up valuable developer time, enabling them to concentrate on core business functions and original objectives.
Choosing the buy option ensures maximum security and expedites time-to-market, offering a swift return on investment (ROI). Studies have shown that analytics solutions deliver significant returns, with each dollar invested yielding an impressive return. This translates to substantial benefits for both your organization and its clientele.
Conversely, developing in-house analytics capabilities may be the preferred path for organizations equipped with the necessary budget and human resources. While this approach entails a higher initial investment and longer development timeline, it affords greater customization and control over the analytics solution's features and functionalities.
The cost of building a bespoke analytics solution can be substantial, with an estimated average expenditure of several hundred thousand dollars and a project duration of around seven months. In contrast, pre-built embedded solutions offer competitive pricing, typically ranging from tens of thousands to a few hundred thousand dollars annually.
Ultimately, the decision to build or buy customer-facing analytics hinges on your organization's unique circumstances, priorities, and long-term objectives. Both options offer distinct advantages and considerations, and careful evaluation is essential to determine the most suitable path forward.

Additionally, as emphasised by Nucleus Research, analytics presents an opportunity for a significant return on investment, with a remarkable ratio of $13 generated for every dollar invested.
In the realm of Software as a Service (SaaS), customer-facing analytics are becoming increasingly indispensable. These analytics provide valuable insights which are crucial for optimizing product offerings and enhancing user experience.
We at  EnqDB  help SaaS businesses to delight their customers with AI analytics under a day.