Dynamic Performance Monitor

In-depth Data Study and Core Metrics Identification

At the initial stage of the project, I carefully examined the meaning of each field to ensure a clear understanding of the information they represented. Simultaneously, I analyzed the intrinsic connections, calculation principles, and navigation rules among the data, laying the groundwork for subsequent metric selection and dashboard design.

For example, when I discovered that two sets of data were related and their total summed to 100%, I planned to use a pie chart for visualization. For information with hierarchical relationships, I considered highlighting it through visual contrasts and size variations, enabling users to quickly grasp the key points. Furthermore, I analyzed users' query habits across multiple dimensions such as time, type, and status, aiming to satisfy modular and dynamic time query conditions in the page design.

Based on this research, I clarified the core direction of the metric system:
• North Star Metric: Revenue, serving as the core measure for overall business performance.
• Core Metrics: Profit, order count, room bookings, etc., which are key factors directly impacting revenue.
• Sub-metrics: Further breakdown of core metrics, such as channel-specific order conversion rates, revenue percentages by region, and average order value per order.

These metrics laid the foundation for comprehensive business analysis and clarified the priorities and logical relationships for subsequent design.

Designing Data Hierarchies and Analysis Dimensions

To assist users in analyzing business performance from multiple perspectives, I categorized the data into different hierarchies and dimensions:
• Time Dimension: Supporting year-over-year and month-over-month analyses to help users observe trend changes.
• Regional Dimension: Presenting comparisons of revenue and profit across regions, allowing users to intuitively identify regional differences.
• Channel Dimension: Analyzing order volume, conversion rates, etc., across different sales channels to optimize marketing strategies.

During the design process, I deeply considered how users would apply this data in real-world scenarios. For example, how users would use certain data to solve specific problems, and the frequency and importance of their data views influenced my adjustments to data display priorities. Taking the "Sales Target Achievement" module as an example, I provided users with a comparative view of target achievement rates by region, using a combination of bar and line charts to dynamically showcase the gap between actual achievements and targets.

Ensuring Metric Actionability and Optimizing Data Application Scenarios

When constructing the metric system, I paid special attention to the actionability of the data, i.e., whether each metric could directly guide user actions.
• For the Revenue metric, I designed modules showcasing source distribution and trend changes, helping users quickly identify high-value channels.
• For the Order Count metric, I added comparative analyses by channel and time period, enabling users to discover reasons for order volume declines.
• For the Profit Margin metric, I broke down details such as costs and channel commissions, intuitively presenting opportunities for cost optimization.

Through these designs, I not only made the data more insightful but also helped the team translate analysis results into specific actions. For instance, when pinpointing a persistent rise in order cancellation rates for a certain channel, I used multi-dimensional breakdowns of metrics to identify that the cause was promotional activities not matching the actual needs of target customers, ultimately optimizing the activity strategy.

Data Visualization Design: From Metrics to Experience

In the final dashboard design, I focused on combining visual design with user experience, ensuring that the data was not only accurate and easy to understand but also intuitive and interactive.
• Chart Selection: Using pie charts to display revenue percentages by channel, bar charts to compare sales across different regions, and line charts to show revenue trends.
• Color and Style: Adopting clear color coding to make different labels visually distinguishable.
• Hierarchical Layout: Drilling down from overall data to various regions and channels, helping users quickly locate issues.
In the "Channel Performance Analysis" module, I designed a donut chart to provide an at-a-glance view of revenue percentages for all channels, while supporting clicks on each region to further view specific data details. Additionally, I optimized the time query function based on user needs, allowing users to choose global time queries or independent time queries for each module, enhancing flexibility and efficiency.

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