Sales Data Simulator Dashboard Design for Ambev
Client: Ambev
Year: 2020
Contracted by: BigData
Project Duration: 3 weeks
Objective:
To design a user interface for a sales data simulator tool already developed by data scientists and developers, enabling sales representatives and managers to track and predict product sales based on the latest sales data.
Challenges
Usability Limitations: The existing tool, although functional, had significant usability issues that needed to be addressed.
Data Complexity: Integrating and presenting complex sales data in a user-friendly manner.
User Experience: Designing an intuitive interface for users with varying levels of technical expertise.
Solution
I designed a comprehensive user interface for the existing sales data simulator tool that includes the following features:
User Input Filters: Users can apply various filters (e.g., product category, region, time period) to customize their view of sales data.
Interactive Dashboard: The dashboard displays real-time sales data and predictive analytics for future sales periods.
Visualization Tools: Graphs, charts, and heat maps to help users easily interpret data trends and insights.
Predictive Simulation: The simulator forecasts future sales based on historical data and selected filters, providing valuable insights for sales strategies.
Outcome
The redesigned dashboard enabled Ambev’s sales teams to:
Improve Sales Forecasting: More accurately predict future sales trends and adjust strategies accordingly.
Enhance Decision-Making: Make informed decisions based on comprehensive data insights.
Increase Efficiency: Quickly access and analyze sales data through an intuitive interface.
Tools and Technologies Used:
Design Tools: Adobe XD
Key Learnings
User-Centered Design: The importance of involving users throughout the design process to ensure the final product meets their needs.
Data Visualization: Effective data visualization techniques can significantly enhance user understanding and decision-making.
Iterative Testing: Continuous user testing and iteration are crucial for refining complex data-driven tools.