• Language: Python
• Dashboard Framework: Streamlit (for interactive simulation and visualization)
• Causal Modeling: NetworkX (to construct and visualize causal graphs), custom simulation logic in Python
• Data Handling: Pandas, NumPy (for metric calculations and scenario modeling)
• Visualization: Streamlit native components and matplotlib/Plotly as needed
• Deployment: Hosted directly via Streamlit sharing (cloud deployment)

‍

During my tenure at an E-Commerce company within the User Research team, the fragmented approach to handling key performance indicators (KPIs) caught my attention. Each product manager had their set of KPIs, leading to a lack of a unified framework to understand how these metrics interconnected, particularly in relation to usability and its impact on business outcomes. Despite external benchmarks like UMUX-Lite, our internal metrics lacked cohesion, especially when it came to linking them directly to revenue generation.

Addressing this issue required a two-pronged approach: firstly, to illustrate the interconnections among various KPIs and their collective influence on business progress, with a focus on revenue; and secondly, to create a tool that translated theoretical KPIs into actionable insights for real-time strategic decision-making. This was especially pertinent as the Chief Product Officer emphasized the need to enhance revenue by improving market reach, customer engagement, and our pricing model. Thus, visualizing the causal relationships between these factors became crucial.

The solution began with developing a causal decision diagram to visually represent the factors driving revenue, under the purview of decision-makers like our Chief Product Officer. This foundational step led to the creation of a more sophisticated tool: a decision simulation dashboard.

This dashboard was conceived to dynamically illustrate the potential impacts of strategic choices on revenue outcomes. By adjusting levers such as Marketing Spend, Usability, and Item Discount percentage, the tool simulates various scenarios, projecting their effects on vital metrics like visitor numbers, conversion rates, and revenue.

For instance, setting Marketing Spend at $275,000, Usability at 75% to denote a user-friendly site, and a 10% Item Discount resulted in a projected revenue just under $20 million, underscoring the dashboard's utility in strategic planning. Moreover, the "Graph" feature allowed users to delve deeper into the causal links, showing how adjustments to one lever affected different elements, thus proving the simulation's causal nature.

This initiative, based on data visualization and simulation, aimed to significantly bridge the gap between theoretical and actionable KPIs. It envisioned empowering teams with informed, strategic choices, potentially revolutionizing business tools by making complex data accessible and actionable, and serving as a hypothetical asset in driving towards sustainable growth.

• Created a dynamic simulation tool that allowed executives to test strategic trade-offs and visualize revenue outcomes based on variable adjustments (e.g., marketing spend, usability, discounting).
• Aligned siloed KPIs under a causal framework, improving cross-team understanding of how usability and product changes impact business metrics like conversion rate and revenue.
• Enabled the Chief Product Officer and senior leadership to engage with usability data in strategic planning, linking UX investments directly to financial projections.
• Demonstrated that improving usability to 75%, coupled with targeted spend and discounting, could project revenue gains approaching $20M, showcasing the real-world value of informed metric interplay.
• Bridged the gap between theoretical research metrics and actionable business decisions, providing a reusable model for scenario planning and product experimentation.

‍