Metric Explorer

Enterprise Tool • Data Visualization

Metric Explorer is an internal tool designed to track key business metric performance and decompose metric change to locate the problem area.

! Important: data in this page is for illustration purpose only and doesn't represent any real business numbers.

time

2019

client

Uber

role

Lead Designer

data in this page is for illustration purpose only and doesn't represent any real business numbers.

Project Summary

Metric Explorer is a new application I designed for Uber's budgeting and performance tracking platform. As the solo designer, I brought the project from wireframes to final design and conducted 2 rounds of research within 6 weeks.

Background

Problem

While Uber has identified the few most critical business KPIs, our response time to diagnose significant decline in those metrics has historically been delayed between 2-6 weeks. The costs of delayed diagnosis in key metrics changes have resulted in enormous extra cost.

What caused this problem?

Lack of consistent monitoring framework

Monitoring and analysis is usually ad hoc, and different team members would grab different methods and tools

Discrepancy in
data source

Each team writes their own queries to build their own spreadsheets, resulting in difficulties in communication and lack of trust

Highly manual data collection and analysis

Spreadsheets from queries is the most used tool to collect and analyze data, which requires massive manual manipulation before getting insights

Product Definition

To solve the problems, we planned to add an application to Uber's budget and performance tracking platform. This application helps contextualize metric changes by identifying WHERE and WHY a particular metric change is happening.

Key Features

Change Decomposition
Anomaly Detection
Segment Time Series
Correlation Analysis

ihub%20sketch%203_edited.jpg

Research

Concept Validation

To accommodate a shorter project timeline, we decided to replace fundamental research with concept validation. I started sketching some ideas and turned them into low fidelity mockups. Then we visited 12 target users to collect feedback.

In our conversations with the users, we also discovered two different type of use cases. I summarized them into two personas.

Research Outcome

2 Personas

Jeremy

Strategy and Planning

Analyze data overtime to better understand what drives metric change

Anita

Incentive Allocation

Examine metrics correlations in top cities to see whether certain budget is impacting performance

Design Iteration

Explorations

Design Iteration

Final Outcome

More details available upon request.

© 2020 by Fiona (Muyao) Ding. All rights reserved.