Overview
Duration:
2 months
Role:
UX Design Consultant
Responsibilities:
To explore and reimagine ideas and develop concept-driven mockups to inspire direction and support early-stage product thinking–translating complex data models and business processes into a streamlined, user‑centric interface.
Description:
As part of ExxonMobil’s AI Lighthouse initiative, the Revenue Manager application aimed at transforming pricing, forecasting, and margin optimization across the company's global Base Oils and Waxes (B&W), Finished Lubricants (FL), and Cost of Goods Sold (COGS) through AI‑powered insights and user‑driven analytics.
Working from a detailed Business Requirements Document, the project focused on integrating advanced AI forecasting models and real‑time market sentiment analysis and value chain visibility into a unified, intuitive interface for Revenue Managers and Product Optimization Advisors.
Empathize
1
Research and Discovery
Challenge
Problem Statement
ExxonMobil’s global teams were relying heavily on manual processes—Excel sheets, independently collected market data, and ad‑hoc reports.
Users
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Revenue Managers
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Product Optimization Advisors
Analysis
To begin my analysis, I collaborated with Associate Partners to understand existing workflows used by Revenue Managers and Product Advisors.
Pain points were identified from discovery sessions and an analysis of forecasting models and required data sources (ARGUS, SAP HANA, Snowflake, market‑sentiment feeds, etc.).
Painpoints: As-is
1
Limited visibility into forecasts drivers
2
No centralized or intuitive interface for analyzing data
3
Pricing and forecasting were highly manual and inconsistent, with no standardization across regions
4
Opportunity insights were difficult to identify
Design Challenge
Key Painpoints
1
High cognitive load for users interpreting complex data
2
No centralized or intuitive interface for analyzing data
Revenue Managers and Product Optimization Advisors piece together information from reports, Argus data, and multiple internal systems without a cohesive view.
Objective
Project Vision Statement
To design an AI-powered platform that transforms complex forecasting data into clear, actionable insights that facilitate smarter and faster decision‑making for Revenue Managers and Product Advisors.
How
Using standardized data sources, such as ARGUS, the platform would leverage AI to efficiently collect, accurately analyze and forecast data, to inform and guide recommend actions.
Capabilities: To-be
1
Trust the forecasting model and understand drivers
2
Simulate pricing and cost scenarios
3
Improve collaboration across business units
4
Proactively identify Opportunities
My Focus
On this project, I translated a complex, 80‑page Business Requirement Document (BRD) into clear, actionable UX flows and an intuitive, user-friendly digital experience.
Deliverables
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Designed end‑to‑end interfaces for Bases and Waxes (BW), Cost of Goods Sold (*COGS), and Finished Lubricants (*FL)
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Turned complex data into visuals that users can quickly understand
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Designed the UI for the *AI chat assistant enabling users to access forecasting insights through a clear, intuitive chat experience
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Ensured UX alignment across ExxonMobil brand guidlines and operational workflows
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Iterated rapidly based on feedback from Associate Partners and time restraints.
*In Development
Comparative Analysis
I also explored Oil and Gas industry websites to study how complex data visuals are typically presented within the sector.
Bloomberg.com
Oilprice.com
IDEATION
1
Previous Design
Early prototypes were built without UX involvement and failed to meet expectations, Therefore, Associate Partners requested a redesign.
They wanted to ensure the design reflected the needs, decision‑making patterns, and workflows of actual Revenue Managers and Pricing Advisors.
Finished Lubes Price Forecasting
The examples show the Finished Lubes price forecasting page, both in its initial state and with a product group selected to display its component details.
Finished Lubes Price Forecasting (Product Group Selected)
Where Users Struggled
The interface felt visually unrefined, resulting in a look and feel that did not resonate with Associate Partners. The UI lacked an overall engaging user experience.
Unclear visualizations left users unsure about understanding forecast drivers, making it difficult to quickly grasp key pricing trends and opportunities.
Define
2
Previous Prototype
The early prototype was built without UX involvement and failed to meet expectations, therefore, Associate Partners requested a redesign.
The examples show the Finished Lubes price forecasting page, both in its initial state and with a product group selected to display its component details.
They wanted to ensure the design reflected the needs, decision‑making patterns, and workflows of actual Revenue Managers and Product Advisors.
Finished Lubes Price Forecasting
Finished Lubes Price Forecasting (Product Group Selected)
Where Users Struggled
1
The interface felt visually unrefined, resulting in a look and feel that did not resonate with Associate Partners. The UI lacked an overall engaging user experience.
2
Unclear visualizations left users unsure about understanding forecast drivers, making it difficult to quickly grasp key pricing trends and opportunities.
Ideate
3
Wireframes
An AI prototype was generated for the purposes of reviewing the output to quickly identify useful components.
In the Mural workspace, we used them as a starting point to brainstorm and develop low-fi wireframes.
Low-Fidelity Wireframes
Home Page
B&W Forecast Page
Historical Data Component
Medium-Fidelity Wireframes
FL Price Optimization Page
AI - Poweres Analys
Historical Data Component
4
Interaction Design
User Flow Examples
To translate the complex forecasting and pricing requirements into clear, actionable user experiences, I produced multiple structured user flows for three core areas of the Revenue Manager application.
B&W User Flow for Forecast Exploration
*B&W prototype completed; FL, COGS and AI Chat prototypes forthcoming.
*B&W flow guides the user through:
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Landing on the B&W dashboard
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Selecting a region and marker
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Reviewing the opportunity summary report
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Adding conditional filters
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Reviewing detailed insights or exporting the analysis
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Downloading, exporting or sharing insights
FL User Flow for Price Opportunity Identification
This flow guides the user through:
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Landing on the FL dashboard
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Selecting a Product Group
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Adjust forecast drivers
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Analyze impacts
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Run a Scenario Planning Report
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Downloading, exporting or sharing insights
COGS User Flow
COGS flow guides the user through:
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Landing on the COGS dashboard
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Selecting a Product Group
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Adjust forecast drivers
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Run a What‑if Analysis Report
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Downloading, exporting or sharing insights
Prototype
5
Design System Component Examples
Custom UI Components
In addition to leveraging the ExxonMobil UI library, I developed custom components to support the application’s functional requirements.
6
Final Output
Revenue Manager BW Forecasting
The Bases and Waxes (B&W) landing page mockup highlights key components, including the (1) Sidebar Menu, (2) Impacted Markers, (3) Forecast Analysis panel, and (4) Data Insights tabs.
Pop-up Windows
Pictured are the pop-up windows for Selecting Conditions and of the Opportunity Summary.
Client Feedback
1
Menu
The Menu sidebar is the primary navigation providing access to the Home screen, B&W anding page, COGS, and FL landing Pages.
2
Impacted Markers
At the top of the B&W page are the AI-powered Impacted markers displaying the most important pricing insights.
3
Forecast Analysis Panel (Conditions Selected)
The Forecast Analysis panel allows users to set filter conditions for the product model.
4
Data Insights Tabs
The Data Analysis tabs allow users to navigate between the product model Overviews, Forecast Analysis, Impact Tracking, Sentiment Analysis, and Market Indicators views.
Empathize
1
Research and Discovery
Challenge
Problem Statement
ExxonMobil’s global teams were relying heavily on manual processes—Excel sheets, independently collected market data, and ad‑hoc reports.
Users
-
Revenue Managers
-
Product Optimization Advisors
Painpoints: As-is
1
Limited visibility into the drivers behind forecasts
Made it difficult to trust model outputs or understand what was influencing price recommendations.
2
No centralized or intuitive interface for analyzing data
Revenue Managers and Product Optimization Advisors piece together information from reports, Argus data, and multiple internal systems without a cohesive view.
3
Pricing and forecasting processes were highly manual and inconsistant, with no standardization across regions.
Each region relies on different workflows, spreadsheets, and subjective judgment — leading low and reactive pricing decisions, and limited accuracy.
4
Opportunities for margin improvement or volume capture were difficult to identify
Existing tools provided no automated insights, impact analysis, or scenario‑planning capabilities to guide decision-making.
Summary
7
User Pain Points Addressed
1
Painpoint
Limited visibility into forecasts drivers
Painpoint Addressed
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Improves visibility into price drivers: Users instantly see which markers are most impacted and why, addressing the lack of transparency that previously made forecasts difficult to trust.
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Reduces manual analysis: Instead of sifting through Argus data or spreadsheets, key insights are highlighted upfront.
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2
Painpoint
No centralized or intuitive UI for analyzing data
Painpoint Addressed
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Keeps essential tools accessible: Users can quickly to navigate to different tools within the application using the Sidebar Menu. This brings improved organization, accessibility, structure, and clarity where users previously had to piece together manually.
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Makes complex data digestible: Instead of overwhelming users with all markers at once, pagination breaks the content into manageable segments.
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Makes the user's experience enjoyable: The new UI elevates both aesthetics and usability, something the earlier prototype lacked entirely.
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3
Painpoint
Pricing and forecasting were highly manual and inconsistent, with no standardization across regions
Painpoint Addressed
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Creates a centralized space for analysis: Users no longer need multiple sources to understand forecast drivers—everything is consolidated into one functional area.
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Creates a centralized, standardized entry point: Users have a centralized site for reviewing prices and forecasts consistently across all regions. This creates trust and data accuracy.
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Gives users control over the data they see: The “Add Conditions” pop‑up allows users to customize their view based on needs, resolving the frustration of rigid, developer-designed tables.
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Supports faster pattern recognition: Active conditions displayed above the table make it easy to filter and compare segments of data on demand.
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Enables smarter, targeted decisions: Users can isolate products, markers, or model health issues that require action—something they could not do efficiently before.
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4
Painpoint
Opportunity insights were difficult to identify
Painpoint Addressed
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Provides immediate context and rationale: The Opportunity Summary powered by AI gives users a clear narrative around forecast impacts, trends, and drivers.
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Addresses poor look and feel: Clean, modern, branded components replace the unappealing and outdated interface that executives disliked.
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Supports proactive decision‑making: Users can quickly identify margin opportunities, something the existing tools failed to highlight.
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Improves clarity and usability: Pop‑ups isolate important insights without overwhelming the user, reducing cognitive load.
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Lessons Learned
Collaboration with SMEs is invaluable
Frequent check‑ins with Associate Partners helped ensure the UI reflected real decision‑making processes, not just theoretical ones.
Flexibility empowers users
Providing ways for users to customize conditions, filter views, and dive deeper into data insights gave them more control over their analysis, significantly reducing friction and supports diverse regional workflows.
AI features must be transparent to build trust
Users will not rely on AI forecasts unless they understand the drivers behind the predictions. This project reinforced the need for explainability and clear data visualization.
Complex data requires clear and intentional design
Working with complex visual data highlighted how easily it can overwhelm users. Visual clarity, hierarchy, and thoughtful interaction design are essential to making analytics tools intuitive and trustworthy.