Redesigning Revenue Manager

This project was completed over an 8-week period, with each phase carefully planned to ensure comprehensive research, ideation, and design.

Large-scale management and organization systems play a crucial role in industries that handle complex operations and data management. In energy data analysis specifically, professionals often encounter inefficient workflows and unintuitive interfaces that make their tasks more challenging to navigate.

Our main stakeholder, who works at Calpine Energy Solution, uses a traditional Revenue Management (RM) system that allows data analysts to review customer records, maintain databases, and execute various analysis reports. However, the current system is outdated, difficult to navigate, and often freezes when handling large data sets.

Given the increasing volume of data and the growing reliance on technology for decision-making, improving the user experience of these systems became a necessity for our stakeholder. Our team was tasked with redesigning the interface to create a more intuitive, efficient, and error-free workflow for their data analysts.

Research Methods

We conducted interviews with two key stakeholders. Our goal was to understand the current software used for large-scale data management and the challenges that stakeholders face in their day-to-day operations.

Stakeholder Interviews

Our semi-structured protocol included:

• Introduction: Introducing the goal of the study and obtaining consent to record/transcribe the interview.
• Background: Gathering information about their daily work and main tasks using specific software.
• Observation: Asking stakeholders to walk us through their typical tasks using the software to identify breakdowns and pain points in their workflow.

Key Findings

Our primary stakeholder is a Data Analyst for Calpine Energy Solution. Their responsibilities include:

• Analyzing customer records and ensuring accurate account setup
• Generating and reviewing exception reports to identify errors
• Performing correlation analysis on billing, utility, and customer service data
• Communicating with other departments to ensure proper functionality

Throughout their day, they use multiple software applications, but the two main ones are:

• Revenue Manager (RM): Used to access the revenue management application to ensure proper billing processes
• Calpine Webtool: An in-house tool for employees to access utility data and other resources

Pain Points

• The RM software is outdated and often freezes when working with large data volumes
• Tabs in the RM software tend to switch positions after the user changes tabs, causing confusion and errors
• The toolbar in RM is confusing as the functions are not clearly labeled or understood
• Access to records can be disrupted if background processes fail, leading to delays
• Searching for specific customer information is tedious, especially when using long account numbers

We conducted a competitive audit of similar data analysis tools to identify best practices and potential improvements for our redesign. We compared three major products in the energy data analysis space:

Features	Tableau	EnergyCap	DNV
Web Based	Yes	Yes	Yes
Learning Curve	Highest	Lowest	Moderate
Data Visualization	Excellent	Standard	Limited
Real-Time Monitoring	Limited	Yes	Yes
Dashboard Customization	High	Moderate	Read Only
Large Datasets	Struggles	Handles Well	Handles Well
Collaboration	Yes	Yes (not live)	No

Key Insights

• Tableau offers the most customizable and user-friendly interface, which should be a priority for our redesign
• EnergyCap's strengths are in its simplicity and ability to work with large datasets without slowing down. This shows the importance of optimizing layouts to ensure intuitiveness.
• DNV's ability to handle very large datasets is very popular. However, it has very low customizability and lacks visuals.
• Overall, Layout optimization is essential for ensuring intuitive navigation across all platforms

These insights guided our design decisions, helping us focus on creating an interface that combines the best aspects of these tools while addressing the specific needs of energy data analysts.

Based on our research and competitive analysis, we developed UX flows to map the user journey through the redesigned interface. We focused on the most common tasks performed by data analysts:

Key Flow Components:

1. Enter the LDC account number that the customer is under
2. Search for and select a customer
3. View customer information (city, state, zip, etc.)
4. Access the customer's account information
5. View the customer's invoices
6. Review and confirm any changes made

This flow addresses the main pain points identified in our research:

• Simplifies the search process with multiple entry points (account number, name, etc.)
• Provides a clear, logical progression through tasks
• Reduces the likelihood of tab-related confusion by creating a more intuitive workspace
• Allows for easy confirmation of changes to prevent errors

We developed two distinct approaches for our low-fidelity wireframes:

Prototype 1: Tab-Based Interface

The first approach featured a simple, tab-based environment that focused on:

• Clear organization with tabs for different types of information
• An improved toolbar with illustrative icons and descriptions
• Dedicated workspaces for each type of task

Prototype 2: Node-Based Interface

The second approach took a more experimental direction with a node-based canvas that offered:

• Visual representation of connections between information
• More flexibility in workspace arrangement
• Dynamic, customizable working experience
• Better visualization of relationships between data points

We also explored a hybrid approach that combined elements from both designs, which later influenced our final direction.

Tools Used

For our low-fidelity prototypes, we used:

• Figma: Our primary tool for wireframing and collaboration
• Paper sketches: For initial ideation and rapid concept exploration

Testing Method

We conducted user tests with our primary stakeholders to evaluate both prototypes. Participants were asked to complete the following tasks:

1. Enter an LDC account number
2. Search for and select a customer
3. Find customer information (city, state, zip, etc.)
4. View the customer's account information
5. View the customer's invoices

Key Findings

The testing revealed a strong preference for Prototype 2 (node-based interface) with some elements from Prototype 1:

• Features users liked:
  • The general information page layout from Prototype 1
  • The overall design and node-based workspace of Prototype 2
  • The larger, more visible toolbar from Prototype 1
  • The advanced search function in Prototype 2, which allows searching by multiple criteria
• Suggested improvements:
  • Add a tab about electrical usage of customers from start to current date of account
  • Include more visualization options for trends analysis
  • Provide clearer differentiation of the node connections

Based on this feedback, we decided to focus on the node-based design while incorporating some elements from the tab-based approach, particularly the customer information layout that users found intuitive.

Our high-fidelity prototype focused on refining the node-based workspace concept while addressing the feedback received during user testing. We maintained a clean, intuitive layout while adding more customization options and clearer visual hierarchy.

Design Process in Figma

Using Figma as our primary design tool, we created a comprehensive high-fidelity prototype with interactive elements. This allowed us to simulate the actual user experience and test more detailed interactions before implementation.

Key Screens:

Design Decisions

• Node-based organization: Allows for visual representation of relationships between data modules
• Advanced search functionality: Provides multiple entry points (customer name, ID, account number)
• Data visualization: Incorporates charts for electrical usage trends
• Customizable workspace: Users can arrange modules according to their workflow
• Change confirmation: Clear view of changes before committing them
• Color scheme: Soft colors that are easy on the eyes for prolonged use

Based on additional feedback from peers, TAs, and stakeholders, we made several key improvements to our high-fidelity prototype:

Our redesigned Revenue Manager addresses the key pain points identified in our research while incorporating best practices from competitive products and feedback from user testing.

Key Features:

The final design creates a more efficient workflow by:

• Reducing the cognitive load required to navigate complex data
• Allowing users to customize their workspace based on task requirements
• Providing clear visual relationships between different information modules
• Simplifying the search process with multiple entry points
• Offering visualization tools to identify trends more easily
• Including safeguards to prevent accidental changes

Users can now complete their analysis tasks more efficiently, with fewer errors, and with a reduced learning curve for new team members.

This project was a collaborative effort by a team of dedicated designers who brought diverse perspectives and skills to the challenge.