portfolio

Platform Analytics Adoption Guide

overview

Created a user-friendly guide to help stakeholders and ServiceNow users transition from the legacy reporting tool to Platform Analytics.

Role: Content Designer / Documentation Lead
Goal: To support self-service learning and reduce dependece on the ServiceNow team during an upcoming platform change

problem

ServiceNow’s legacy reporting tool was being retired and replaced with Platform Analytics, a new reporting experience unfamiliar to many users. Stakeholders needed clear guidance on how to create visualizations and dashboards in the new interface without disrupting their existing workflows.

process

– Researched Platform Analytics through training courses, release documentation, knowledge articles, and community resources
– Evaluated the new user interface and identified common user tasks and learning needs
– Designed and wrote a step-by-step guide focused on creating visualizations and dashboards
– Organized content around user goals rather than system features
– Added tips, best practices, and contextual guidance to help users succeed beyond basic task completion

solution

Rather than creating documentation that simply listed clicks and screenshots, I focused on helping users understand:
– When to use Platform Analytics
– How dashboards and visualizations support reporting goals
– Common pitfalls and troubleshooting considerations
– Best practices for creating effective reports

outcome

– Provided users with a centralized learning resource during a major platform transition
– Increased self-service capabilities for reporting and dashboard creation
– Reduced the need for one-on-one support from the ServiceNow team
– Helped prepare stakeholders for adoption of the new Platform Analytics experience

reflection

This project reinforced the importance of validating documentation with users. Even when a workflow seems intuitive to the person creating it, observing others follow the instructions can reveal gaps, assumptions, and opportunities to improve clarity.

Improving Internal Workflow in ServiceNow

overview

Redesigned an internal call-logging workflow in ServiceNow to capture more meaningful data about customer inquiries and support decision-making.

Role: Analyzed user needs, restructured the inquiry form, and led workflow improvements in collaboration with the support team
Goal: To enable more accurate reporting on call types so leadership could make more informed decisions to reduce call volume

problem

The support team handled a high volume of external customer calls, but the existing inquiry form did not capture why customers were calling. As a result:
– Reporting lacked meaningful detail
– Trends in call drivers were unclear
– Leadership couldn’t identify actionable ways to reduce call volume

process

User Research: Spoke directly with team members to understand how calls were being logged and were gaps existed
Audit: Reviewed the existing ServiceNow inquiry form and identified missing data points
Synthesis: Determined that call context was the key missing variable
Design: Proposed updates to the form structure to better categorize call reasons

solution

Introduced a standardized categorization system within the inquiry form, enabling consistent data capture across the team
– Standardized how call types were recorded
– Reduced ambiguity in data entry
– Trained team members on consistent usage of new field

outcome

– Transformed previously unstructured call logs into clear, categorized data
– Enabled reporting on call volume by specific customer need
– Identified key drivers behind incoming calls
– Created a data-backed recommendation for an Audio Attendant system

reflection

This project reinforced that improving data structure is a form of UX design. By collaborating closely with subject matter experts and focusing on data quality, I was able to turn an unclear problem into a measurable opportunity for improvement.

Chatbot User Experience

overview

Improved a public-facing chatbot by optimizing response accuracy and refining the natural language understanding (NLU) model to better align with real user behavior.

Role: Analyzed user interaction data, rewrote chatbot content, expanded NLU training inputs, and tested conversational flows
Goal: To increase response accuracy, reduce user confusion, and build trust in the chatbot’s reliability

problem

Users frequently received unhelpful or incorrect responses due to gaps in intent recognition and unclear or outdated content

process

User Research: Analyzed chatbot interaction data and user inputs to understand how people were actually phrasing requests
Audit: Evaluated existing chatbot responses, intent coverage, and conversation flows to pinpoint gaps in the NLU model, outdated content, and unclear or unhelpful replies
Synthesis: Identified opportunities to expand intent recognition and improve how information was structured and delivered
Design: Expanded and refined NLU training data with more representative utterances, restructured conversation topics for better alignment with user intent, and rewrote responses to be clearer, more accurate, and easier to navigate. Tested updates and iterated based on performance

solution

– Introduced more comprehensive intent coverage by expanding the NLU model
– Restructured conversation pathways to better guide users to relevant answers
– Replaced unclear or outdated responses with more precise, user centered content

outcome

– Users received more relevant answers with fewer breakdowns in conversation flow
– Interactions became more predictable, reducing frustration and repeated queries
– Chatbot delivered clearer, more dependable information, improving overall usability

reflection

This project reinforced that conversational design is iterative. A chatbot isn’t a one-time launch; it requires continuous analysis, content refinement, and model training to remain accurate and useful over time.

From “Infamous” to Iconic: A proposal

what

Send a new type of notification that is more positive and personal, yet effective. Accomplish this by taking the already-existing Duolingo characters and developing them further.

why

Friends help friends grow. By further developing the characters and making them more personal, a bond between them and the user will be formed. It will be that bond and the helpful reminders from their “friends” that will encourage the user to come back.