Legacy proto-personas created by the prior UX team in 2021 help support our secondary research. However, the team wasn't sure if the personas were current since the product had recently been launched. Marlon began validating the proto-personas, re-crunching the data, and interviewing business and product managers. Since they were left incomplete, Marlon began developing the remaining personas.

In due time, the objective is to create true User Personas that reflect our current users.

Now that the UX Team is familiar with the FOX claim processing application, we created a list of survey questions, ranging from open-ended to closed-ended, qualitative to quantitative, to help validate our insights. Some questions were straightforward, some open-ended, and others tried to elicit an emotional response.

The 24-question survey questionnaire was emailed to 140-150 AARP Claims Distribution List users. Eleven participants responded (approx. 7.9% of the total pool), providing valuable feedback. We then selected a few key participants for an in-depth study, to gather more detailed user feedback.

Also referred to as contextual or ethnographic interviews, the UX team conducted hour-long observational studies with 17 users from various areas of the business to gain insights into real-life interactions with the FOX and COMPAS systems.

Adopting a "fly on the wall" approach, we observed how claims processors performed their tasks, asking targeted questions about specific workflows and processes. Trainers shared their methods, shortcuts, and ideal "happy paths," while also highlighting limitations and common frustrations within the systems.

In-Depth User Interviews

During the deep dive phase of the project, we conducted interviews with three participants to further explore and clarify their experiences. The UX team crafted a script and questionnaire based on insights from the initial user survey, focusing on ambiguous feedback and leveraging the participants' willingness to engage. With their consent, we recorded the sessions using Dovetail, enabling effective capture, analysis, and management of user feedback.

The 30-minute interviews, conducted over two weeks, involved a collaborative team approach:

• Marlon moderated the interviews, steering discussions to uncover valuable insights.

• A UX colleague provided support with development and accessibility expertise.

• The UX manager contributed business-level perspectives to ensure alignment with organizational objectives.

To synthesize the findings, Marlon's team utilized task analysis, thematic analysis, sentiment analysis, and affinity diagrams, transforming raw data into actionable insights to inform strategic hypotheses. Leveraging Marlon's AI Playbook further augmented the research, expediting and enhancing the depth of the findings to ensure meaningful outcomes.

To derive meaningful insights from qualitative data, I employed a multifaceted approach to uncover patterns, themes, and sentiments that represent the core experiences of claim examiners.

1. Task Analysis:

   • Examined the detailed steps claim examiners take to complete workflows, identifying inefficiencies and pain points that disrupt productivity.

   • Highlighted recurring friction points, such as inconsistent tabbing functionality and redundant processes, directly impacting their ability to meet quotas.

2. Thematic Analysis:

   • Extracted overarching themes from user feedback to uncover the root causes of frustrations, such as system reliability issues, cognitive overload from poorly structured layouts, and data discrepancies.

   • Identified opportunities for improvement that align with user needs and business priorities, such as enhancing screen navigation and addressing UI inconsistencies.

3. Sentiment Analysis:

   • Assessed the emotional tone of user feedback to gauge levels of frustration, satisfaction, and urgency.

   • Revealed heightened anxiety over system errors and a desire for smoother workflows, emphasizing the human impact of technical issues.

4. Affinity Mapping:

   • Clustered findings into logical categories to visually represent patterns and relationships between recurring themes.

   • Prioritized issues based on frequency and severity, enabling the team to tackle the most pressing problems first.

This comprehensive analytical process transformed qualitative feedback into actionable insights, allowing us to prioritize solutions that addressed both functional and emotional user needs. By combining advanced methods like thematic and sentiment analysis with task-focused approaches, I ensured that the design recommendations were deeply rooted in user experiences and aligned with strategic goals.