CX Research & Strategy
I design and run research studies that help teams understand their customers, translating real human behavior into product decisions, operational improvements, and strategies that actually work.
I'm a customer experience researcher and strategist with over 15 years of direct customer work, starting in frontline support and building toward research from the inside out. The greatest period of growth occurred during my 11 years at Storytree, a photo product startup, culminating in my role overseeing CX operations. I identified that the team was making product decisions without adequate customer data to back them up. Despite research not being a part of my role, I pitched a research initiative to leadership, secured a budget, and built the company's research practice from the ground up.
While I was establishing research practices at Storytree, I was also completing my BA in Sociology at the University of South Florida with a deliberate focus on research methodology, including a highly selective undergraduate research seminar alongside only 8 students selected from across all majors and class years. Learning formal methodologies in the classroom while applying it professionally accelerated my knowledge and skill.
My background in frontline support heavily influences my approach to CX Research and Strategy. Before I was designing studies, I had closed thousands of tickets, resolved tricky technical issues, heard product feedback directly from customers every day. My curiosity and empathy, backed by years of experience working directly with customers, make me better at asking the right questions and producing actionable analysis.
I believe every planning and design meeting needs a person in the room who can confidently say "This is what our customers actually think, and here's the data to prove it." Someone insatiably curious, a tenacious problem solver who asks the right questions and can uncover them. Being that person is the heart of my career.
A selection of research projects spanning UX, mixed-methods, survey design, and applied social research.
Evaluated how app store content affects user trust, appeal, and likelihood to install through a 10-participant competitive study using branding-stripped mockups.
View case study → Moderated InterviewsExplored user experience and feature engagement following the launch of a new product category through structured post-purchase interviews with real customers.
View case study → Mixed-MethodsUncovered a significant gap between stated preferences and actual purchasing behavior, with direct implications for pricing strategy and feature investment.
View case study → Survey DesignDesigned Storytree's first baseline customer satisfaction instrument, built for long-term sentiment tracking and multi-team KPI alignment.
View case study → Academic · Mixed-MethodsSenior capstone study combining qualitative thematic coding of Reddit narratives with quantitative U.S. Census analysis to explore how stigma shapes lived experience.
View case study →New user conversion rates for SimplePrints (Storytree's photo book mobile app) were consistently low. To understand why, we started at an early touchpoint in the customer journey: the app store page. The goal was to understand how potential users experienced and evaluated our page, and how it compared to two leading competitors, as one piece of a broader picture of where and why users weren't converting.
I created unbranded mockups of three app store pages (SimplePrints and two leading competitors), stripping all logos and identifying information to ensure unbiased evaluation. I recruited 10 participants through User Interviews using a pre-screening questionnaire to filter out anyone who had previously used any of the three apps. Sessions were conducted as unmoderated studies in Lookback: each participant reviewed the mockups and responded to Likert-scale ratings across five dimensions (likelihood to install, attractiveness, trustworthiness, ease of use, expected quality) paired with open-ended questions. I coded and analyzed all session recordings and wrote the formal findings and recommendations report.
Our page was the clear loser aesthetically. Participants overwhelmingly found SimplePrints' description too wordy and disorganized. One participant said verbatim: "Do not use this app." Most participants said they wouldn't read paragraphs of copy before deciding to install. They skim, then decide.
Visual design signals product quality. Multiple participants directly equated app store page organization and image quality with their expected quality of the physical product; the better the page looked, the higher they rated what they'd receive.
Past experiences shape current evaluations more than the page itself. Prior bad experiences with similar apps made participants more skeptical across the board; good past experiences increased trust even for unfamiliar apps. The competitive landscape, including how users have been treated elsewhere, matters beyond anything on your own page.
Subscription models were strongly off-putting. Almost every participant said one competitor's subscription model reduced their likelihood to install, even for participants who were otherwise interested in that app. This signal was consistent across age groups and income levels.
A competitor's AI features challenged an internal assumption. Internal assumptions held that AI-assisted features posed a reputational risk with our user base. The data challenged this: the majority of participants reacted positively to a competitor's AI photo-organization tool, and not one reacted negatively. A useful signal for future product planning, and a reminder that assumptions about what users will resist are worth testing.
An unexpected discovery: users start their search on Google and AI tools, not the App Store. Participants overwhelmingly reported beginning their search through Google or AI tools before arriving at the App Store. This wasn't directly related to the conversion question we set out to answer, but it was a notable secondary finding worth flagging to broader stakeholders.
Rewrite the description block. Drastically reduce word count and reorganize for scannability: bullet points, headers, and clear structure over dense prose. This was the single biggest barrier to conversion.
Rebrand the app store imagery. Move toward more colorful, lifestyle-focused photography that shows the finished physical product: books being unboxed, displayed in homes, in the hands of families. Participants equated image quality and warmth directly with expected product quality.
These findings fell outside the primary research question but surfaced competitive and product intelligence worth noting for longer-term planning.
Subscription model aversion. Near-universal negative reaction to a subscription-based competitor, a signal worth tracking as the industry evolves and as Storytree evaluated its own monetization options.
AI-assisted features as a differentiator. Participants were enthusiastic about a competitor's AI photo organization tool, often rating it highest on this basis alone. Worthy of longer-term investment consideration as a competitive positioning opportunity.
Storytree had recently launched Wall Calendars as a new product category. Leadership and the Product team wanted early post-launch feedback from real purchasers, covering what drew people to the product, how the creation experience felt, and whether the customization features we'd invested in were landing with users.
Recruited 5 wall calendar purchasers through a targeted Mailchimp campaign with a $15 Amazon gift card incentive for 30 minutes of their time. Because wall calendars are a seasonal product, we anticipated going in that some participants would have purchased several months prior, meaning recall of the creation experience might be limited. We built the interview guide with this constraint in mind, focusing on impression and emotion over precise feature recall, and treated any gaps in participant memory as data in themselves rather than noise to be discarded. My colleague and I co-designed the guide and divided interview and note-taking responsibilities across sessions. Interviews were conducted as moderated video calls via Lookback, covering shopping motivation, purchase experience, app feature usage, typical calendar habits, competitive landscape, and overall satisfaction. Each session used a consistent note-taking framework to capture buyer type, language patterns, purchase motivations, and unique participant details.
The core creation experience was frictionless. All 5 participants found the calendar easy and fast to make and were happy with the final product quality. No participant had meaningful suggestions for improvement to the basic creation flow. One participant had switched from a competitor after a disruptive change on that platform. She found Storytree's app easier to use and the product quality noticeably better, and stated she planned to switch permanently. This anecdote reinforced the broader frictionless experience finding rather than standing on its own.
Customization options were invisible to users. Despite a significant investment in customization features (backgrounds, custom date layouts, collages, and more), not one participant recalled seeing them, let alone using them. This was a meaningful finding, but an important detail shaped how we interpreted it: the customization options lived in a foldout menu at the bottom of the screen that was collapsed and hidden by default. That context reframed the question: was this a discoverability failure built into the UI, or had we built features that users simply didn't want? The data couldn't answer that on its own.
Support recovery drove deeper loyalty than the product itself. Two participants had experienced catastrophic order failures: one received a calendar with a serious printing defect from our fulfillment partner, another had accidentally set the wrong year and received a calendar that was completely unusable. Both participants were among the most enthusiastic respondents in the study. In each case, the explanation was the same: when they contacted support, the problem was fixed immediately and without friction. No pushback, no hoops, just a replacement calendar. One participant described the support experience as more meaningful than receiving the product itself. This was an emergent finding we weren't looking for. Notably, prior NPS surveys and customer interactions had already suggested that support quality was a major driver of loyalty and retention, but those were brief signals. These interviews gave us rich, detailed documentation of exactly how and why that dynamic played out.
The customization finding left a critical question unanswered: Were users not finding the features, or were they finding them and not caring? One is a UI/IA problem; the other is a product direction problem. They require very different responses. This study also surfaced a methodological lesson: waiting months after a seasonal purchase to ask customers about their experience significantly degrades the quality of recall data. Future research on seasonal products needed to reach customers much closer to the moment of use.
I proposed a follow-up survey to launch after the next major sale, designed to reach customers while the experience was still fresh and to specifically probe feature awareness, discoverability, and perceived value. The survey was drafted, but I had left the company before it was launched.
The customization discoverability gap directly informed how new features were surfaced in subsequent product iterations. The support recovery finding was shared with leadership as documented evidence (richer than NPS scores alone) for maintaining the team's customer-first resolution philosophy as the company scaled.
Storytree's existing feedback mechanisms (NPS surveys, post-launch studies) only reached purchasers. That left a significant blind spot: the opinions of active app users who hadn't converted. Without a way to hear from non-purchasers, it was difficult to understand what was driving conversion friction or how app experience factors were shaping the decision to buy. At the same time, there was no baseline instrument for measuring satisfaction across the dimensions that mattered most to the business: support quality, product satisfaction, and usability.
Built around three core principles: clarity: plain language questions accessible to a diverse and predominantly older user base; skip logic: routing respondents to relevant questions based on their experience (purchaser vs. non-purchaser paths, for example), reducing fatigue and irrelevant items; and bias mitigation: question wording and response scale construction informed by extensive reading on effective survey design, with internal test runs from teammates to catch leading language and response order effects before launch. Likert-scale scoring was aligned to existing internal KPI definitions to integrate cleanly with team reporting. I coordinated with Marketing and Engineering to integrate the survey into the app's carousel banner, ensuring organic delivery to active users. A modest account credit incentive was proposed to encourage participation from non-purchasers in particular.
The survey launched successfully. Beyond establishing a feedback baseline, the design's longitudinal intent meant it was built to answer questions the business hadn't been able to ask before, particularly around what active users who hadn't converted were thinking. The cross-functional coordination required to get it integrated into the product experience helped establish clearer communication channels between CX, Marketing, and Engineering around customer feedback infrastructure.
Note: I departed Storytree shortly after launch and was unable to observe results firsthand. This case study focuses on the research design, decision-making, and implementation process.
This was the capstone project for SYA 4935: Senior Seminar, completed as part of a BA in Sociology at the University of South Florida. Working in a group of five, we conducted a complete mixed-methods study on the lived experience of food insecurity, with particular focus on how social stigma shapes behavior, resource-seeking, and psychological well-being. Food insecurity is extensively measured quantitatively, but the qualitative dimensions of how people experience and internalize stigma are underrepresented in the literature.
Mixed-methods design combining two data sources, with distinct contributions across the team.
Qualitative (group): We selected a high-engagement thread from r/poor, a Reddit community focused on the lived experience of poverty, and coded 71 replies using a shared coding framework developed collaboratively. Our six coding categories were: guilt/shame, adaptive habits from food insecurity (hoarding, gorging, restricting), kids/family eat first, history of poverty/homelessness, physical health impacts, and poor relationship with food. Each group member was assigned specific comments to code; usernames were redacted to protect anonymity. I completed my coding assignments using color-coded notation in Google Docs to systematically flag language patterns within each response.
Quantitative (my individual contribution): The quantitative component fell to me to lead after the group stalled on this deliverable (it was a pass/fail requirement for the course). I identified and sourced the U.S. Census Bureau's December 2024 Household Pulse Survey as an appropriate dataset, cleaned and reformatted it to make it analysis-ready (several columns required restructuring before the data was usable), ran the analysis, and produced the data visualizations. The analysis examined food security status alongside demographic factors, food assistance program utilization, and mental health indicators including anxiety, depression, and life satisfaction.
Internalized stigma was the primary barrier, not external stigma. We entered the study expecting to find external sources of stigma (institutions, social judgment) as the main reason food-insecure people avoid seeking help. The data told a different story: the dominant barrier was internalized. People declined to seek assistance because of their own guilt, shame, or the belief that others were more deserving, not primarily because of how institutions treated them. Institutional stigma did appear in the literature, particularly in college campus food programs, but personal, self-directed shame was far more prevalent in our Reddit data.
Adaptive behaviors from food insecurity persist long after the scarcity ends. The most frequently coded theme (25 instances) was adaptive habits: food hoarding, gorging when food is available, eating once a day or less, skipping meals. Critically, many commenters reporting these behaviors described themselves as currently food secure; the habits developed during scarcity had become the default. One commenter described a fully stocked pantry and still eating once a day, "irrationally scared" of running out again. These are trauma responses, not present-day necessities.
Poor relationship with food was the second most common theme. Nineteen instances captured disordered eating patterns: anxiety around food, rationalizing minimal eating as healthy ("we don't have to eat 3 meals a day, that's a myth"), and difficulty distinguishing real hunger from conditioned restriction. Several commenters described losing the ability to feel hunger at all after years of going without.
Census data confirmed population-level patterns and revealed a striking assistance gap. The December 2024 Household Pulse Survey showed higher rates of anxiety, depression, and life dissatisfaction among food-insecure respondents, consistent with the literature. A notable finding: over half of respondents reporting some food insecurity in the prior 7 days did not use any food assistance programs, a pattern consistent with the internalized stigma and self-disqualification we observed in the Reddit data. The Census data also showed higher rates of food insecurity among female respondents, those aged 35–44, and those identifying as Black or Hispanic/Latino.
Synthesized collaboratively into a 20-page written research report and an oral presentation to Dr. Graham's faculty review panel. While the qualitative coding and paper writing were shared across the group, the quantitative data component (sourcing, cleaning, analyzing, and visualizing the Census survey data) was my individual contribution. The project as a whole demonstrated the ability to apply mixed-methods research skills in an academic context: literature review, research question development, thematic coding, and quantitative data analysis.
Platforms and approaches I've worked with across research, CX operations, and analysis.
I'm exploring opportunities in customer insights, applied research, and CX strategy. I'd love to hear from you.