Sawyer Xavier

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.

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Research grounded in operations

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.

15+
Years in customer experience
5+
Research studies designed & led
10+
Research tools & platforms

Case Studies

A selection of research projects spanning UX, mixed-methods, survey design, and applied social research.

Unmoderated UX · 10 Participants

App Store Optimization

Storytree (SimplePrints)  ·  Fall 2024  ·  Tools: User Interviews, Lookback, Figma

Background

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.

Research Questions

  • How do potential users experience our app store page as an early touchpoint in the conversion journey?
  • How does our page compare to competitors in driving likelihood to install?
  • What specific elements drive or reduce likelihood to install?
  • What signals do users use to evaluate trustworthiness and quality?

Methodology

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.

Key Findings

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.

Primary Recommendations

1

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.

2

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.

Secondary Strategic Observations

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.

Moderated Interviews · 5 Participants

Wall Calendar Post-Purchase Interviews

Storytree  ·  Summer 2023  ·  Tools: Lookback, Mailchimp, Calendly

Background

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.

Research Questions

  • Why did users choose to purchase a wall calendar?
  • How did the creation experience compare to their expectations?
  • Which customization features did they engage with, or overlook?
  • What would improve the product or experience going forward?

Methodology

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.

Key Findings

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.

Open Question Raised

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.

Impact

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.

Mixed-Methods

Premium Customization Features Survey

Storytree  ·  Fall 2023  ·  Tools: Typeform, Account Data Analysis

Background

After investing in a set of background patterns that turned out to be poorly received, Storytree wanted to be more deliberate about future creative asset decisions. At the same time, an earlier premium feature, the ability to customize a photo book's back cover at a ~$0.50 price point, had seen unusually fast organic adoption with no marketing push. This raised a question worth researching: are customers willing to pay for premium features, and if so, which ones?

Research Questions

  • Which creative assets do customers actually want?
  • Are customers willing to pay for premium customization features?
  • What is the relationship between stated preference and actual purchasing behavior?

Methodology

Co-designed with a colleague who served as a UXR mentor during this phase of my research practice. Recruited from Storytree's active user base and deployed a Typeform survey asking about interest in and willingness to pay for various premium customization features: stickers, backgrounds, and other options. Participants were not anonymized, which meant I could match survey responses directly to accounts in our backend using email addresses.

When initial results came back showing an overwhelming "no" to paying for premium features, I wasn't satisfied to stop there. I had recently attended the Human Insight Summit (THiS) hosted by UserTesting, where a keynote speaker described a near-identical phenomenon: employees who said on a survey that they wanted healthy food in the cafeteria, but ignored a bowl of apples placed right in front of them. That talk was fresh in my mind. I went back to the data and cross-referenced survey responses against actual account activity and found something worth paying attention to.

Key Findings

The stated vs. revealed preference gap. The majority of respondents said they would never pay for a premium feature. Cross-referencing with account data showed that more than half of those same respondents had already purchased the custom back cover option. People's survey responses reflected their self-concept ("I'm not the type to pay for extras"), not their actual behavior. When a feature delivers clear value at a low, non-disruptive price point, purchase happens regardless of what customers say they'll do.

No clear winners among customization types. Across stickers, background patterns, color options, themes, and other features, responses were broadly distributed with no singular feature generating strong consensus interest. Customers generally wanted more options, but couldn't tell us which ones.

Customers were deeply anxious about complexity. This was an emergent finding from free-text responses, and arguably the most important one. SimplePrints' primary value proposition was its simplicity (the name says it all), and our primary demographic skewed toward older adults who were uncomfortable with technology. Across multiple open responses, customers expressed genuine anxiety about future changes to the app: not that they disliked the current features, but that they feared the app would eventually become too complex for them to use comfortably, forcing them to find an alternative. They didn't want to switch. They loved this app specifically because it didn't overwhelm them. This finding reframed how we thought about the product roadmap: more customization options wasn't the same thing as a better product for this user base.

Impact

Findings directly informed the team's approach to pricing future premium features and reinforced a "less is more" product philosophy, focusing on high-value, low-friction additions rather than an expansive feature catalog. The customer anxiety finding in particular provided documented evidence for a conservative approach to product changes with this demographic. The behavioral cross-referencing technique also shaped how the team interpreted survey data going forward.

Survey Design

CSAT Survey Design

Storytree  ·  Summer 2024  ·  Tools: Typeform, Cross-functional coordination

Background

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.

Objectives

  • Capture feedback from both purchasers and non-purchasers, specifically to surface conversion factors and understand what might be preventing active users from buying.
  • Establish a baseline CSAT measurement across support, product, and usability dimensions, aligned to internal KPIs.
  • Design the instrument for longitudinal use: to be repeated annually, semi-annually, or after major app changes, enabling comparison against the baseline to track sentiment over time.
  • Deliver the survey organically within the product experience, not as a disruptive separate touchpoint.

Design Approach

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.

Impact

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.

Academic Research · Mixed-Methods · Group of 5

Food Insecurity & Social Stigma

University of South Florida · SYA 4935: Senior Seminar  ·  Jan–May 2025  ·  Instructor: Dr. Laurel Graham

Background

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.

Research Questions

  • How do food-insecure people experience social stigma?
  • In what ways does stigma shape behavior, resource-seeking, and psychological health?
  • What do population-level quantitative measures reveal about who experiences food insecurity and how they cope?

Methodology

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.

Key Findings

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.

Output

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.

Tools & Methodologies

Platforms and approaches I've worked with across research, CX operations, and analysis.

Research & UX Tools

Lookback User Interviews Typeform Qualtrics Figma Miro Mailchimp Calendly

CX Operations Tools

Zendesk Jira Help Scout Trello SQL Google Workspace

Research Methods

  • Unmoderated UX studies
  • Moderated user interviews
  • Survey design (skip logic, bias mitigation, Likert scaling)
  • Thematic coding & qualitative analysis
  • Competitive analysis
  • Behavioral data cross-referencing
  • Mixed-methods research design

Analysis & Output

  • Stated vs. revealed preference analysis
  • Findings synthesis & reporting
  • Strategic recommendations
  • Stakeholder presentation
  • Quantitative data analysis & dataset cleaning
  • SOP & process documentation

Let's connect

I'm exploring opportunities in customer insights, applied research, and CX strategy. I'd love to hear from you.