Dylan Fino headshot

Dylan Fino

Senior UX Researcher Quantitative Qualitative Strategy-defining research Roadmap decisioning AI-driven Data triangulation

I operate as the translation layer between customer reality and business strategy. I am deeply customer-obsessed, focused on how customers think and feel, what motivates them and what frustrates them, and how those realities should shape product and business decisions. I draw from the most relevant data sources and speak directly with customers and cross-functional stakeholders to maximize clarity and shared understanding.

I specialize in distilling complex signals into clear, decision-grade guidance, applying research that is as fast and as rigorous as the moment demands, and turning customer insight into aligned strategy, roadmap direction, and measurable impact. I identify the single highest-impact problem and concentrate my time there, recognizing that the teams doing the most important work often don’t have time to fill out a research intake form.

Amazon
Salesforce
Samsung
Xfinity
Prime
Qualtrics
Figma
Shopify
Comcast
Samsung (brand)
Xfinity
UserTesting
Python
ChatGPT

Selected Work

Do Off-Amazon Sellers Want Faster Shipping Speeds?

Multi-Year Roadmap • Multi-method program • Strategy decision

Mixed Methods Data Triangulation Roadmap strategy Qualitative interviews

Executive summary: Amazon leadership faced a high-stakes strategic decision: whether to invest heavily in same-day and sub-same-day shipping for off-Amazon fulfillment (MCF). Despite pressure to mirror Amazon.com speed leadership, early signals suggested limited merchant demand. I led a multi-method research program that resolved a long-standing internal debate, redirected a $1B+ roadmap, and reframed how leadership evaluated “speed” as a value proposition for SMB DTC merchants.

Outcome
Prevented misaligned 2026 speed investment; redirected toward configurable fulfillment (2026–2028+)
Core finding
Faster-than-3-day was low-demand; cost + flexibility dominated adoption decisions
Method
7,000+ feedback signals, pilot + rollout analysis, 11× 90-min interviews
Read the case study

Executive Summary

Amazon leadership faced a high-stakes strategic decision: whether to invest heavily in same-day and sub-same-day shipping capabilities for off-Amazon fulfillment (Multi-Channel Fulfillment, MCF). Despite internal pressure to mirror Amazon.com’s speed leadership, early signals suggested limited merchant demand. This program resolved a director/principal-level disagreement, redirected a $1B+ roadmap, and reframed how leadership evaluated “speed” as a value proposition for SMB DTC merchants.

Outcome: Prevented misaligned investment in faster shipping speeds for 2026 planning and redirected the roadmap toward configurable, merchant-centric fulfillment capabilities now embedded in the 2026–2028+ roadmap.

The Business Question

In late 2025, the L8 Product Director for MCF requested research to inform 2026–2028 planning: Should Amazon invest in developing same-day or sub-same-day shipping for off-Amazon sellers?

Context

  • MCF launched 3-Business-Day Standard (3BDS) in September 2024.
  • A prior pilot (Oct 2023) showed promise, generating 2.7M incremental unit commitments.
  • Broad rollout produced mixed adoption and weak sustained demand.
  • 7,000+ merchant feedback signals showed no organic demand for faster-than-3-day speeds.
  • Product leadership argued speed parity with Amazon.com was strategically necessary; IC product leaders believed adoption would be minimal.

My Role

Senior UXR - end-to-end research owner

  • Resolved a director- and principal-level disagreement blocking roadmap decisions.
  • Translated ambiguous customer sentiment into an unambiguous strategic recommendation.
  • Delivered leadership-ready guidance under tight planning timelines.
  • Owned problem framing, success criteria, research design, synthesis, storytelling, and executive readouts.
  • Drove cross-org alignment (Product, Ops, AFN, SCOT, NATS).

Research Approach

I combined existing internal signals with focused qualitative deep dive to understand not just what merchants were doing, but why. The goal was decision-grade clarity over cloudy discovery theme generation.

Existing data analyzed

  • 7,000+ merchant feedback echoes (AI-assisted thematic analysis)
  • 3BDS pilot results (Oct 2023)
  • 3BDS broad rollout performance (Sept 2024)

Primary research

  • 11 in-depth interviews (90 minutes each)
  • SMB DTC merchants ($1M–$50M ARR)
  • ~1,000+ MCF orders/month
  • Roles: Heads of Ops, Logistics, Supply Chain
  • 5–15 years fulfillment experience across B2C, B2B, and marketplaces

Topics explored

  • Decision-making process for existing shipping speeds
  • Perceived customer expectations by shipping speed
  • Willingness to pay for 1-day / same-day delivery
  • Inventory placement and operational complexity
  • Tradeoffs vs. smaller, more flexible 3PLs

Key Insights

Five findings consistently emerged across data sources and directly challenged internal assumptions:

  1. Demand for faster-than-3-day shipping was limited. Merchants reported minimal customer pressure for same-day or sub-same-day delivery. Existing 2–3 day options already met expectations. Speed beyond 3 days was “nice to have,” not table stakes.
  2. Cost was the primary constraint. Merchants could not justify incremental fulfillment costs, especially under macroeconomic pressure, even when faster shipping sounded appealing in theory.
  3. ROI was unproven. Merchants lacked credible evidence that faster shipping increased conversion, reduced cart abandonment, or improved retention. Without ROI clarity, speed investments stalled.
  4. Flexibility mattered more than speed. Merchants consistently prioritized custom packaging/branding, bundling and kitting, order edits, and routing flexibility. MCF was perceived as fast but rigid relative to smaller 3PLs.
  5. Competitive pressure for faster speeds was decreasing. China-based marketplaces (e.g., Temu/Shein) were normalizing slower delivery windows, reducing urgency to compete purely on speed.

Impact on Strategy

This research changed the roadmap. Rather than accelerating faster standard shipping, leadership pivoted toward configurable fulfillment as the core differentiator.

Strategic shift

From: “Faster is always better.”

To: “Merchants want control across speed, cost, and operations.”

Resulting roadmap framework

  • Crawl (2025–2026): MCF-enabled inventory placement supporting multiple speed tiers
  • Walk (2026–2027): Configurable fulfillment models balancing speed, cost, and complexity
  • Run (2027+): Gradual acceleration of shopper expectations once value is proven

Organizational impact

  • Informed July 2025 MCF Speed Strategy
  • Embedded into 2026 OP1 planning
  • Influenced roadmap decisions through 2028+
  • Created alignment across MCF, AFN, SCOT, and NATS
  • Most importantly: prevented costly misinvestment in capabilities merchants were not ready to adopt

Why I Chose This Project

  • I resolve ambiguous, high-stakes debates with evidence.
  • I translate customer sentiment into roadmap-level decisions.
  • I’m comfortable recommending against prevailing internal narratives.
  • My outcome is the business impact, not the research deliverable.

Always-On VOC: AI-Powered Personas from Real Interviews

AI-assisted qualitative synthesis • Research infrastructure • Org-wide leverage

Qualitative interviews AI tooling Research systems Democratized insights

Executive summary: I designed and built an AI-powered persona generation system that turns raw interview transcripts into “always-on Voice of the Customer.” Grounded entirely in real deep-dive conversations, the system dramatically reduced synthesis time, multiplied the impact of qualitative research, and enabled researchers, designers, and product partners to directly interrogate customer insight, without setup, access friction, or ongoing researcher mediation.

Outcome
Reduced synthesis time by days; persistent VOC embedded across teams and artifacts
Adoption
Actively used by researchers, product, and design; referenced live in meetings
Differentiator
Always grounded in real interview transcripts rather than synthetic GenAI proxy personas
Read the case study

Executive Summary

Traditional qualitative research breaks down after the study ends: insights live in decks, knowledge decays, and teams rely on secondhand interpretations. I built an AI-powered system that transforms interview transcripts into durable, interactive personas, creating an always-on VOC that lives far beyond any single research project.

These personas are explicitly informed by real customer conversations, can be backfilled for older studies, require no setup or special access, and are available to anyone internally. The result is a step-change in how teams access, trust, and apply qualitative insight.

The Problem

Even high-quality qualitative research has structural limits:

  • Synthesis is time-intensive and often bottlenecks teams.
  • Insights are frozen in artifacts and decay over time.
  • Stakeholders can’t easily “go back to the customer.”
  • Older interview data becomes effectively inaccessible.

I wanted to answer a different question: What if customer voice were persistent, interrogable, and available on demand?

My Role

Senior UXR - system designer and builder

  • Designed the end-to-end concept and research workflow.
  • Built AI-powered personas directly from interview transcripts.
  • Defined guardrails to ensure fidelity to real customer voice.
  • Enabled org-wide access with zero setup or specialized knowledge.
  • Drove internal adoption through documentation, enablement, and community shareouts.

How It Works

From raw transcripts → living VOC.

  • Qualitative interview transcripts are consolidated and cleaned.
  • An AI persona is generated to represent the collective voice of interviewed customers.
  • The persona can be queried in natural language about needs, tradeoffs, motivations, and constraints.
  • Responses are grounded explicitly in real conversations and quotes.

Unlike traditional personas or AI chatbots, these outputs:

  • Are explicitly sourced from real interviews
  • Persist indefinitely and improve with additional research
  • Can be reused across projects, teams, and time

Impact

  • Massive time savings: Reduced qualitative synthesis time by days per project.
  • Research multiplier: Extended the value of deep-dive interviews far beyond initial readouts.
  • Democratized access: Product, design, and research partners directly interrogate VOC without mediation.
  • Embedded in practice: Actively referenced in meetings, linked in docs, and embedded in research artifacts.
  • Durable knowledge: Personas “live forever” and can be backdated for older studies.

Adoption was strong enough that my internal presentation on the system had record turnout for a research community shareout, driven entirely by organic interest from peers and stakeholders.

Why I Chose This Project

  • I design systems, not just insights or studies.
  • I optimize qualitative research for scale, longevity, and real-world use.
  • I leverage AI to amplify and multiply deep human conversations.
  • I focus on durable impact that compounds over time.

Why Were Buy with Prime Onboarding Rates So Low?

Rapid usability research • Launch-critical risk • SKU matching

Usability testing Rapid research Organizational Blindspot

Executive summary: Ahead of a high-visibility Shopify app launch, onboarding completion remained low despite strong interest. I initiated and led rapid, streamed usability research that pinpointed SKU-matching failures as the primary blocker, unlocking $1M+ investment and a redesigned “fuzzy” matching system that materially improved onboarding efficiency and growth.

Outcome
Unlocked $1M+ investment; launched fuzzy SKU matching within 1 month
Impact
35% reduction in onboarding time; SKU parity improved 45% → 56.2% by EOY
Root cause
Exact-match SKU requirements + confusing expectations + no recovery path
Read the case study

Executive Summary

Ahead of a high-visibility Shopify app launch, Buy with Prime faced persistently low merchant onboarding completion, despite strong interest in the value proposition. With no prior merchant feedback on the onboarding flow and launch timelines locked, I initiated and led a rapid, decision-oriented research effort that revealed a critical SKU-matching failure blocking adoption. The findings prompted immediate leadership action, unlocked $1M+ in investment, and resulted in a redesigned “fuzzy” SKU-matching system that materially improved onboarding efficiency and long-term merchant growth.

Outcome: Reduced onboarding time by 35%, increased SKU parity from 45% to 56.2% by EOY, and delivered cross-product benefits to both Buy with Prime and MCF.

The Business Problem

Buy with Prime enables merchants to offer Prime benefits on their own DTC sites, but only if they successfully onboard and map SKUs between Shopify and Amazon systems.

Leading into launch

  • Onboarding was known internally to be “difficult,” but not well understood.
  • No merchant research had been conducted on the actual flows.
  • Shopify app launch was ~3 months away.
  • Failure risk was high, but invisible.

The question was no longer “Is onboarding hard?” but:

Where exactly is it breaking, and what do we do about it now?

My Role

Senior UXR - self-initiated, end-to-end owner

  • Proactively identified onboarding as a launch-critical risk.
  • Designed and executed rapid qualitative research under tight timelines.
  • Recruited merchants via Sales partnerships.
  • Ran live, streamed usability sessions with Product, Design, and GTM stakeholders.
  • Facilitated leadership listening sessions to drive accountability and action.

Research Approach

Fast, high-signal qualitative testing optimized for action.

  • Sales-recruited Buy with Prime merchants
  • Live usability testing of real onboarding flows
  • Sessions streamed directly to stakeholders
  • Immediate synthesis focused on critical failure points.

This approach surfaced issues traditional metrics had masked.

Key Insight

The primary blocker was SKU matching.

Merchants consistently failed during SKU setup due to:

  • Rigid, exact-match requirements
  • Confusing system expectations between Shopify SKUs and Amazon SKUs
  • No recovery path when matches failed

Leadership moment: After watching clips from the sessions, our org VP said:

“Can’t we just throw $1 million at this to fix it?”

Impact

The research directly led to:

  • Executive approval for a $1M+ investment
  • Design and launch of “fuzzy” SKU matching within three months
  • Measurable improvements documented in the Buy with Prime Quarterly Performance Report:
    • +2,500 bps increase in FBA unit-weighted SKU parity by EOY
    • 35% reduction in merchant onboarding time
  • Benefits extended to both Buy with Prime and unified MCF onboarding

Why This Project

  • I surface invisible risks before they become launch failures.
  • I design research for speed, clarity, and executive decision-making.
  • I turn qualitative evidence into concrete investment and roadmap change.
  • I optimize for outcomes rather than just research artifacts.

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