Everything you need to know about Terron's background in 60 seconds.
I am a Senior Quantitative UX Researcher with 8 years of experience leading research and architecture across Meta, Microsoft, JPMorgan Chase, and startups. Backed by a Master’s in Statistics from the University of Pennsylvania, I specialize in translating complex behavioral telemetry into executive product strategy.
My work spans building ecosystem-scale behavioral pipelines (800M+ MAU), massive global monitoring platforms (100K+ responses/month), establishing pyschometric launch gates for first of its kind tech (Agentic AI/cryptographic identity) and engineering full-funnel A/B testing frameworks that delivered a +$20M monthly revenue lift.
Whether you are navigating 0–1 product ambiguity, scaling tracking infrastructure, or eliminating funnel friction to drive growth, I partner with cross-functional teams to solve high-stakes behavioral and quantitative challenges.
As Founding Researcher in a blank-slate environment on a 3-person core team, I architected the research strategy and validity frameworks for a 0-1 civic advocacy platform utilizing blockchain digital IDs. My mandate was to translate user behavioral insights directly into technical protocols, UI specifications, and launch validation frameworks alongside engineering and product design.
User research uncovered critical psychological barriers: users were hesitant to connect cryptographic identities to real-world advocacy without clarity on anonymity guarantees. I diagnosed onboarding drop-off points and collaborated with engineering to design transparent, user-facing cryptographic explanations, demystifying zero-knowledge concepts into intuitive trust cues.
By systematically aligning protocol security with user mental models, we significantly improved onboarding funnel conversion and unlocked successful multi-market pilot deployments across California and New York.
The main objective of this project was to research the value of AI-powered agentic business messaging as it transitioned from concept to execution, focusing on identifying areas of value for businesses on Messenger and Instagram. Understanding how AI could perform as a substitute for what had traditionally been human customer support interactions, like giving information about products, was critical particularly for small businesses that had very few employees.
As the Quantitative UX Researcher, I led multinational alpha, beta and general availability studies combining survey data with platform interaction data. I managed the research process, including survey design, data analysis, and communicating findings to stakeholders. My approach involved close collaboration with qualitative researchers, providing them with user profiles and targeted outreach lists to ensure we spoke with the right participants.
For data analysis, I utilized internal survey tools, SQL for data retrieval, and Python for manipulation, allowing efficient extraction of relevant information. When we encountered broken eligibility criteria for survey participants, I was able to recover the data by finding other criteria by which to check whether the user had interacted with an AI or a human. I determined whether the data was random or systemic, to help determine whether the data was still representative as well as to understand what the likely failure point was.
Working with the Data Engineering team, we identified and resolved these issues to maintain data integrity on future projects. To address data eligibility challenges that delayed our research timeline, I implemented survey weighting techniques ensuring our results represented target populations accurately. This critical step validated our findings across different user segments and allowed us to confidently present recommendations to stakeholders.
Through analysis of platform interaction data and survey responses, I uncovered key insights:
These insights directly influenced the deployment strategy, ultimately enhancing customer engagement and satisfaction.
The project marked my first experience with a non-iterative research approach, teaching me valuable lessons about quickly bringing products to market to achieve relevance. I learned that prioritizing speed and responsiveness is essential for success in a rapidly evolving landscape. These insights will guide my future work as I apply these lessons to drive innovation and enhance user experiences in upcoming projects.
Within Meta's Small and Medium Business (SMB) advertising ecosystem, self-serve merchants face significant operational friction when launching and scaling campaigns. High interface complexity, confusing audience targeting options, and overwhelming budgeting controls led to frequent onboarding drop-offs, sub-optimal campaign setups, and premature ad spend abandonment.
To unlock advertiser growth and improve platform liquidity, Meta needed deep, actionable insights into where and why self-serve merchants were stalling during the campaign setup journey.
I designed and executed an end-to-end mixed-methods research initiative to map the complete advertiser lifecycle and identify key UX and functional friction points:
Our research revealed that SMB advertisers didn't lack marketing intent, they lacked confidence. Over-choice in targeting parameters created decision paralysis, while a lack of real-time feedback made merchants hesitant to commit advertising budgets.
I partnered directly with Product Managers, UX Designers, and Engineering squads to translate these findings into concrete product interventions:
By addressing core setup friction and simplifying targeting workflows, the team drove measurable gains across key platform metrics:
As the Quantitative UX Researcher for customer Business Messaging tracking, I led the implementation of a global research initiative designed to continuously monitor consumer sentiment across messaging platforms. The project tracked key metrics including value perception, relevance, and negative experiences associated with business messaging, serving as a critical guardrail against messaging overload. With hundreds of thousands of monthly responses spanning both Instagram and Messenger across priority markets, this project represented one of our largest-scale research efforts, providing visibility into global messaging trends and user sentiment.
My responsibilities included survey design, determining optimal invitation volumes, establishing data pipelines, and building comprehensive dashboards to visualize insights for stakeholders.
To effectively analyze the massive dataset, I utilized:
Working across diverse global markets presented unique challenges that required thoughtful solutions and cultural adaptations, including translation. By implementing weighted sampling techniques, I ensured our findings accurately represented user populations across all markets despite varying response rates.
These insights directly informed critical product decisions, including the implementation of message frequency capping in high-volume markets and the optimization of delivery timing based on regional usage patterns. In the future, our hope is to use this abundance of data to understand what levers are able to be pulled to improve business messaging with a much longer understanding of effects.
Microsoft's Hybrid workstream collected over 2,000 employee survey responses monthly to inform internal policies and product direction. Initially, analyzing open-ended text responses and compiling stakeholder updates was done completely by hand. This manual analysis was prohibitively slow and consumed significant time that could otherwise be spent on high-value research.
To resolve the speed barrier, I developed an automated data processing pipeline and fine-tuned a BART-large-MNLI zero-shot topic modeling solution to classify unstructured feedback automatically. Fine-tuning increased classification accuracy from 61% to 83%, drastically speeding up analysis and establishing consistent, objective weekly sentiment tracking.
While the NLP model successfully accelerated text processing, a fundamental issue persisted: open-ended responses were rarely utilized downstream by operational teams. The sheer ambiguity of open-ended text made it difficult for stakeholders to extract concrete, decisive actions, proving that automating an inefficient data source was only a partial fix.
Recognizing that the data collection method itself needed an overhaul, I redesigned the survey methodology. We transitioned from monolithic surveys with heavy open-ended text to a modular framework that served dynamic, targeted question subsets, reducing length from dozens of items to fewer than 12.
We shifted primary data collection to structured, closed-ended questions for quantifiable insights, while retaining an "Other" field to capture emergent qualitative nuances.
Recognizing that qualitative researchers frequently waited on central engineering queues for basic data queries, I initiated and led a technical training curriculum teaching team members Python scripting, SQL data extraction, and statistical fundamentals. This endeavor aimed not only to empower team members with new technical skills but also to leverage these skills to boost productivity and the overall performance of our team.
Through hands-on workshops and 1-on-1 coaching, qualitative researchers learned to query and visualize their own respondent datasets independently, accelerating research turnaround times and fostering deeper cross-pollination between qualitative empathy and quantitative rigor.
During my tenure at JPMorgan Chase, I spearheaded and supported a series of technical and analytical initiatives aimed at constructing robust data infrastructure, enhancing digital user experiences, and driving digital account acquisition across public and private portals.
To facilitate a unified tracking system, I led the technical initiative to construct a high-performance data lake utilizing behavioral clickstream data. Supporting Chase.com, one of the top 50 most visited U.S. websites with 800 million monthly visits and 6 billion page views, required optimizing an expansive database with prohibitively long query times and overly complex table joins.
I engineered an automated Teradata transformation script that streamlined the architecture, reducing the tables required to view an application journey from four to just one. This automation guaranteed data integrity and scalability while allowing multi-dimensional filtering across demographics, product types, and traffic sources. Analysts experienced a >90% reduction in query times, retrieving insights almost instantly rather than waiting hours.
With scalable infrastructure in place, we conducted full-funnel A/B testing across owned advertisements and acquisition pathways. Unlike traditional siloed analyses that evaluated media clicks or application starts independently, our end-to-end approach tracked users from initial link exposure through final account opening. This prevented low-intent clicks and ensured tailored product placements aligned with user eligibility.
These infrastructure and UX enhancements delivered a 7% year-over-year increase in total account volume, driven primarily by deposit account growth, generating approximately $20 million in incremental monthly revenue.
In my role at JP Morgan Chase, I played a pivotal part in identifying and addressing a critical issue related to the effectiveness of the "Announcements" feature which is intended to provide information on changes after a customer signs in. It began when I performed fundamental research to assess the feature's performance. What caught my attention was that the Announcements were consistently underperforming, falling far below the expected metrics. It became evident that a significantly lower number of users were actually seeing these Announcements, even though the feature was designed to reach everyone eventually. In some instances, the visibility rate for an Announcement was as low as 30%.
Upon digging deeper, I uncovered several key insights that shed light on the problem. First, I realized that all "Sign in interstitials," including "Announcements," were competing for priority in terms of display. Second, I discovered that the platform responsible for showcasing these interstitials wasn't under the jurisdiction of any internal team, resulting in a lack of controls and design guidelines. Third, a closer examination of other "Sign in interstitials" revealed a stark lack of design cohesion across the board. Lastly, I found that some of these interstitials were broken, repeatedly disrupting users instead of being dismissed after the initial display. User feedback in our Voice of Customer (VOC) channels confirmed that this issue was causing significant frustration.
To tackle this issue head-on, my team within the organization took charge of the "Sign in interstitials" platform. We implemented comprehensive design guidelines, ensuring consistency and effectiveness across all interstitials. Additionally, we fixed the broken ones, ensuring that they would function correctly and no longer disrupt users excessively. This initiative not only improved the user experience but also enhanced our ability to effectively communicate important announcements to our customers.
During early 2022, our core analytics team experienced sudden attrition. I took on a role in re-expanding our team's capacity. Over the course of just three months, I successfully recruited and onboarded five new team members, effectively growing our team from three to eight members. This endeavor was not just about increasing headcount; it was about strategically identifying and bringing on board individuals who would not only fit seamlessly into our team culture but also excel in their roles.
To achieve this, I developed an objective criteria to evaluate potential candidates. This criteria encompassed both a behavioral portion of the interview process and a coding assessment. It was essential for us to assess not only technical skills but also interpersonal qualities that would contribute to a positive and productive team dynamic. Notably, each of these new hires not only successfully integrated into the organization but also demonstrated outstanding performance in their respective roles, consistently delivering valuable contributions to our team's success. All five of the team members that were hired were still in the organization adding value one year later, proving that the decisions made were the correct ones.
Practical problem-solving blueprints: How I diagnose friction, navigate constraints, and drive executive decisions.
Integrating non-deterministic AI, ranging from generative content models to autonomous agentic workflows, introduces unpredictable, non-repeatable system behavior into product experiences. Unlike deterministic software, user friction doesn't stem from traditional usability bugs, but from the ambiguity of delegation versus control: users struggle to understand system boundaries, monitor autonomous task execution, evaluate non-standard outputs, and know when or how to intervene when a model or agent strays.
An executive diagnostic framework that identifies whether user friction or drop-off is driven by core model capability, insufficient system state observability, or an improper balance between automated delegation and human control, delivering clear operational directives for engineering architecture versus interaction design.
When faced with high ambiguity, the need to ship fast causes significant pressure to make any decision instead of spending time to make a good decision. Because waiting for data feels like an unnecessary tax on release momentum, leadership can decide to go without. Research in any practical setting isn't an academic exercise, it exists to drive decisions, reduce uncertainty, and influence outcomes, which means working within the time and resources available. Without a mechanism to deliver fast, high-conviction signal within those constraints, teams risk mistaking velocity for progress and shipping the wrong solution faster.
A phased decision framework that pairs immediate, high-conviction directional guidance with a clear roadmap for scaling data fidelity over time. It provides leadership with synthesized desk research and early MVP findings to make near-term product choices safely, while establishing explicit metrics and timeline triggers for expanding lightweight probes into long-term tracking infrastructure.
Relying on self-reported user sentiment or behavioral telemetry in isolation creates major strategic risk because individuals are frequently unreliable narrators of their own habits. When survey responses directly contradict live interaction metrics, product teams struggle to diagnose whether low feature adoption stems from severe usability friction, an automated background habit, or purely aspirational intent. Without a structured framework to reconcile this disconnect within project constraints, leadership risks mistaking release velocity for true progress and wasting engineering capital on features users claim to want but never actually use.
A synthesized conflict resolution matrix that translates divergent attitudinal and behavioral data into explicit execution directives: Kill, Fix UX, or Automate Defaults. It provides product and engineering leadership with clear, risk-adjusted directives on whether to overhaul workflow UI, deprecate aspirational feature requests, or reallocate development cycles toward high-utility daily habits.
Expanding software globally creates a costly dilemma between central efficiency and local market survival. Pushing an identical product worldwide preserves lean engineering costs, but causes low adoption and high churn in regions where local expectations around payments, trust, and workflows differ. Conversely, customizing the product for every country delivers local market fit, but fractures the codebase, inflates development overhead, and erodes margins. Executive leadership lacks a systematic method to determine where the platform must remain globally uniform and where it must adapt to capture regional growth.
An Executive Localization & Architectural Blueprint that maps universal product functionality against approved regional extensions, equipping Product and Engineering leads with clear operational criteria to decide when to enforce global standardization and when to build modular regional features.
Senior researcher with 8 years designing and executing research strategies at global organizations including Meta, Microsoft, and JPMorgan Chase. Expert in translating complex behavioral data into actionable executive insights, designing scalable infrastructure, and measuring impact through data-driven frameworks across Fortune 50 enterprises and 0-1 startups.