Potloc

Product Designer at a market research tech company

potloc.combutton-icon
Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡Selected works ㋡
MaisonMotte_Potloc_Open-end_DQOverview

Automated data cleaning

2023 - 2025

Product Designer
Discovery
Design Sytem
UX/UI
Documentation

Context
For two years, I led Product Design for the Data Quality and Fraud Prevention team at Potloc, overseeing a vital aspect of the company’s business
Problem
Data integrity is a growing challenge; according to IBM, poor data cost U.S. organizations $3 trillion in 2016, a figure that has only increased over time.
Solution
We built a 90% automated data quality system using 14 checks across three survey phases and an internal dashboard that provides real-time quality metrics, flags suspicious responses, and enables seamless collaboration.
Read case study
MaisonMotte_Potloc_Openend_Thumbnails
MaisonMotte_Potloc_Open-end_UI6

Open-ends AI analysis

2024

Product Designer
Discovery
AI
UX/UI
Documentation

Context
Potloc is a market research company fighting poor quality and fraud in the industry. Open-ended responses are both the most insightful and the riskiest.
Problem
Lots of open-ended responses are bad quality answers : gibberish, irrelevant, repetitions, wrong language, emoji only responses, AI generated, offensive or profane, copy pasted...
Solution
Our technical and user research revealed we could automatically identify the quality of open-ended responses.
Read case study
Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡Team org. ㋡

Product x Engineering

The team is divided into three core groups: Sales, Supply, and Product & Engineering. All working together to deliver consistent value and maintain a high level of customer satisfaction.

In this page, I’ll focus on the Product & Engineering group, highlighting our workflow and how roles connect across the team.

Our collaboration was supported by a set of structured rituals, including:

  • All Hands (Company-wide, monthly)
  • Town Hall (Product & Engineering, bi-weekly)
  • Squad Weekly (Team-specific, weekly)
  • Triangle (Squad leads, weekly)
  • Daily Stand-ups (Squad, daily)
MaisonMotte_Potloc_Organisation

To streamline communication and knowledge sharing, I also reorganized the entire Product & Engineering space in Notion. I created an interconnected system of databases covering Discoveries, Documentation, News, and Culture. These were linked across all squad and team templates to ensure seamless collaboration and easy access to key information for everyone.

Data Quality Squad

I was part of the Data Quality squad, a cross-functional team made up of a product manager, engineers, and myself as the product designer.

The Product and Tech Leads set the squad’s vision and managed the roadmap, while each engineer took full ownership of their features, working closely with the PM and me throughout the design and development process.

MaisonMotte_Potloc_Organisation-1

Our work was driven by three key objectives:

  1. Ensure the quality of responses by identifying and removing fake or dishonest participants from the panel
  2. Guarantee that all respondents matched the campaign’s target audience—for example, confirming we were surveying real doctors, not just self-proclaimed ones
  3. Equip our internal teams with the right tools to manage quotas, reach, and sample quality efficiently

This close collaboration and shared ownership allowed us to tackle complex data challenges while delivering meaningful, trustworthy insights to our clients.

Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡Ownership ㋡

Sitemap

Within the Sample area, two squads were actively working in parallel—each with its own dedicated designer. I was the designer for one of these squads and owned three of the five core sections:

  • Quotas,
  • Data Quality,
  • Respondent Report.

In close collaboration with product and engineering, I led the design efforts for these areas, from concept to delivery. Over the past year, our squad alone shipped more than 50 features, significantly enhancing how internal teams manage sample quality and performance.

MaisonMotte_Potloc_Sitemap MaisonMotte_Potloc_MyPart

Methodology

Because I worked on so many features every quarter, I've focused on one of my favorite which was a game changer for Potloc.

Part of a squad of 7 people, collaborating with ML engineers, Data Scientists, DevOps; I had the opportunity to enhance the data quality section of our product.

Phases

Discovery
The goal of the research is to expand my knowledge through in-depth project analysis and user research inside the company.
Project definition
A high-level description of the project and its purpose. Identified and communicated the main fraud systems and the potential solutions.
Userflow
Based on identified usecases, I prepared the main userflows to help all the stakeholder understand the steps needed to fight against fraud.
UX/UI
Start fast, use existing components to implement quickly the MVP into the product, then iterate on top of it.
Prototype
Thanks to a strong collaboration with engineers, create a low-fi but functional prototype to test the feature live, with real Data.
User tests
We gave users the feature even if it was not perfect. This allowed us to do some shadowing, ask for feedback and optimization during the first weeks of implementation.
Documentation
Once everything seems to work well, write a precise documentation about the feature, a strong user guide and identify the components added to the Design System.
Design System
Send the new components for review to the design team and the Design System champions. Once validated, update Figma and Storybook.

Let's work together

We'll begin around a call to understand your expectations and the problem we've to solve.

Get in touchbutton-icon

Eloi Motte

UX & Product Designer

View