Research Lunch Club is heading beyond the small events space: RLC is planning in-person lunch events in 🇬🇧 London, 🇺🇸 New York, 🇺🇸 San Francisco, and 🇸🇬 Singapore, and the team is looking for people to help coordinate them.

These aren't membership events — they're open to any researcher, capped at 8–10 people, and designed to be the kind of lunch you actually look forward to. If you're interested in helping host or coordinate one — whether in those cities or somewhere closer to home — drop a note to [email protected]. or follow our luma page to be the first hear of our events!

🧹 Cleaning CRM Data for UXR 101 | Trevor Calabro argues that UX researchers increasingly work directly with operational CRM data to recruit participants, build samples, and manage outreach, which makes basic data cleaning a core research skill rather than a pure ops function. He walks through the common failure modes — inconsistent formats, mixed data types in one field, non-printable characters, irregular capitalization — and how each one quietly undermines sampling logic and segmentation. The piece then covers foundational spreadsheet techniques: trimming whitespace, standardizing capitalization, normalizing dates, splitting multi-value fields, and enforcing one data type per column. He includes 14 reusable cleaning formulas researchers can copy into their own sheets. His conclusion: investing in repeatable cleaning patterns pays off in more reliable recruiting pipelines and fewer surprises when operational data doubles as a sampling frame.

🤖 AI-Assisted UX Research: Leveraging Machine-Learned Insights Without Overriding Human Judgment | DeShawn Harris lays out a five-step workflow for folding AI into UX research: ingestion and preparation, algorithmic pattern spotting, human contextualization, narrative synthesis, and strategic application. AI is positioned for scale-intensive work — clustering, pattern detection, first-pass summarization — while researchers keep responsibility for interpretation and framing. The central rule of thumb: if an AI output just confirms what you already believed, treat it skeptically as possible bias; if it surprises you, dig in to check whether it's a real pattern or a statistical artifact. Harris recommends a "tear down and rebuild" habit — actively hunting for gaps, missing context, or misread sentiment in AI-generated summaries before combining them with a close read of the raw data. The conclusion is that the strongest research comes from AI and humans working the data together, not either one alone.

🧊 Minimally Technical Reporting: The Information Iceberg | Carl Pearson makes the case for "minimally technical reporting" — keeping research write-ups short, action-focused, and stripped of unnecessary method detail. Drawing on Chapman and Rodden's framing, he argues the amount of technical detail a report needs should shrink as the audience moves up the org chart, since most stakeholders care about the answer and its implications far more than the method used to reach it. He also argues for matching format to context — a deck for a presentation, a document for reading — while acknowledging some stakeholders will insist on a specific format regardless. The underlying point is that researchers should scale detail to what the decision-maker actually needs, not default to the fullest possible methodological explanation.

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