AMA: AssemblyAI Head Of Product Marketing, JD Prater on Market Research

When a launch date is barreling toward us, I don’t skip research—I compress it. First, I map every open question (messaging resonance, pricing, must-have features, likely objections) and score each one on a simple Risk × Impact × Effort matrix. Anything that scores high on both risk and impact but can be answered with modest effort gets my attention; the rest becomes “post-launch learning.” This triage usually pares the list to two or three critical unknowns, which I tackle in a “lean research s ...Read More

If I could hand every new PMM one qualitative super-power, it’d be the think-aloud usability walkthrough. Invite a real user, give them a single everyday task (“spin up a streaming key and transcribe a file”), and ask them to narrate every thought as they click. You sit back, say nothing, and let the friction surface itself: hesitation on a button, a muttered “wait—do I pay extra for this?”, or a copy-and-paste from docs that proves the UI isn’t doing its job. I run these in four fast moves: Rec ...Read More

I apply a very Occam’s-razor rule to market research ops: use the simplest system that still makes the insights searchable, up-to-date, and easy to broadcast. Here's the workflow we've built: First, I create a single “home base.” For us that’s an Airtable; a Google Sheet works fine at smaller scale. Every data point—win-loss quote, usability clip, analyst stat—lands here as a row with fields for source, date, persona, JTBD, and a link to the raw artifact (recording, PDF, Tweet, whatever). One ba ...Read More

I lean on market research for every launch, but I never assume we have to start from scratch. First stop is the shared intel repository—usage logs, win-loss notes, usability clips, analyst snippets, social posts. If that evidence answers the core questions, great. If it exposes blind spots, then I commission fresh research matched to the size of the bet. Net-new products or entries into a new category. These moves usually reveal big gaps in the repo—no customer language, no clear decision criter ...Read More

The lack of sticker prices and public roadmaps in enterprise software turns “competitive research” into a bit of a scavenger hunt—but you don’t have to don a trench coat. I start with the richest source: buyers who just finished evaluating vendors. In debriefs with both won and lost prospects, AEs often get forwarded SOWs, red-lined MSAs, or at least a verbal rundown of discount tiers and usage-based add-ons. Those artifacts reveal real street pricing and which features pop onto the invoice only ...Read More

I treat research like a layered funnel—first-party is at the core, second-party for calibration, third-party for context—and let AI widen that throat so the best insights surface fast.We start with what only we own. Product-usage logs, support tickets, sales recordings, and customer notes are both cheapest and most actionable. Each feed pipes into a weekly Claude workflow where it clusters themes, flags sentiment swings, and highlights anomalies (“latency mentioned +43 % week-over-week”). The au ...Read More

Absolutely—market research should live in a single, neutral repository that both PM and PMM treat as their “source of truth.” Whether you stand it up in Airtable, a spreadsheet, or a purpose-built platform, the goal is one table where all market intelligence—competitive teardowns, customer interviews, analyst forecasts, social listening snippets—gets logged with identical light-weight fields (source, persona, date, confidence score, link to the raw artifact). When every fact sits side-by-side, d ...Read More