Modern Customer Research: Continuous Discovery with AI

We explore the foundations of continuous discovery: why one-off research isn't enough, how the "curse of knowledge" blinds product teams, and how Teresa Torres' framework of interviewing, opportunity mapping, and ideation keeps you connected to real customer needs. You'll see how this approach applies whether you're a startup founder, a PM, or part of an agile team.

Time to get practical. We cover the tools you need, how to run a kickoff meeting that gets everyone aligned, and how to translate business goals into actionable product goals using Teresa Torres' Opportunity Solution Tree. You'll also learn how to define team roles and communication rhythms for a successful discovery partnership.

Good screeners are the foundation of good research. We cover how to write multiple-choice questions that capture real behaviours, when to use disqualifying criteria and decoy questions, and how to keep screeners broad enough to support an evolving research focus. You'll also see how to manage screener responses in your Notion Research Hub with filtered views and eligibility tracking.

Consistency is the engine of continuous discovery — but only if the rhythm is sustainable. We explore how to factor in team velocity, research depth, and bandwidth when setting your cadence. You'll learn to use Notion Calendar booking links to streamline scheduling, build in flexibility for cancellations and rescheduling, and discover how async and AI-assisted methods can supplement your core live interviews.

Your moderation guide is a living document, not a rigid script. We cover how to structure interviews for consistency while staying flexible enough to follow fascinating tangents. You'll learn to ask behaviour-specific questions that cut through aspiration bias, probe deeper into user stories, and iteratively refine your guide over time. We also address the critical topic of informed consent when using AI to transcribe and analyse interviews.

In this screencast, we’re building out our Research Hub with a Studies database and generate a discussion guide using Notion AI so everything is ready to go for your first interview.

In this lesson, we build an Interview Snapshot page template from scratch. We set up AI blocks that generate structured analysis from a transcript and walk through a full interview analysis. Whether you've been building your Research Hub step by step or using the done-for-you template, this lesson shows you how everything works under the hood.

You've been analysing individual interviews. Now it's time to zoom out. In this lesson, we walk through a completed Research Snapshot — a weekly synthesis of discovery interviews — and break down what makes it work: the structure, the use of participant quotes, the opportunity framing, and the visual design choices that make it scannable and compelling. This is the "what" and "why" of Research Snapshots.

This is the hands-on build lesson. We walk through creating a Weekly Research Snapshot template in the Studies datasource, then set up the infrastructure that enables automatic AI insights synthesis. By the end, you'll have a working pipeline to synthesise multiple interviews into a stakeholder-ready snapshot.

Even the best-designed processes hit bumps. We tackle the most common challenges — from narrow research scope and stakeholder disengagement to cadence fatigue and vague recommendations. You'll learn practical fixes for each, and run a pre-mortem exercise to anticipate risks before they derail your project.