Customer Discovery Research Methods

Session 4 Recap of Professors Eisenmann & Bernstein’s new HBS class on Ideation and Prototyping for Innovation

Converging on a “Problem Worth Solving”

Professors Shai Bernstein and Tom Eisenmann opened this session by stressing a central challenge of innovation: identifying a Problem Worth Solving. Not every idea deserves to become a startup. The test is whether the problem is significant enough to matter—and whether it can be solved in a way that creates real value.

They reminded students that this stage is not about rushing into solutions. Instead, the work lies in digging deeper: uncovering the root causes of a problem, separating surface-level symptoms from underlying needs, and deciding if the problem space itself merits further exploration.

Deepening the Research

By Session 4, students were already deep into product and company research. Their focus was on sharpening problem definitions through multiple approaches:

• Interview Excerpts — pulling insights directly from conversations with potential users or customers.

• Updated Personas — refining profiles to reflect evolving understanding of who the customer really is.

• Journey Mapping — charting pain points and unmet needs across a customer’s experience.

• Preliminary Market Sizing — evaluating the scale and urgency of the problem.

Each student was tasked with conducting 5–10 discovery interviews and capturing detailed notes in their Primer. The goal: ensure hypotheses were supported by real-world evidence, not assumptions.

Peer-to-Peer Feedback in Action

The session also featured a structured peer-to-peer feedback discussion. Teams submitted one or more problem statements via a Google poll (one per team), ensuring everyone had material ready for review.

During the exchange, teams presented the evidence they had—or pointed out what was still missing. This openness created space for richer, more collaborative feedback. Classmates could challenge assumptions, suggest new directions, and highlight gaps worth exploring.

Each team shared their problem statement(s) on the spot, whether through printed copies or by pulling them up on a laptop. The exercise fostered both accountability and collective learning.

Key Learning

You are not your customer—and you can only solve a problem you truly understand. Founders often mistake their own perspective or love of technology for customer need, but real progress comes from humility, immersion, and peeling back surface symptoms to uncover root causes. Deep understanding requires living in the customer’s world, listening more than assuming, and recognizing the messy realities—data, workflows, incentives, and constraints—that shape adoption. Without this, you risk building elegant solutions that nobody uses; with it, you build products that solve meaningful problems and create lasting value.

Use of Generative AI to Accompany the Research

Customer discovery is a disciplined, evidence-driven process that blends generative AI with firsthand research. It begins with source tracking and aggregation, then moves to synthesis, stakeholder mapping, and hypothesis formation. Founders triangulate signals, trends, and market sizing to keep assumptions grounded, while expert input and interview role-play add depth. AI accelerates insight generation through summarization and sentiment analysis, and contrarian testing stress-tests assumptions. The result: a scalable yet nuanced workflow that produces actionable, evidence-based insights.

The Bigger Lesson

This session underscored a key principle of the course: rigorous, collaborative problem discovery is the foundation of innovation. Before building, before scaling, before pitching—students must prove the problem is real, urgent, and solvable.

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