Table of Contents
Google Ventures UX researcher Michael Margolis reveals his proven framework for finding your most likely early adopters before you waste time building something nobody wants.
Michael Margolis has compressed 30 years of customer research into a single-day process that helps startups avoid the biggest mistake founders make: building for everyone instead of someone specific.
Key Takeaways
- Bullseye customers are more specific than ICPs—they're the exact subset most likely to initially adopt your product or service.
- The "5-3-1" formula uses five bullseye customers, three simple prototypes, and one intensive day of team-watched interviews.
- Narrow targeting should feel "comically narrow" with roughly seven specific attributes including inclusion, exclusion, and trigger criteria.
- Past customer behavior trumps future predictions—focus on what people have done, not what they say they'll do.
- Qualitative interviews in small batches reveal patterns that individual scattered conversations cannot expose.
- Team watch parties eliminate the need for research reports while building consensus and momentum around customer insights.
- Multiple prototypes prevent over-attachment to single ideas while helping customers articulate preferences through comparison shopping.
- Most expert teams consistently overestimate customer knowledge, problem awareness, willingness to pay, and readiness to buy.
- Successful bullseye identification creates visible customer excitement—people asking "can I sign up for this?" rather than polite encouragement.
Timeline Overview
- 00:00–09:11 — Michael's Background: 30+ years of UX research from anthropology to Walmart to Google to GV, working with 300+ companies across diverse industries
- 09:11–12:32 — Bullseye vs. ICP Definition: How bullseye customers are more specific than ideal customer profiles, focusing on initial adoption likelihood
- 12:32–20:56 — Sprint Overview: The "5-3-1" formula combining five customers, three prototypes, one day of team-watched interviews for maximum learning velocity
- 20:56–22:19 — When to Use the Method: Before building, expanding markets, changing sales motions, or troubleshooting lukewarm traction
- 22:19–23:48 — Step One: Goal Alignment: 45-minute team meeting to identify key questions, assumptions, and nagging debates that need resolution
- 23:48–29:00 — Step Two: Bullseye Definition: Team exercise to narrow customer definition, pushing past comfortable generalizations to specific characteristics
- 29:00–38:24 — Real Example: Specialty Medications: Case study showing progression from general delivery service to refrigerated medication focus with predictable windows
- 38:24–43:28 — Narrowing Framework: Seven-attribute approach using inclusion criteria, exclusion criteria, and trigger events for precise targeting
- 43:28–56:11 — Step Three: Recruitment Process: Using screener questionnaires through platforms like UserInterviews.com, compensating appropriately for attendance
- 56:11–01:01:10 — Step Four: Prototype Creation: Building three distinct value proposition examples as flat PDFs focusing on clear positioning over functionality
- 01:01:10–01:08:49 — Step Five: Interview Guide: Two-part structure starting with discovery about past experiences before showing prototypes for contextualized feedback
- 01:08:49–01:19:40 — Step Six: Watch Party Method: Live-streamed interviews with team note-taking, structured debriefs, and consensus-building through shared observation
- 01:19:40–01:24:43 — Common Pitfalls and Success Patterns: Avoiding mushy targeting, recognizing genuine excitement vs. polite feedback, curse of knowledge blindspots
- 01:24:43–End — Biotech Applications and Resources: Expanding methods beyond tech startups, free book availability, contact information for feedback
The Problem: Building for Everyone Means Building for No One
Every ambitious founder dreams of building a product for everybody, but success requires starting much more narrowly. Amazon began selling only books. Facebook served only college students. These companies understood a fundamental truth that Michael Margolis has witnessed across 300+ research sprints: you must identify your bullseye customer before you build, or risk wasting months on products nobody needs.
A bullseye customer represents the very specific subset of your target market who initially is most likely to adopt your product or service. This goes beyond traditional ideal customer profiles (ICPs) to pinpoint exactly who will say "yes, I need this thing" when presented with your solution.
The stakes are enormous. Margolis regularly sees founders spend months building complex products—hardware, software, subscription services—only to discover through his research process that their target customers don't actually want what they're offering. In one case, a founder went through three rounds of bullseye customer research before killing a project entirely, calling it the most valuable outcome because it saved him from building something destined to fail.
The bullseye customer framework solves multiple problems simultaneously: it helps prioritize what to build, clarifies which feedback matters, and aligns entire teams around shared customer understanding. When everyone agrees on exactly who you're serving first, decisions become dramatically clearer.
The 5-3-1 Formula: Maximum Learning in Minimum Time
Margolis has distilled decades of research expertise into a deceptively simple formula: five bullseye customers, three simple prototypes, and one intensive day of team-watched interviews. This combination generates more actionable insights than months of scattered customer conversations.
The five-customer threshold hits what researchers call "data saturation"—the point where additional interviews start repeating the same patterns. Teams consistently report feeling overwhelmed by similarities after four or five sessions, creating natural urgency to synthesize insights and take action.
Three prototypes enable comparison shopping rather than single-point feedback. When customers evaluate multiple options, they reveal preferences by contrasting features, delivery methods, pricing models, and value propositions. This generates much richer feedback than showing one concept and asking for opinions.
The one-day compression creates shared team experience that eliminates traditional research bottlenecks. No reports to write, no findings to present, no months of debate about what customers "really meant." Everyone watches the same conversations and reaches consensus in real-time.
This approach particularly benefits distributed teams where alignment becomes challenging. When engineers, designers, product managers, and founders all witness the same customer reactions simultaneously, they build shared empathy and understanding that persists long after the research concludes.
Step-by-Step: From Vague Ideas to Specific Targets
The process begins with a 45-minute team alignment meeting focused on a crucial question: what keeps you up at night? This forces teams beyond surface-level concerns toward fundamental uncertainties about their product, market, or customers.
Teams must articulate specific hypotheses rather than vague hopes. Instead of "we'll learn how customers respond to our concept," effective questions sound like "customers will prefer ASAP delivery over predictable time windows" or "people will trust drone delivery for expensive medications."
The second step involves defining bullseye customers through structured team interrogation. Margolis keeps asking "what do you mean by that?" until teams move past comfortable generalizations toward measurable, concrete characteristics.
This often reveals surprising disagreements within founding teams. When one person says "busy professionals" and another envisions "parents with young children," the resulting customer research will inevitably feel mushy and inconclusive. The definitional exercise forces alignment before any actual customer contact begins.
Real Example: From General to Specific
Consider a startup developing specialty medication delivery. Initial thinking might target "people who need expensive prescription medications delivered." This feels specific but lacks actionable precision for research purposes.
Through bullseye customer definition, the team might narrow to: people taking refrigerated specialty medications, who have used delivery services like Uber Eats previously, who manage their own prescriptions (not in assisted living), who live in urban areas dense enough for delivery logistics, and who have been on their current medication for at least six months.
Even this detailed profile proved insufficient. After initial research revealed lukewarm interest, the team discovered that customers with refrigerated medications faced much more acute delivery timing needs. They couldn't leave expensive, temperature-sensitive drugs sitting on doorsteps.
This insight led to a second research round targeting people with cold-chain medications who were dissatisfied with current delivery options and needed predictable delivery windows rather than ASAP service. This group showed genuine excitement and buying intent that the broader category never demonstrated.
Seven Attributes: The Narrowing Framework
Effective bullseye customers typically involve roughly seven specific attributes across three categories: inclusion criteria, exclusion criteria, and trigger events.
Inclusion criteria establish basic qualifications: industry, company size, current product usage, geographic location, or demographic characteristics. These usually come easily to founding teams since they represent obvious target market boundaries.
Exclusion criteria require more deliberate thought but often prove more valuable. Teams might exclude expert users who don't represent typical customers, existing competitive product users who are locked into alternatives, or people with specific past experiences that make them unsuitable research participants.
Trigger events identify specific situations that make someone particularly ready for your solution. A new chief security officer reviewing vendor relationships, parents who just had their first baby and need life insurance, or companies that recently experienced a cybersecurity incident all represent triggered states where people actively seek new solutions.
The narrowing should feel "comically narrow" according to one framework user. If teams feel comfortable with their targeting, they probably haven't gone specific enough to generate actionable insights.
Recruitment: Finding Your Needle in the Haystack
Once teams align on bullseye customer definition, recruitment becomes a filtering exercise. Margolis creates screener questionnaires that translate customer criteria into questions without telegraphing "right answers."
Instead of asking "Do you listen to Lenny's podcast?" (which invites false positives), better approaches ask "What podcasts do you regularly listen to?" or "Where do you get trusted product management information?" These open-ended versions reveal authentic behavior patterns.
Platforms like UserInterviews.com dramatically accelerate this process. Margolis can typically recruit specific customer types within three to four days, sometimes receiving hundreds of responses that require careful filtering to identify genuine matches.
Compensation matters significantly for attendance reliability. Margolis pays $125 per hour for most consumer interviews, scaling up to $400 per hour for busy professionals like attorneys. When five precious interview slots support team-wide research investment, no-shows become expensive failures.
The recruitment difficulty itself provides valuable market intelligence. If your bullseye customers prove nearly impossible to find and recruit, this suggests potential challenges for actual sales and marketing efforts later.
Prototypes: Comparison Shopping for Product Concepts
Margolis emphasizes building three distinct prototypes rather than iterating on single concepts. This prevents teams from becoming overly attached to specific features while helping customers articulate preferences through comparison.
These prototypes remain intentionally simple—typically flat PDFs rather than functional demos. The goal involves clearly communicating distinct value propositions and problems being solved, not demonstrating technical capability or polished design.
For the medication delivery example, three prototypes might emphasize different variables: pharmacist delivery versus courier versus drone delivery, combined with different timing promises like "within one hour," "predictable 15-minute window," or "sometime today when you're available."
Customers respond to these options by identifying preferred combinations: "I like that this one has pharmacist delivery, but I prefer the predictable timing window from this other option." This generates ideal feature combinations that no single prototype presented.
The writing exercise forces teams to articulate clear positioning for each concept. Marketing speak and vague benefits get exposed quickly when customers can't understand what makes each option distinct or valuable.
Interview Structure: Past Behavior Predicts Future Action
Each one-hour interview follows a two-part structure designed to build context before gathering feedback. The first thirty minutes explore customers' existing experiences, past solutions, current pain points, and previous purchasing decisions.
This discovery phase serves multiple purposes beyond information gathering. It builds rapport with interviewees, helps them feel comfortable sharing detailed stories, and provides crucial context for interpreting their later reactions to prototypes.
Margolis emphasizes putting much more weight on past experiences than future predictions. When someone describes never having anything delivered but claims they'd "totally use" a delivery service, this contradiction suggests skepticism about their actual likelihood to adopt.
The prototype evaluation phase leverages comparison shopping psychology. Rather than asking for winner selection, Margolis encourages customers to identify preferred elements across different options. This generates building blocks for ideal solutions rather than binary preferences.
Teams watching these conversations gain invaluable context. When a customer reacts negatively to a prototype feature, observers understand the reasoning based on earlier stories about past negative experiences with similar services.
Watch Parties: Building Consensus Through Shared Observation
The team watch party represents Margolis's "magic hack" for turning research into action. Rather than conducting interviews privately and presenting findings later, entire teams observe customer conversations in real-time.
This approach eliminates traditional research bottlenecks. No time spent writing reports, no meetings to present findings, no debates about what customers "really meant." Teams witness conversations directly and reach consensus through shared experience.
Structured note-taking ensures focus during intense observation periods. Teams assign rotating roles for detailed documentation while maintaining Slack channels for quick questions and observations. This prevents observers from checking email or losing attention during crucial moments.
Between interviews, teams conduct structured debriefs using predefined spreadsheets that capture key learning goals. By day's end, patterns become obvious across multiple similar conversations rather than requiring complex analysis to identify themes.
The process concludes with individual reflection where each team member independently identifies big takeaways, recommended next steps, and bullseye customer definition adjustments. This prevents groupthink while ensuring everyone processes insights personally.
Recognizing Success: When You Find Product-Market Fit
Genuine bullseye customer identification creates unmistakable enthusiasm. Customers lean forward, ask follow-up questions, and inquire about availability rather than offering polite encouragement. Margolis describes being able to "sense the energy and excitement" when concepts resonate strongly.
This contrasts sharply with lukewarm feedback that sounds positive but lacks urgency. Comments like "that sounds interesting" or "I could see using that" typically indicate insufficient problem-solution fit despite superficially encouraging language.
One of the most valuable outcomes involves learning what "no" looks like. Through repeated exposure to customer conversations, teams develop intuition for distinguishing genuine interest from social politeness. This skill prevents wasted effort on concepts that generate neutral reactions.
Successful bullseye identification often reveals one or two distinguishing characteristics that matter most. Instead of seven screening criteria, teams discover that customers with refrigerated medications or triggered by specific life events represent the core addressable market worth prioritizing initially.
Common Pitfalls: How Expert Teams Get Blindsided
The most frequent failure mode involves insufficient narrowing during customer definition. When recruitment feels "mushy" or interview insights seem inconsistent, teams usually failed to specify bullseye customers precisely enough for meaningful pattern recognition.
Expert teams consistently fall victim to the "curse of knowledge"—overestimating customer awareness, problem urgency, willingness to pay, and readiness to purchase. Founders deep in their problem space struggle to imagine that others don't share their expertise or urgency.
This creates predictable blind spots across multiple domains. Teams assume customers understand technical concepts, feel acute pain around problems, or are actively seeking solutions when reality often involves much lower awareness and urgency than experts anticipate.
Another common mistake involves letting existing relationships contaminate research. When teams recruit customers they already know or include internal experts in interview pools, results become skewed toward artificially positive feedback that doesn't represent typical market responses.
Applications Beyond Tech Startups
Margolis increasingly applies these methods in biotech, where product development cycles span years and customer feedback becomes crucial for eventual commercialization success. Clinical trial recruitment, physician workflow integration, and patient adoption all benefit from bullseye customer clarity.
Healthcare teams often use different terminology—"target product profiles" instead of customer research—but face identical challenges around customer definition, market validation, and stakeholder alignment. The same methods transfer directly with minor language adjustments.
Manufacturing, financial services, and professional services companies also adapt this framework successfully. Any organization building new products or entering new markets can benefit from precisely identifying their most likely early adopters before significant investment.
The key insight transcends industry boundaries: successful innovation requires understanding exactly who needs your solution most urgently, rather than trying to serve broad markets from the beginning.
Common Questions
Q: How is a bullseye customer different from an ideal customer profile?
A: Bullseye customers are more specific and focused on initial adoption likelihood, while ICPs often describe broader target markets for eventual scaling.
Q: What if I can't find people matching my bullseye customer definition?
A: This suggests your definition may be too narrow, or these customers might not exist in sufficient numbers for a viable business.
Q: Should I use functional prototypes instead of flat PDFs?
A: Keep prototypes simple to avoid over-attachment and focus on value proposition clarity rather than functionality demonstration.
Q: How do I know if customers are genuinely excited or just being polite?
A: Look for forward-leaning body language, follow-up questions, and inquiries about availability rather than vague positive comments.
Q: Can this method work for B2B enterprise products?
A: Yes, but recruitment may require different approaches like professional associations, snowball sampling, or conference outreach rather than consumer panels.
Conclusion
The bullseye customer framework transforms the most crucial early-stage startup decision from guesswork into systematic investigation. By investing one intensive day in precisely understanding your most likely adopters, teams avoid months of building products nobody wants while gaining confidence to pursue opportunities with genuine market demand.
This research investment pays dividends throughout the product development process. Clear customer definition guides feature prioritization, marketing message development, sales strategy formation, and partnership evaluation. Most importantly, it prevents the heartbreak of launching products that solve problems nobody actually has.
Practical Implications
- Schedule bullseye customer research before significant product development investment to avoid building unwanted features or products
- Define customers using roughly seven specific, measurable attributes across inclusion, exclusion, and trigger event categories until targeting feels "comically narrow"
- Recruit participants through screening questionnaires that don't telegraph desired answers, compensating appropriately for reliable attendance
- Create three simple prototype concepts as flat PDFs focusing on distinct value propositions rather than functional demonstrations
- Structure interviews with discovery about past experiences before prototype evaluation to provide context for customer feedback
- Organize team watch parties where everyone observes interviews simultaneously to build shared customer understanding without requiring research reports
- Weight past customer behavior much more heavily than future predictions when evaluating likelihood of actual product adoption
- Look for genuine customer excitement through forward-leaning engagement and follow-up questions rather than polite positive feedback
- Use recruitment difficulty as early market intelligence about potential sales and marketing challenges for your target segment
