Between idea and market entry, innovative ventures face substantial risks. Many startup teams develop their product in isolation initially and only discover at market entry that the target audience reacts differently than expected. This is precisely where the lean startup approach comes in. Systematically conducting customer interviews in the early development phase forms the foundation for realistically assessing future demand and avoiding costly product misdirections.
Those who shift their role from salesperson to listener gain deep insights into their target audience’s daily life. The challenge lies in structuring conversations so they deliver reliable data rather than polite pleasantries. Particularly in tech- and research-focused startup hubs like Munich, this empirical groundwork often determines long-term success and later readiness for funding rounds.
Why early-phase interviews fail
Most conversations with potential customers fail because founders unconsciously seek validation. They present their business idea with great enthusiasm and ask the other person how they think the concept is. The answers are almost always worthless. People tend to respond politely in interview situations to avoid disappointing the inventor. They praise the idea, praise the design, and nod along with the presentation.
Another problem is hypothetical questions directed toward the future. The question of whether someone would purchase specific software for a monthly fee leads to pure speculation. The human brain is poor at precisely predicting its own future behavior and future spending. Only when the real purchase decision is at hand do actual barriers emerge, such as budget limits, priorities, or existing contracts.
The framework for the interview: asking for facts
A successful learning conversation requires a structured script that invites stories and concrete experiences rather than provoking standardized yes-no answers. The methodology championed by subject matter experts like Rob Fitzpatrick recommends focusing on three core areas: the concrete problem, current workarounds, and financial constraints.
1. Validating the problem pain point
The conversation begins with questions about the starting situation in the target person’s daily life. The team determines how the person solves a specific task today and what frustrations arise in the process. It’s important to ask about the most recent concrete incident, since memories of general routines are often imprecise.
- Better to ask: “Tell me how you solved this problem last week. What was the most difficult step?”
- Avoid: “Do you often have problems coordinating schedules?”
2. Uncovering existing workarounds
If genuine suffering exists in the market, the target audience is already making active attempts to circumvent the problem. They build makeshift solutions from spreadsheets, use incomplete free tools, or combine various service providers. If the team discovers in the interview that the person has never sought a solution despite the alleged problem, the pain point for a new commercial product is likely too low.
- Better to ask: “What tools or workarounds do you currently use for this and how much time do you invest weekly in these processes?”
- Avoid: “Would an automated app for this process be a relief for you?”
3. Measuring economic urgency
Budget for new solutions rarely exists independently of existing expenses. In the interview, willingness to pay can be determined by capturing spending on current workarounds. If a company is already spending money monthly on inefficient software or paying for labor on manual processes, the economic value of your own innovation can be directly quantified.
- Better to ask: “How much money did you spend last month to remedy this situation manually? Who in your organization had to approve this expense?”
- Avoid: “Would you pay ten euros per month for software that does this work for you?”
Finding the right interview partners
To identify usable patterns, the profile of interview partners must be precisely tailored to the assumed target audience. General surveys among acquaintances or on the street provide no data because specific technical problems or life circumstances are missing. The team creates a precise profile of the ideal customer in advance. For business customers, this concerns the industry, company size, and the exact position of the person in the organization. For end consumers, behaviors, habits, and specific pain points take center stage.
Recruitment in practice occurs through various channels. For digital B2B models, platforms like LinkedIn are suitable for specifically contacting professionals and decision-makers. The message should be kept brief and emphasize the learning aspect. Those who emphasize that this is not a sales conversation receive a significantly higher response rate. For consumer goods projects, subject-specific online forums, industry events, or directly approaching people in situations where the problem occurs are suitable. For a first qualitative indication, five to ten interviews usually suffice. To obtain a solid foundation for building a prototype, successful teams typically conduct twenty to thirty structured individual conversations in practice.
Conducting the interview
The actual interview ideally lasts between 30 and 45 minutes and should be conducted as a one-on-one conversation. Group discussions often result in opinions influencing each other or dominant individuals dominating the feedback. At the start, the interviewer makes clear that the project is in an early phase and honest, even negative feedback is of greatest value for development. When the respondent begins to contribute their own ideas or criticize the process, those are valuable signals. An audio recording—with the person’s consent—secures the results for later analysis, so the interviewer can focus fully on the nuances and reactions.
Structured analysis after the interviews
After completing the interview series, the collected protocols must be systematically evaluated to exclude subjective perception errors of the founding team.
Step 1: Reviewing and coding the material
The audio recordings are transcribed in summary form. When reviewing the texts, the team assigns fixed categories to the statements. A color system or tabular structure in tools like Notion or Excel has proven effective. Relevant categories for coding are:
- Problem intensity: Passages where the user expresses clear frustration.
- Current costs: Concrete sums or time units that accrue for workarounds.
- Usability barriers: Fears or technical limitations of the target audience in daily life.
- Unexpected insights: Topics the team hadn’t considered beforehand.
Step 2: Condensing in the empathy map
The coded statements from individual interviews are then brought together in an empathy map. This tool structures the findings into four quadrants: What does the target audience say? What does it think? What does it do? What does it feel? Through this visual presentation, the focus shifts away from bare statistics toward a deep understanding of users’ lived reality.
Step 3: Recognizing patterns
Individual reports are now placed side by side to identify statistical clusters. For example, if eighty percent of surveyed managers complain about the complicated onboarding of new employees, this sub-problem is verified. It serves as the core for the specification of the later product. However, if the pain points of respondents differ fundamentally from each other, this suggests that the target audience was chosen too broadly or the assumed problem has no relevance in reality.
When the problem is considered validated
The problem is verified when a clear majority of interview partners independently report similar frustrations, are already actively seeking solutions, or expend measurable resources for workarounds. In this case, the startup exits the pure survey phase.
When a pivot becomes necessary
If the data shows that the problem plays little role in respondents’ daily lives, the target audience is unwilling to spend money on a solution, or the pain point is already completely covered by existing offerings, the team must react. Clinging to the original idea at this phase will inevitably lead to a product developed past the market.
The startup uses the insights gathered for a course correction (pivot). This might mean retaining the technology but transferring it to a completely different industry. Often, interviews also reveal a hidden secondary problem that proves significantly more lucrative than the original main idea.
Connection to Munich support structures
For startups in Munich, thorough documentation of this interview phase has direct practical significance. The local ecosystem, with its university founding centers and government support programs, places great value on data-based validation before financial resources flow.
When applying for the EXIST founding grant at the Technical University of Munich or Ludwig Maximilian University, awarding bodies require clear evidence in the business plan that market potential is backed up by qualitative customer feedback. The judges of Munich’s Business Plan Competition by Baystartup also rate submissions in the initial phase significantly better when the team presents concrete findings from their own structured interviews instead of vague market estimates.
Local venture capitalists and Munich’s business angel networks use this data during initial review (due diligence). In a market environment that recorded a regional funding volume of 2.7 billion euros in 2025, capital providers preferentially invest in teams that can demonstrate through methodical surveys that they precisely understand their target audience and build their product on real behavioral data.
