How to Run Pricing Workshops: A Customer Discovery Framework for Early Stage Founders

Why Early-Stage Pricing Feels Like Guesswork (And How to Fix It)

Setting your first price feels like a high-stakes guess. For early-stage SaaS, Biotech, or Deeptech founders, this single decision can feel more daunting than the product build itself. The fear is real: price too high and you scare away early adopters; price too low and you leave money on the table, shortening your runway. The core problem is not a lack of data, but a lack of a process. Without a clear framework, founders often default to copying competitors or plucking a number from thin air, risking a disconnect with the market from day one.

This guide provides a practical, founder-led framework for how to run pricing interviews with early customers. It is designed to turn qualitative conversations into a confident, testable pricing structure. By following these steps, you can move from uncertainty to a data-informed hypothesis you can take to market, ensuring your pricing reflects the true value you deliver.

The Foundational Mindset: Shift from Selling to Co-Designing

Before you schedule a single conversation, the most critical step is an internal one: shifting your mindset. You are not selling your product during these interviews. The moment a potential customer feels they are being sold to, their guard goes up, and they start thinking about discounts. Instead, you are inviting them to co-design your business model. You are seeking their expert advice on the value of the problem you solve for them. This reframing is essential for eliciting honest, unfiltered feedback.

The data you gather from these conversations is directional, not definitive. With a small sample size, you are not aiming for statistical significance. The goal is a compass, not a GPS. You are looking for patterns and signals that point you toward a reasonable starting point. What we consistently see work in practice is treating the entire exercise as a way to form a testable pricing hypothesis. This removes the pressure to find the one “perfect” price and correctly frames pricing as an ongoing experiment, which is crucial for achieving product-market fit pricing.

Part 1: How to Run Pricing Interviews by Preparing for Success

The quality of your insights depends entirely on the quality of your participants and your questions. A well-prepared workshop is the difference between generating noise and generating a clear signal for your pricing strategy validation.

Recruiting Participants for Signal, Not Statistics

Your insights are only as good as the people you talk to. At this stage, you must recruit for signal, not for statistics. This means prioritizing in-depth conversations with 5-8 individuals who perfectly match your Ideal Customer Profile (ICP) over a large, generic sample. Finding these individuals can be done through targeted outreach on LinkedIn, introductions from your existing network, or by engaging in niche industry communities like specialized subreddits or professional Slack groups. Be specific about the roles and challenges you are looking for to ensure the feedback is relevant.

Crafting the Invitation and Incentive

When you invite potential participants, be explicit that you are seeking their advice on product strategy, not making a sales pitch. Frame it as an opportunity for them to influence the direction of a new tool in their industry. To honor their time and keep the relationship neutral, offer a non-cash incentive. The recommended neutral incentive for their time is a $100-$150 gift card. This compensates them fairly without creating a transactional, discount-seeking dynamic, which could skew their feedback toward lower price points.

Designing Your Question Script for Unbiased Feedback

The questions you ask will determine the quality of your output. Use Mom Test principles to avoid biased or leading questions. Critically, avoid asking “What would you pay for this?” directly, as this invites speculation rather than reflection on true value. Instead, anchor the conversation in their current reality and problems. Start with broad discovery questions to build context:

• “Walk me through how you currently handle [the problem your product solves].”
• “What’s the hardest part about that process? Can you give me an example?”
• “Have you tried to solve this before? What happened with that attempt?”
• “What would it mean for you or your business if this problem was solved perfectly?”

These questions get them talking about value in their own terms. This provides the crucial context you need before ever mentioning a price, allowing you to map your solution to their stated pains and desired gains.

Part 2: Conducting the Interview to Uncover Willingness to Pay

With your script and participants ready, the next phase focuses on executing the conversation. The structure of the interview is designed to prevent your own biases from leading the witness and to create a comfortable space for honest financial feedback.

Structuring the Conversation Flow

A simple, effective flow prevents confusion and builds trust. We recommend a three-part structure for the conversation itself:

1. Problem Deep Dive: Spend the first third of the interview focused entirely on their workflow, their pains, and the impact of the problem. Do not mention your product.
2. Solution Concept Introduction: Briefly introduce your solution as a concept designed to address the pains they just described. Frame it as a potential future, not a finished product for sale today.
3. Value and Price Exploration: Only after they understand the problem and the proposed solution do you transition to a discussion of value and price.

During the entire process, your only job is to listen. Use a tool like Otter.ai or Fathom to transcribe the call so you can remain fully present and focus on follow-up questions instead of scrambling to take notes.

Using the Van Westendorp Method Conversationally

To move from abstract value to concrete numbers, use a simplified, conversational version of the Van Westendorp Price Sensitivity Meter. This technique avoids the single, scary question of "what would you pay?" and instead explores a psychological range. Frame it naturally:

• “Thinking about a solution like this, at what monthly price would you feel it’s so cheap you’d question its quality?” (Too Cheap)
• “At what price would you consider it a bargain, an instant purchase?” (Bargain)
• “At what price would it start to seem expensive, where you’d have to think carefully before buying?” (Expensive)
• “And at what price would it be so expensive you would not consider it at all?” (Too Expensive)

This sequence provides four data points that create a much richer picture of perceived value than a single number ever could.

The Power of Visual Aids

To make the pricing packages feel real, use visual aids. A scenario we repeatedly see is founders sharing their screen and walking through Figma mock-ups of potential pricing tiers. Showing a visual representation of what is included in a “Basic” versus a “Pro” plan makes the value exchange tangible. This simple step helps generate much more specific feedback on feature differentiation and packaging, as participants can react to concrete bundles rather than abstract concepts.

Part 3: Synthesizing Qualitative Feedback into a Pricing Strategy

After completing your 5-8 interviews, you will have a collection of transcripts and notes. The next step is to bridge the gap from qualitative insight to a quantitative starting point for your pricing model.

From Transcripts to Patterns: Identifying Customer Segments

Begin by systematically reviewing your transcripts. Look for common language used to describe the core problem, the desired outcomes, and the perceived value. As you read, tag recurring themes and phrases. Group interviewees by the similarities in their answers; these groupings often represent your initial customer personas or segments. For example, you might find one group talks about saving time, while another talks about generating new revenue. These are likely two different segments with a different willingness to pay.

Mapping Price Sensitivity by Persona

For each persona or segment you identified, map out the four price points you gathered (Too Cheap, Bargain, Expensive, Too Expensive) in a simple spreadsheet. You will likely see clusters emerge. For example, your “Solo Consultant” persona may have a bargain price around $50/month, while your “Small Agency” persona might see $250/month as a bargain. This analysis forms the empirical foundation for tiered pricing that serves distinct customer needs.

How to Handle Outliers in Your Pricing Research

While analyzing this data, you will inevitably encounter outliers whose price points are dramatically higher or lower than the rest. Do not immediately discard them. An outlier might indicate a misunderstanding of the value proposition, or it could signal an entirely new, high-value customer segment you had not considered. Analyze the “why” behind their number. Did they describe a fundamentally different, more critical use case? If so, that data point is a valuable signal, not noise, and warrants further investigation.

Discovering Your Core Value Metric

As you review their language, pay close attention to the units of value they mention. Do they talk about success “per project,” “per user,” “per report,” or “per successful experiment?” Using transcripts and notes helps you preserve the exact phrasing customers use. This is your value metric: the core unit of consumption that the customer is actually buying. It is the most critical component of a scalable and fair pricing model because it aligns the price they pay with the value they receive.

Part 4: Building Your V1 Pricing Model

With your personas, price ranges, and potential value metrics identified, you can now construct your first pricing model. The goal at this stage is not perfection but a clear, testable structure that is directly informed by your customer conversations.

Choosing a Value Metric Over a Price Point

A lesson that emerges across nearly all early-stage pricing research is that discovering the correct value metric is more important than the price point itself. For example, consider a SaaS company providing data analytics for e-commerce stores using Shopify. They initially planned to price “per user” (per seat). But in their interviews, they discovered that store owners did not care about how many staff members could log in; they cared about the number of actionable sales reports the tool generated. Their true value metric was not “per user,” but “per report generated.” Shifting the model to align with this metric made the value proposition instantly clearer and more compelling to customers.

Packaging Features into Tiers

Using your value metric as the foundation, you can begin clustering features into packages. The recommended number of initial pricing tiers to create is 2-3. This provides choice without causing paralysis. A common framework for packaging is to structure tiers based on customer maturity:

• Tier 1 (Starter): Solves the core problem for a single user or a very small team.
• Tier 2 (Professional): Adds features for collaboration, automation, and increased usage for growing teams.
• Tier 3 (Enterprise): Adds advanced security, compliance, and dedicated support for large organizations.

A Practical Example: Pricing for a Biotech Startup

Consider a platform-based Biotech startup whose software accelerates compound screening. Based on their interviews with academic labs and commercial R&D teams, they might build a V1 model like this:

• Tier 1: Researcher. Aimed at PhDs and postdocs (Persona A). Priced around their "Bargain" point. Value Metric: 50 compound analyses per month. Key Feature: Core screening algorithm.
• Tier 2: Lab. Aimed at small research teams (Persona B). Priced between their "Bargain" and "Expensive" points. Value Metric: 250 compound analyses per month. Key Features: Everything in Researcher, plus collaboration tools and shared project spaces.
• Tier 3: Enterprise. Aimed at large pharma. Priced via custom quote, informed by their "Expensive" data. Value Metric: Unlimited analyses. Key Features: Everything in Lab, plus advanced security, compliance support, and a dedicated science liaison. For specific guidance on this tier, see our guide to Enterprise Pricing.

This structure directly maps the pricing and packaging to the distinct needs and willingness to pay of each discovered persona.

From Hypothesis to Market: Your Next Steps

The framework outlined here is designed to de-risk one of the most critical early decisions a startup makes. By shifting your mindset from selling to co-designing, you can gather honest early adopter feedback that serves as the foundation for your entire commercial strategy.

Remember that your goal changes with your company’s stage. For Pre-Seed and Seed stage companies, the objective of customer interviews for pricing is validation: Is someone willing to pay for this, and roughly how much? For Series A and B companies, the goal shifts to optimization: How can we refine our tiers, value metric, and price points to maximize growth and revenue?

The price you launch with is not final. It is a hypothesis. This process gives you a well-researched, defensible hypothesis to test in the market. As you grow, ensure your model accounts for local regulations. For example, UK-based companies must follow VAT place of supply rules, while US companies must navigate complex state-specific sales tax laws. The greatest risk is not in getting the price slightly wrong, but in failing to build a systematic process for listening to your customers and learning from their perception of value.

For your next steps, identify and reach out to those initial 5-8 people who represent your ideal customer. Treat them as collaborators on your pricing strategy. The conversations you have with them will be the most valuable data you can collect. They are not just sales prospects; they are the co-designers of your future success.

Frequently Asked Questions

Q: How many pricing interviews are truly enough for an early-stage startup?

A: For early-stage validation, 5-8 in-depth interviews with your Ideal Customer Profile are typically sufficient. The goal is to identify recurring patterns and achieve qualitative saturation, not to reach statistical significance. Once you hear the same themes repeatedly, you have a strong directional signal to build your initial hypothesis.

Q: What should I do if a customer is reluctant to talk about specific price numbers?

A: If a participant is hesitant, reframe the question away from their personal budget. Ask about their company’s budget for tools that solve similar problems, or use analogies. For example, "Is a solution like this priced more like a utility such as your electricity bill, or more like a specialist consultant?"

Q: How is this customer willingness to pay research different for Deeptech or Biotech compared to SaaS?

A: The core principles of value-based pricing research are the same, but the value metrics are often different. While a SaaS company might use "per user" or "per report," a Biotech or Deeptech firm's value could be tied to research milestones, accelerated speed to market, or the value of the intellectual property generated.

Q: How often should we revisit our pricing after launch?

A: Treat your pricing like a product feature that requires regular iteration. Plan to review your pricing and packaging at least annually, or whenever you release significant new functionality. For early-stage companies, it is wise to conduct lighter-weight pricing check-ins with new customers on a quarterly basis to stay aligned with the market.