Designing a Demand Survey

This toolkit provides a practical structure for designing a demand survey.

It assumes you have already completed Toolkit — Framing a Demand Learning Problem, including:

If those elements are not yet clear, return to the problem framing toolkit before proceeding.

The goal of this toolkit is not to guarantee accurate demand estimates. Its goal is to help you design a survey that produces interpretable demand evidence rather than misleading confidence.

How to Use This Toolkit

This toolkit is organized as a sequence of survey sections.

Each section includes:

  • its purpose,
  • example question types or wording,
  • and notes on common failure modes.

You should be able to translate each section directly into your preferred survey tool.

Screening the Population

Purpose
Ensure that respondents belong to the customer population whose demand you are trying to learn.

Demand evidence is only meaningful if it comes from people who could plausibly face the decision you are studying.

What to Include

  • One or more screening questions that establish eligibility
  • Clear exclusion of respondents who do not fit the target customer definition

Example Prompts

  • “Do you currently [engage in X / face Y problem / purchase Z]?”
  • “Which of the following best describes your role or situation?”

Design Notes

  • Screen early. Do not ask valuation questions before eligibility is confirmed.
  • Avoid overly permissive screens. Broad samples dilute demand signals.
  • If many respondents fail the screen, that is useful information—not a failure.

Establishing Context and Purpose

Purpose
Frame the survey as a request for informed judgment, not a transaction or a pitch.

This section sets expectations and reduces both strategic lowballing and sympathetic overstatement.

What to Include

  • A brief description of what is being considered
  • Why the respondent’s honest input matters
  • An implicit sense of scarcity or consequence

Example Language (Adapt, Do Not Copy)

  • “We are evaluating whether to move forward with a new offering.”
  • “Your answers will help determine whether this is built and how it is priced.”
  • “Honest answers matter more than encouraging ones.”

Design Notes

  • Do not oversell the product.
  • Do not promise that the product will exist.
  • The goal is to make the decision feel real, not promotional.

Grounding the Problem and Solution

Purpose
Ensure that respondents understand the problem and proposed solution well enough to imagine a real decision.

Willingness-to-pay responses are meaningless if respondents are guessing about what is being offered.

What to Include

  • A clear description of the problem being addressed
  • A concrete description of the proposed solution
  • Enough detail to approximate a checkout-level understanding

Design Notes

  • This is not marketing copy.
  • Avoid aspirational language.
  • If respondents would need more information before buying in real life, include it here.

Evaluating Appeal Before Price

Purpose
Allow respondents to express judgment about quality and appeal without anchoring on price.

This separates evaluation from valuation and reduces transactional bias.

What to Include

  • A general appeal or “wow factor” rating
  • Open-ended questions about strengths and concerns

Example Prompts

  • “On a scale from 1–10, how appealing is this offering overall?”
  • “What do you like most about this?”
  • “What concerns you or gives you pause?”
  • “What could be improved?”

Design Notes

  • Average appeal scores below the mid-range often indicate weak demand regardless of price.
  • Open-ended responses are as valuable as numeric ratings.
  • Do not interpret high appeal as proof of demand.

Willingness to Pay

Purpose

Elicit monetary valuation as evidence about demand boundaries, not as a pricing recommendation.

This section should come after appeal has been assessed.

What to Include
  • A willingness-to-pay question appropriate to the demand type
  • Clear reference to the defined unit and decision period

For Yes/No Demand

Ask for the maximum price at which the respondent would still choose to buy one unit in the defined period.

Design Notes
  • Avoid asking for a “reasonable” or “fair” price.
  • Make sure respondents know exactly what one unit represents.
  • Treat responses as boundaries, not promises.

For How-Many Demand

Designing surveys for how-many demand requires additional care.

When customers may consume multiple units within a decision period, demand is not just about whether they buy—it is about how much they buy at different prices. That means quantity must be elicited explicitly rather than inferred.

There are two practical ways to gather this information. Both can work. Each involves tradeoffs between respondent burden, realism, and bias.

The key is to choose deliberately.

Approach 1: Name a Sequence of Prices and Ask Quantity at Each

In this approach, respondents are shown a small set of prices and asked how many units they would consume at each price.

Example framing:
“At a price of $X, how many units would you consume per [period]?”

Design Notes
  • This approach is intuitive. It mirrors how people often encounter prices in the real world and make purchase decisions.
  • It produces direct quantity responses at specific prices, which can feel reassuring.
  • The main risk is survey fatigue. Respondents are asked essentially the same question repeatedly, differing only in price. As attention declines, later responses may become less thoughtful.
  • To mitigate this, the number of prices should be kept small—typically no more than 4–8.
  • This structure can also make the interaction feel transactional rather than collaborative, which may affect how carefully respondents engage.

This approach is best used when simplicity and transparency are priorities and respondent burden can be kept low.

Approach 2: Anchor at Zero Price and Maximum Willingness to Pay

In this approach, quantity is elicited at two meaningful anchors rather than across many prices.

The sequence is:

  1. Ask how many units the respondent would consume if the price were permanently $0.
  2. Ask for the respondent’s maximum willingness to pay for one unit.
  3. Ask how many units they would consume at that maximum price.
Design Notes
  • This approach uses fewer questions, reducing fatigue and maintaining engagement.
  • Each question asks the respondent to think in a different way, which helps re-engage attention.
  • Beginning with a zero-price scenario emphasizes need and usage rather than transaction, reinforcing a collaborative frame.
  • This method requires extrapolating quantities at intermediate prices rather than observing them directly.
  • In the companion Profit Analytics app, this extrapolation is handled automatically, allowing entrepreneurs to focus on design rather than mechanics.

This approach is often less biased and more cognitively realistic, but it relies on the analyst to be comfortable treating demand as inferred rather than directly reported at every price.

Choosing Between the Two

There is no universally correct choice.

The first approach favors directness and familiarity at the cost of respondent burden.
The second favors engagement and realism at the cost of requiring extrapolation.

What matters is not which approach is “better” in the abstract, but whether the data generated is:

  • interpretable,
  • consistent with the defined unit and period,
  • and suitable for the analysis that follows.

Whichever approach is used, the goal remains the same: to gather quantity evidence that reflects how customers actually think about consumption, not just how surveys are easiest to administer.

If you are using the Profit Analytics app, these are the two supported data structures for how-many demand.

A Final Reminder

A single willingness-to-pay question is sufficient only when demand is yes/no.

When quantity varies, it must be learned deliberately.

How-many demand requires more structure—not more complexity, but more care.

See the problem framing toolkit for demand-type guidance and later toolkits for transformation approaches.

Customer Characteristics

Purpose
Enable interpretation of variation in responses and refinement of the target customer definition.

Differences in willingness to pay are often systematic rather than random.

What to Include

  • Characteristics plausibly related to demand or valuation
  • Information that helps distinguish high- and low-value segments

Design Notes

  • Collect only what you expect to use.
  • Avoid demographic fishing.
  • These questions should come last to avoid priming earlier responses.

Interpreting the Results (A Caution)

Completing a well-designed survey does not mean demand has been “measured.”

It means you have gathered structured evidence that can be analyzed, stress-tested, and challenged.

In the next toolkit, we turn to ways of validating demand evidence and identifying when results are robust enough to inform real decisions—or fragile enough to require redesign.