Pricing Strategy Research for New Businesses – Willingness-to-Pay Studies and Model Testing

Launch with confidence. Price for profit. Validate customer demand before you scale.

Setting the right price is one of the most powerful levers for a new business. It affects customer acquisition, margins, positioning, and long-term viability. At Research Bureau we combine proven willingness-to-pay (WTP) methodologies with rigorous model testing to deliver a pricing strategy that’s both defensible and revenue-maximising. Our work is designed specifically for startups and entrepreneurs who need practical, actionable insights fast.

Why invest in pricing strategy research now?

Every pricing decision carries risk: underpricing leaves money on the table; overpricing reduces adoption and growth. For new businesses, that risk is amplified by limited data, unknown demand curves, and constrained budgets. A structured WTP study and model test transforms guesswork into quantifiable insights.

Reduce launch risk by estimating realistic demand at different price points.
Maximise early revenue by identifying willingness-to-pay segments.
Inform product positioning and feature prioritisation tied to price sensitivity.
Build investor confidence with data-driven pricing assumptions.

What we deliver — outcomes that drive decisions

We translate complex research into immediate actions you can implement.

Validated price ranges with high-confidence lower/upper bounds.
Segment-level WTP distributions to support tiered pricing and bundling.
Elasticity estimates to model volume vs revenue trade-offs.
Revenue-maximisation scenarios under multiple launch strategies.
A/B and market-test plans ready for execution to confirm recommended prices in-market.
Clear, executive-ready deliverables including slide decks, tables, and model files.

Our end-to-end approach

We follow a proven six-step process tailored to startups and early-stage ventures.

Discovery & framing — Define target users, value metrics, and pricing objectives.
Method selection & design — Choose WTP technique(s) and experimental design.
Survey & experiment fieldwork — Recruit representative respondents and run tests.
Data cleaning & modelling — Estimate WTP distributions and demand elasticity.
Scenario simulation & optimisation — Produce revenue forecasts across strategies.
Action plan & testing roadmap — Recommend and prioritise market tests.

Each step includes stakeholder checkpoints so you stay in control and aligned with business goals.

Which WTP methods we use (and when to use them)

We select methods based on your product type, customer sophistication, and budget. Below is a concise comparison.

Method	Best for	Pros	Cons
Van Westendorp Price Sensitivity Meter	Simple consumer goods, quick estimates	Fast, intuitive price range; low cognitive load for respondents	Doesn’t model choice trade-offs or elasticity precisely
Gabor–Granger	Direct purchase intent at price points	Produces demand curve by price; easy to interpret	Requires preselected price grid; less accurate for feature bundles
Conjoint / Discrete Choice Experiments (DCE)	Complex products, bundles, features-based pricing	Models trade-offs; supports optimal feature–price combinations; can segment WTP	More complex design and analysis; higher sample sizes
Auction & Incentivised Bidding	High-stakes launches, enterprise pricing	Reveals real willingness when incentives are real	Operationally intensive; not always practical for startups
Experimental Price Testing (A/B tests)	Live-market validation	Real behavioural data; highest external validity	Requires traffic and infrastructure; potential revenue risk
Anchoring & Framing Tests	To test messaging impact on price perception	Cheap, fast; identifies how framing affects WTP	Not a substitute for actual demand estimation

H3: Van Westendorp — fast and intuitive

Van Westendorp asks four price questions (cheap, expensive, too cheap, too expensive) and produces a price range where perceived value is highest.

Best when you need a rapid directional estimate.
Deliverable: acceptable price range, indifference price, and visual price sensitivity curve.
Limitation: less precise for choice-based decisions or B2B offerings.

H3: Gabor–Granger — straightforward demand curves

Gabor–Granger presents respondents with a price and asks purchase likelihood, repeated across price points.

Best when you want a simple demand curve and revenue optimisation.
Deliverable: purchase probability by price point, break-even and revenue-maximising price.
Limitation: grid must cover plausible prices; cognitive load if too many points.

H3: Conjoint / DCE — deep product-level insights

Conjoint or discrete choice experiments mimic real-world trade-offs by presenting alternatives with differing attributes and prices.

Best when features, bundles, and tiering matter.
Deliverable: utility values for attributes, WTP for features, optimal bundle configuration.
Advanced techniques: Hierarchical Bayes (HB) estimation, mixed logit to capture heterogeneity.
Limitation: requires careful design and larger samples to estimate complex models.

H3: Experimental & Field Tests — real behaviour

When possible, we recommend live A/B tests or limited-market rollouts to confirm survey-based findings with behavioural data.

Best when you can route traffic or run a pilot.
Deliverable: conversion lift, revenue per visitor, and validated elasticities.
Limitation: needs conversion funnel infrastructure and traffic.

Model testing & validation — statistical rigor for decisions

Turning WTP data into a pricing policy requires models you can trust. We apply a suite of statistical and econometric tests to validate assumptions and forecast outcomes.

Demand modelling: logistic regression on purchase intents, price elasticity estimation, and non-linear demand curve fits.
Choice modelling: multinomial logit (MNL), mixed logit, and latent class models to capture segments.
Bayesian hierarchical models: estimate individual-level preferences while pooling information across respondents to improve inference for small samples.
Elasticity analysis: point and arc elasticity calculations, with confidence intervals and sensitivity testing.
Model validation: cross-validation, holdout sets, and posterior predictive checks to detect overfitting.

We quantify uncertainty for every recommendation. You receive point estimates plus credible intervals and scenario-level risk assessments.

Sampling, recruitment, and power considerations

Sampling strategy drives the reliability of pricing insights. Our approach balances cost and statistical power with startup constraints.

Representative panels: use reputable online panels for broad consumer segments.
Custom recruitment: targeted recruitment for niche or B2B buyers via outreach and partner networks.
Sample sizes: rule-of-thumb guidance
- Van Westendorp / Gabor–Granger: 200–400 respondents for broad consumer segments.
- Conjoint / DCE: 300–1,000 depending on attributes, levels, and segmentation needs.
- B2B / enterprise: 50–200 qualified buyers can be sufficient when combined with qualitative interviews.
Power calculations: we conduct power analysis aligned with your smallest meaningful effect size and acceptable Type I/II error rates.

We recommend mixing quantitative WTP work with qual interviews to contextualise results and discover value drivers.

Segmentation and price discrimination

Pricing rarely fits all customers. Identifying segments with distinct WTP supports tiering, bundling, and targeted offers.

Behavioural segments: price-sensitive vs value-focused users.
Demographic & firmographic segments: age, income, company size, industry.
Value-based segments: based on use-case intensity, feature importance, or benefits derived.

We estimate segment-specific WTP distributions using clustering on choice-model coefficients or latent class analysis. This enables practical segmentation strategies such as freemium conversion rates, premium tiers, and enterprise negotiation windows.

Revenue simulation & optimization

We don't stop at WTP estimates. We simulate real business outcomes so you can choose the best path forward.

Revenue vs volume scenarios: model outcomes under low-price/high-volume and high-price/low-volume strategies.
Sensitivity analysis: test how small shifts in price affect churn, lifetime value (LTV), and CAC payback.
Bundling & add-on optimization: identify which features to include/exclude in bundles to maximise ARPU.
Promotional strategies: short-term discounts vs introductory offers and their long-term effects on perception.

All scenarios come with clear KPIs and action steps to implement testable hypotheses.

Deliverables — clear, actionable, and investor-ready

We package research into formats that support quick decisions and stakeholder buy-in.

Executive summary with headline pricing recommendation and confidence levels.
Full technical appendix detailing methods, questionnaires, and model specifications.
Excel/Python/R model files for scenario re-runs and further experimentation.
Slide deck for investors or leadership summarising insights and go-to-market plan.
Testing roadmap with prioritized experiments, timelines, and success metrics.

Every deliverable is written for non-technical stakeholders and includes an optional workshop to walk through findings.

Typical timeline & milestones

We design projects to match startup timelines — from quick sprints to full programmes.

Rapid estimate (Van Westendorp or Gabor–Granger): 2–3 weeks from kickoff to delivery.
Conjoint/DCE with segmentation and advanced modelling: 4–8 weeks.
Field A/B test design and pilot: 6–12 weeks depending on setup and traffic.
Full pricing programme (research + pilot + optimisation): 8–16 weeks.

Timelines assume timely stakeholder inputs and recruitment windows. We offer accelerated delivery for time-sensitive launches.

Pricing & engagement models

We tailor engagements by scope. Below are sample packages to help you choose.

Package	Scope	Typical startup budget (indicative)
Quick WTP Sprint	Van Westendorp or Gabor–Granger, up to 300 respondents, executive summary	$4k–$8k
Conjoint Deep-Dive	DCE design, 500–800 respondents, HB estimation, segmentation	$12k–$30k
Pricing Strategy Programme	Mixed methods, model testing, pilot design, A/B testing roadmap	$20k–$60k
Custom Enterprise	B2B recruitment, in-depth interviews, incentivised tests	Quote on request

Final cost depends on sample size, recruitment complexity, panel costs, and advanced modelling needs. Share your project details and we’ll provide a tailored quote.

Real examples — anonymised case studies

Below are condensed case studies illustrating outcomes we consistently deliver.

Case A — Consumer subscription launch
A direct-to-consumer software startup needed a subscription price for early adopters. We ran a Gabor–Granger and a follow-up A/B test. The WTP suggested a higher price than planned, but with a different feature bundle. The A/B test validated a 20% higher conversion LTV and improved retention, resulting in a projected 35% revenue uplift in year one.

Case B — Tiered B2B SaaS pricing
An early-stage SaaS founder wanted to establish three tiers. We used a conjoint experiment with latent class analysis. Results identified two primary segments and optimal feature–price combinations. The recommended tiers increased the average revenue per account by 28% in simulations and informed a pilot rollout plan that was implemented with early customers.

Case C — Physical product launch with anchoring tests
A hardware startup used Van Westendorp and anchoring experiments to test positioning. The research revealed that premium framing increased acceptable price ceilings, allowing a higher MSRP without erosion in demand. The company launched with a premium channel strategy and exceeded first-quarter revenue targets.

How to start — what we need from you

To give an accurate quote and timeline, tell us:

Short description of product/service and target market.
Target customer profiles or ideal buyer personas.
Launch timeline and business objectives (e.g., maximize revenue vs maximize adoption).
Any pricing hypotheses you already have.
Expected sample constraints or recruitment challenges (e.g., niche B2B buyers).

Share details via our contact form, click the WhatsApp icon on this page, or email us at info@researchbureau.co.za. We typically reply within one business day to schedule a scoping call.

Frequently asked questions

Q: Which method is best for a new product with limited traffic?
A: Start with survey-based WTP (Van Westendorp or Gabor–Granger) for a quick directional read, and pair with qualitative interviews. If feature trade-offs matter, a conjoint provides deeper insight. We recommend a staged approach that culminates in an A/B pilot when traffic permits.

Q: How accurate are survey-based WTP estimates?
A: Surveys provide informative priors but carry hypothetical bias. We mitigate this by using choice-based methods, calibrated scenarios, and, where feasible, small behavioural pilots to validate survey results.

Q: Can you help with pricing for B2B / enterprise products?
A: Yes. B2B pricing requires different recruitment and often incentives. We combine in-depth interviews, conjoint experiments adapted for B2B, and revenue modelling. Sample sizes are smaller but paired with richer qualitative input.

Q: Do you provide legal or tax advice on pricing?
A: No. We provide research and strategic recommendations. For legal or tax implications, consult your legal or financial advisors.

Q: What if our sample size is small?
A: We use Bayesian pooling, expert priors, and mixed-methods approaches to increase inference quality with limited samples, and recommend iterative testing as you grow.

Why Research Bureau?

We specialise in research for startups and entrepreneurs — not enterprise research that misses the speed and budget constraints of early-stage businesses. Our team combines market research design expertise, econometric modelling, and startup operational experience.

Startup-focused: Deliverables crafted for rapid decision-making and investor conversations.
Methodologically rigorous: Advanced models with transparent assumptions and reproducible code.
Practical orientation: Recommendations framed as testable growth experiments.
Transparent pricing: Clear engagement options and variable cost structures based on need.

We work with founders, product teams, and investors to ensure pricing strategies are realistic and actionable.

Ready to act — next steps

Getting a data-informed price is easier than you think. Start with one simple action:

Fill out the contact form on this page with a few key details to get a tailored quote.
Click the WhatsApp icon to speak to a specialist immediately for a quick scope call.
Email us at info@researchbureau.co.za with your project summary and timeline.

We’ll respond within one business day and propose next steps, which typically include a short scoping call and a written proposal.

Appendix: Technical notes (for data-savvy founders)

Hierarchical Bayes (HB): We use HB to estimate individual-level utilities in conjoint studies when sample sizes per respondent are limited. This stabilises estimates by borrowing strength across respondents.
Mixed Logit: We prefer mixed logit for choice modelling where taste heterogeneity matters. It permits random coefficients and more flexible substitution patterns than MNL.
Elasticity estimation: We compute point elasticities using log-log regression and arc elasticities for discrete changes; confidence intervals are derived via bootstrap.
Power analysis: We simulate survey datasets under assumed effect sizes to determine minimum sample sizes for desired precision.
Software & reproducibility: Analyses are reproducible via R or Python scripts; we can deliver code and data for in-house review.

Final note

Pricing is both science and strategy. Our role is to minimise the science uncertainty so you can act strategically with confidence. Whether you need a quick directional read or an in-depth pricing programme with model testing and pilots, Research Bureau is set up to deliver fast, actionable, and investor-ready pricing insights.

Contact us today via the contact form, WhatsApp icon, or email info@researchbureau.co.za to discuss your pricing challenge and receive a tailored quote.