Pricing Research Services: Find the Optimal Price Point for Your New Product

Price is not just a number. It is a strategic lever that determines who buys your product, how profitable each sale is, and whether your launch succeeds or stalls. At Research Bureau, our Pricing Research Services combine behavioural science, rigorous econometrics, and practical market experience to identify the price that maximises revenue, profit and market adoption for your new product.

This page explains our approach, the methods we use, real-world examples and exactly what you’ll receive when you commission pricing research with us. Share details so we can give you a tailored quote — use the contact form on this page, click the WhatsApp icon, or email [email protected].

Why professional pricing research matters

Pricing is a strategic decision with measurable downstream effects. Small changes in price can produce large differences in revenue and profitability.

  • Incorrect pricing risks leaving money on the table, reducing margins or constraining growth.
  • Consumer perception and reference prices shape willingness-to-pay in ways that are not obvious from cost-plus thinking.
  • Competitive dynamics, channel mixes and product line interactions make pricing multi-dimensional.

Professional pricing research turns intuition into tested strategy. It answers precise questions such as:

  • What price maximises profit for each target segment?
  • How price-sensitive are different customer segments?
  • What packaging and bundling strategies increase adoption?
  • How will competitors likely react to our price?

Business outcomes we deliver

We structure each project to generate actionable outcomes that business teams can implement immediately.

  • Optimised launch price with projected revenue and profit scenarios.
  • Segment-specific pricing and recommended price tiers for multiple buyer personas.
  • Price architecture (SKU mix, packaging, subscriptions, discounts).
  • Go-to-market price messaging that reduces friction and increases conversions.
  • A/B test plans and monitoring frameworks for rapid post-launch optimisation.

Who this service is for

Our pricing research is built for teams launching new products or re-pricing existing SKUs across B2C and B2B markets.

  • Consumer electronics, FMCG, retail, e-commerce.
  • SaaS, subscription services, and digital products.
  • Industrial products and B2B offerings with complex buying processes.
  • Startups preparing for product-market fit and enterprises re-optimising portfolios.

Our Pricing Research Services — Methods & When to Use Them

We apply a mix of quantitative and qualitative methods. Each method is selected to match your objective, timeline and budget.

Van Westendorp Price Sensitivity Meter (PSM)

Best for quick diagnostic insight into perceived value ranges.

  • What it does: Identifies acceptable price range and an implied optimal price from direct consumer responses to price prompts.
  • When to use: Early-stage validation or when you need fast, low-cost signals.
  • Strengths: Simple, quick, and interpretable.
  • Limitations: Less accurate than choice-based methods for final pricing decisions and sensitive to anchoring.

Gabor-Granger Pricing

Best for estimating demand at discrete price points.

  • What it does: Asks respondents whether they would buy at a set list of prices to generate a demand-by-price curve.
  • When to use: When you have a shortlist of candidate price points.
  • Strengths: Good for straightforward products and small SKU sets.
  • Limitations: Can overstate purchase intent versus real marketplace behaviour.

Choice-Based Conjoint (CBC) / Discrete Choice Experiments (DCE)

Best for realistic trade-off analysis across price and feature bundles.

  • What it does: Presents respondents with choices between product profiles to infer willingness-to-pay for features and optimal combinations.
  • When to use: Complex products where features, price and packaging interact.
  • Strengths: Mimics real buying decisions; supports simulation of many pricing scenarios.
  • Limitations: Requires larger samples and careful experimental design.

Hierarchical Bayes and Mixed Logit Models

Best for segment-level willingness-to-pay and individual-level prediction.

  • What it does: Provides robust individual preference estimates, capturing heterogeneity across customers.
  • When to use: When you need segment-targeted or personalized pricing strategies.
  • Strengths: Accurate estimates of demand elasticity and preference distributions.
  • Limitations: Computationally intensive and needs quality data.

Conjoint + Monte Carlo Profit Simulations

Best for profit-maximisation under uncertainty.

  • What it does: Combines conjoint-derived demand curves with cost, margin and competitive scenarios to simulate revenue and profit across price points.
  • When to use: When you need a bottom-line optimisation, considering costs and cannibalisation.
  • Strengths: Explicit profit optimisation and sensitivity analysis.
  • Limitations: Requires clear cost inputs and assumptions.

A/B and Multivariate Price Testing (Field Experiments)

Best for validation and iterative refinement post-launch.

  • What it does: Tests prices or price messages on live users to measure actual conversion and revenue outcomes.
  • When to use: When you can run controlled experiments in-market (webstores, digital products).
  • Strengths: Real behavioural data; highest external validity.
  • Limitations: Requires operational capability and traffic volume.

Qualitative Pricing Interviews & Focus Groups

Best for uncovering motivations and price messaging.

  • What it does: Explores buyer psychology, reference price anchors and value drivers.
  • When to use: When initial data is needed to design quantitative instruments or to craft price messaging.
  • Strengths: Deep insights to explain “why” behind numbers.
  • Limitations: Not sufficient alone for price optimisation.

Structural Demand Models and Elasticity Estimation

Best for forecasting and scenario planning.

  • What it does: Uses regression and time-series techniques on historical sales (or simulated data) to estimate price elasticity and forecast outcomes.
  • When to use: When you have transactional data and need demand forecasts under a range of price strategies.
  • Strengths: Empirical and data-driven forecasting.
  • Limitations: Requires high-quality historical data.

Bundling, Subscription and Freemium Pricing Research

Best for subscription models and multi-product offerings.

  • What it does: Assesses optimal bundle combinations, price tiers and freemium upgrade triggers.
  • When to use: SaaS, media, and products with cross-sell opportunities.
  • Strengths: Identifies tier thresholds and churn-resilient pricing.
  • Limitations: Requires longitudinal data for churn modelling.

How our techniques compare

Method Best for Typical sample size Time to field Output
Van Westendorp PSM Quick value range 300–800 1–2 weeks Acceptable price range; cheapness/expensiveness curves
Gabor-Granger Demand at discrete prices 400–1,000 2–3 weeks Demand-by-price curve; revenue estimates
Choice-Based Conjoint (CBC) Feature trade-offs & price 800–2,500 3–6 weeks WTP estimates; market share simulations
Hierarchical Bayes / Mixed Logit Segment-level WTP 1,000–5,000 4–8 weeks Individual preference distributions; elasticity
A/B Price Testing Live conversion & revenue Depends on traffic 2–12 weeks Conversion lift; revenue per visitor
Structural Demand Models Forecasting from sales data N/A (transactional) 3–8 weeks Elasticities; scenario forecasts
Qualitative Interviews Messaging and drivers 15–60 2–4 weeks Rich insights; hypothesis generation

Our process — how we deliver reliable pricing decisions

We follow a structured workflow tailored for high confidence and rapid execution.

1. Strategic brief & hypothesis alignment

We start by clarifying your commercial goals, margin constraints, and competitive context. This ensures research focuses on the most valuable decision levers.

2. Research design & method selection

We recommend the optimal method mix—qualitative scoping, quantitative experiments and/or field testing—aligned with your timeline and budget.

3. Questionnaire & experiment programming

We build and pre-test surveys, conjoint designs or A/B tests using best-practice question wording, blocking and randomisation to avoid bias.

4. Fieldwork & data quality control

We recruit representative samples, monitor fielding in real time and apply attention checks and de-duplication to ensure high-quality responses.

5. Advanced analysis & modelling

We estimate demand curves, willingness-to-pay distributions and mixed logit models. We run Monte Carlo simulations and scenario stress tests for profit optimisation.

6. Commercial recommendations & implementation plan

We deliver clear price recommendations, SKU architecture, recommended launch price, discounting rules and a step-by-step implementation playbook.

7. Post-launch testing & monitoring

We support A/B tests, continuous monitoring dashboards and iteration plans to refine price after-market validation.

Technical approach — what our models do for you

We combine robust econometrics with pragmatic business modelling to provide defendable price points.

  • Demand curve estimation: Using experimental choice data (CBC/Gabor-Granger) we estimate how purchase probability changes with price.
  • Elasticity estimation: We quantify price elasticity by segment so you know how volume responds to price moves.
  • Profit optimisation: We combine price-demand functions with cost and margin inputs to compute revenue and profit-maximising prices.
  • Heterogeneity modelling: Hierarchical Bayes and latent class models reveal subgroups with different willingness-to-pay.
  • Scenario & sensitivity analysis: Monte Carlo simulations stress-test price decisions across competition, cost and adoption uncertainties.
  • Cannibalisation modelling: For multi-SKU portfolios we model substitution patterns and the net effect on revenue.

By using these techniques we produce not just a recommended price, but a defensible, data-driven strategy that your leadership and finance teams can trust.

Example: How our analysis drives decisions (anonymised case study)

Client: Consumer electronics startup launching a smart thermostat.

  • Objective: Maximise first-year revenue without reducing initial adoption.
  • Approach: Mixed-method design — qualitative interviews, CBC conjoint and Monte Carlo profit simulations.
  • Key findings:
    • Two distinct segments emerged: price-sensitive adopters (55%) and feature-driven adopters (45%).
    • Willingness-to-pay distribution for the feature-rich SKU had a median of R3,450 and a 90th percentile of R4,600.
    • Simulations showed that a single-price skimming strategy decreased adoption among price-sensitive buyers but marginally improved short-term profit.
  • Recommendation:
    • Launch with a two-tier architecture: Base model at R3,250 and Pro model at R4,499 with a price anchoring message highlighting advanced sensors.
    • Offer a limited launch bundle (thermostat + 12-month support) at a 10% discount to increase perceived value and capture early adopters.
  • Outcome (projected from model): The recommended architecture increased projected first-year revenue by 18% versus a single-price baseline, while preserving a broad initial adoption profile.

This anonymised example demonstrates how combined techniques produce both strategic insight and a practical go-to-market plan.

Deliverables you can expect

We package insights for easy decision-making and fast implementation.

  • Executive summary with recommended launch price(s).
  • Demand and elasticity tables by segment and price point.
  • SKU architecture with suggested price tiers and introductory promotions.
  • Monte Carlo simulation outputs showing revenue/profit trade-offs.
  • Full technical appendix: model diagnostics, sample details, question wording.
  • Implementation playbook: marketing messages, A/B test scripts, monitoring KPIs.
  • Workshop with stakeholders to interpret results and align on next steps.

Sample sizes, timelines and indicative investment

Project timelines and sample sizes depend on method mix and market complexity. Below are typical guidelines and indicative costs (ZAR), provided to help you scope.

  • Starter diagnostic (Van Westendorp or Gabor-Granger)

    • Sample: 400–800 respondents
    • Time: 2–3 weeks
    • Indicative investment: R35,000–R80,000

  • Standard pricing project (Choice-based conjoint + simulations)

    • Sample: 800–2,500 respondents
    • Time: 4–8 weeks
    • Indicative investment: R80,000–R250,000

  • Enterprise & custom projects (HB, large segmentation, field experiments)

    • Sample: 2,500+ respondents or transactional data integration
    • Time: 6–12+ weeks
    • Indicative investment: R250,000+

These figures are indicative only. Exact pricing depends on sample sourcing, panel recruitment complexity, international samples, programming requirements and scope of deliverables. Share your project details to receive a tailored quote.

Frequently asked questions

How accurate are survey-based pricing methods?

Well-designed choice experiments and field tests provide strong predictive power, especially when combined with behavioural calibration and real-world A/B tests. We validate survey outputs using holdouts, back-testing and post-launch monitoring to reduce risk.

What sample sizes do we need?

Sample size depends on the number of segments you want to target and the level of precision required. Simple diagnostics can work with 400–800 respondents. Segmentation or hierarchical models typically require 1,000+ respondents for robust estimates.

Can you model channel-specific pricing?

Yes. We model price sensitivity by channel (retail, e-commerce, direct sales) and recommend channel-specific prices and promotional rules to avoid margin erosion and channel conflict.

Do you account for competitor reactions?

We simulate competitive responses in scenario planning and provide sensitivity analysis. While competitor behaviour is uncertain, we model plausible reactions to help you choose resilient pricing.

How do you avoid biased responses in surveys?

We use randomized designs, attention checks, outlier detection, pre-tests and careful question wording to minimise bias. Our fieldwork protocols include quality monitoring and deduplication.

Will this research replace A/B testing?

No—it's complementary. Pricing research provides an informed starting point and clear hypotheses for A/B tests, which then validate and refine pricing in-market.

Common pricing strategies and when to use them

  • Price skimming: High initial price to capture early adopters. Use when you have unique features and low price elasticity in early segments.
  • Penetration pricing: Low initial price to build market share. Use in highly price-sensitive categories or when network effects are critical.
  • Tiered pricing: Multiple SKUs or subscription tiers to extract value across segments. Use when segments value different feature sets.
  • Value-based pricing: Price based on perceived value rather than cost. Requires strong customer insight and messaging.
  • Dynamic pricing: Prices change in response to demand or inventory. Use with digital channels and when infrastructure supports rapid repricing.
  • Bundling: Combine products to increase perceived value and reduce churn. Use where cross-sell increases lifetime value.

We advise on which strategy aligns with your product-market fit and growth objectives.

Data privacy and ethics

We adhere to strict data protection practices and ethical guidelines. Respondent anonymity, secure data storage and transparent consent are core to our fieldwork. Where required, we comply with local privacy legislation and client data governance policies.

Why Research Bureau?

  • Proven methodology: We combine behavioural science, market research best practice and advanced econometrics.
  • Experienced team: Pricing analysts, econometricians and industry specialists with years of experience across FMCG, tech and B2B sectors.
  • Action-first outcomes: Reports emphasise implementable pricing strategies, not just academic outputs.
  • End-to-end support: From survey design and fieldwork to implementation playbooks and A/B test execution.

We partner with your commercial, product and finance teams to ensure pricing decisions are both defensible and executable.

How to get started — next steps

We make it simple to get a tailored plan that fits your timeline and objectives.

  • Share a brief: Product description, target markets, cost structure (approx.), launch timeline and main objectives via the contact form on this page.
  • Quick discovery call: We’ll schedule a 30-minute call to align on goals and propose a method mix.
  • Detailed proposal & quote: We deliver a written proposal listing scope, timeline, sample plan and firm investment estimate.
  • Project kickoff: Rapid execution with weekly checkpoints and stakeholder workshops.

Click the WhatsApp icon to start a chat now, or email us at [email protected] to request a proposal.

Additional expert insights — practical tips for pricing launches

  • Start with a testable hypothesis: Use research to validate whether you’re targeting price-sensitive or feature-driven buyers.
  • Segment before you price: One price rarely fits all. Segment-level prices or tiers increase capture and reduce churn.
  • Anchor strategically: Use premium SKUs to anchor perceived value and lift willingness-to-pay for mid-tier offerings.
  • Use promotions sparingly: Frequent discounts train customers to wait. Time-limited launch offers can help adoption without long-term damage.
  • Monitor post-launch: Continual measurement and quick A/B tests allow you to iterate price and messaging rapidly.
  • Align stakeholders early: Pricing decisions require cross-functional buy-in—get product, finance and sales aligned before launch.

Common mistakes we help clients avoid

  • Relying on cost-plus pricing without customer validation.
  • Using a single method or small sample for complex products.
  • Over-discounting to chase volume at the expense of margin.
  • Ignoring channel differentials and cannibalisation effects.
  • Launching without a plan for post-launch experimentation.

We design studies to reveal those blind spots and provide mitigation strategies.

Ready to find the optimal price for your product?

Pricing shapes your product’s commercial trajectory. Get a defendable, data-driven price strategy that drives revenue, protects margin and aligns with your growth plan.

  • Share your brief through the contact form on this page.
  • Click the WhatsApp icon to chat instantly.
  • Email: [email protected]

Tell us about your product, target market, preferred timeline and any constraints (cost floors, channel rules, existing SKUs). We’ll respond with a recommended approach and a tailored quote within 48 hours.

Make your pricing decision with confidence — partner with Research Bureau.