Lexia Consulting
Discrete Choice Modelling & Market Intelligence
Evidence-based market intelligence from real trade-off behaviour
Most market research asks people what they think. DCM reveals what they actually decide — by showing realistic choices and reading the pattern of behaviour beneath them. The result is pricing, portfolio and placement intelligence grounded in evidence, not opinion.
The research problem
The gap between what people say and what they do.
Traditional market research has three structural weaknesses. DCM is designed to fix all three.
Stated preferences mislead
When asked directly, respondents give socially acceptable or aspirational answers. They say they value sustainability or quality more than their actual purchases reveal.
Group settings distort
In focus groups, dominant voices pull the room. Consensus replaces individual preference. The output reflects who spoke loudest — not what the market will do.
Hypothetical questions get hypothetical answers
Asking "would you buy this at €15?" detaches the decision from the trade-offs shoppers actually face. Without real alternatives and real prices, responses are unreliable.
What we can help you understand
One method. Many strategic questions answered.
The same framework that answers pricing questions can decode a surprisingly wide range of commercial decisions.
Price sensitivity & willingness-to-pay
Measure exactly where customers switch — and what price premium specific attributes, formats or brands can command.
Pack & SKU architecture
Identify which pack sizes and range widths maximise volume without fragmenting demand or cannibalising your own portfolio.
Product attributes & claims
Quantify which features, quality signals or trust claims actually move choice — and which add complexity without adding value.
In-store visibility & placement
Test display formats, shelf positions and planogram arrangements before committing to a rollout.
Competitive switching
Understand which competitors your customers would switch from — and at what price gap, feature change or format shift.
Consumer segmentation
Reveal how preferences and switching thresholds differ across consumer groups — defined by behaviour, not just demographics.
How it works
We map what people choose, not what they claim.
DCM is grounded in Nobel Prize-winning economic theory¹ and validated across hundreds of academic and commercial applications worldwide.
Design choice tasks
Define the attributes and levels to test. Design realistic alternatives that mirror the decisions your customers actually face.
Screen & survey your panel
Recruit genuine buyers of the category. Show each one a series of realistic choice tasks — typically 8 to 12 per respondent.
Model the pattern
Statistical analysis extracts the utility weight each respondent places on each attribute — revealing what drives choice, and by how much.
Forecast & simulate
Predict behaviour under any tested scenario — new price points, different formats, competitor moves — with 95% confidence intervals.
Choice tasks in practice
Realistic decisions, not abstract opinions.
Respondents don't rate or score products — they simply choose. Each task is designed to isolate the effect of specific attributes. Below are two examples of how these tasks can be structured.
Looking at the options below, which product would you be most — and which would you be least — likely to purchase?
| Size | 500 g |
| Format | Standard |
| Origin | Local |
| Size | 1 kg |
| Format | Premium |
| Origin | Local |
| Size | 500 g |
| Format | Premium |
| Origin | Imported |
| Size | 1 kg |
| Format | Standard |
| Origin | Imported |
Looking at the shelf below, which product would you be most — and which would you be least — likely to pick up and explore?
Project structure
Four to six weeks from brief to decision.
We handle design, fieldwork and modelling. You get actionable outputs without needing technical knowledge.
Typically 1–2 weeks
Understand & Design
Align on the business questions and decision criteria. Define attributes, levels and sample size. Design the survey instrument and select the right panel for your category and geography.
Typically 2–3 weeks
Fieldwork
Launch survey to a screened panel of genuine category buyers. Track responses against the sample plan, ensure coverage across key segments, and clean the dataset for analysis.
Typically 1 week
Analysis & Delivery
Estimate attribute utilities and willingness-to-pay. Build scenario forecasts with confidence intervals. Deliver a final report and an interactive Excel scenario planning tool.
What you get
Outputs you can act on immediately.
Every project delivers both concrete analytical outputs and the strategic clarity your team can carry forward.
Analytical outputs
Quantified measure of how much each product feature or claim influences choice — across the total sample and by segment.
Price thresholds for every tested attribute, with 95% confidence intervals. Tells you exactly where value is monetisable — and where it isn't.
Predicted market response across the scenarios you care about — price changes, format changes, competitor moves — with uncertainty ranges throughout.
An Excel-based tool that lets your team model new scenarios without coming back to us every time the commercial question changes.
Strategic outcomes
Move on price with confidence, knowing exactly where the switching thresholds are — across segments, occasions and competitive contexts.
Clear view of which SKUs to keep, which to cut and which to add — backed by evidence of what actually drives consumer choice.
A map of which competitors you take share from — and which take from you — at each price point or format change.
Consumer groups defined by their actual trade-off behaviour — not just age or income — so commercial decisions can be genuinely targeted.
DCM in practice
What DCM has delivered across industries.
Published and publicly documented examples of discrete choice modelling driving real commercial decisions.
Vision Care — New Product Launch
Healthcare & Consumer ProductsWhat happened: A vision care company was evaluating whether to launch a new-to-market product innovation. A DCM study was designed combining consumer and eye care professional preferences — identifying appealing features, recommending a price point, and building a market simulator to forecast competitive share under different scenarios.
DCM used as a capital allocation tool — providing the evidence base for a launch or no-launch decision with statistical confidence rather than category instinct.
Osteoporosis Treatment Preferences
PharmaceuticalsWhat happened: A published DCM study asked patients to choose between hypothetical drug treatments varying in efficacy, side effects, administration mode, frequency and cost. Mixed logit modelling revealed the relative importance of each attribute and patients' willingness to accept trade-offs — providing a statistically grounded brief for product development and pricing strategy.
Patient preferences quantified with full uncertainty ranges. The same approach works equally in B2B settings where buyers rarely disclose their true priorities.
Sustainable Packaging Launch
Consumer Goods / FMCGWhat happened: A consumer goods manufacturer used DCM to test packaging format options before committing to a product line change. The study quantified willingness-to-pay for sustainable materials and identified the formats that would drive switching without requiring a price discount. The company launched the sustainable range and captured an 8% market share increase within six months.
Packaging decisions made with evidence rather than category instinct — reducing launch risk and protecting margin on a category investment.
Public Transport Fare Design
Transport & InfrastructureWhat happened: Transit authorities across multiple markets have used DCM to separate price sensitivity from service quality effects — isolating exactly how ridership responds to fare changes, new routes or service improvements. Studies consistently show that different passenger groups have structurally different price elasticities that aggregate demand data cannot reveal.
The logic applies directly to commercial pricing: segment-level elasticity is where real pricing power hides — and only choice modelling can surface it.
Scheduled Delivery Pricing
E-commerce & LogisticsWhat happened: A DCM-based pricing framework for scheduled delivery services was tested in a live A/B experiment and increased the target performance metric by 19%. The framework moved into full production in Q4 2023 — one of the few publicly disclosed examples of discrete choice modelling directly powering a live commercial pricing engine at scale.
Real-world A/B validation that behavioural choice modelling drives measurable commercial outcomes — not just strategic recommendations.
Ready to replace assumption with evidence?
Tell us what commercial decision you're trying to make. We'll tell you whether DCM is the right tool — and what a project would look like.
Get in touch