Conjoint analysis: choose a feasible product and price together
Compare product and price configurations with a transparent bottle choice worksheet. Inspect the design, response source, validation and economics behind a real study.
Conjoint analysis helps you choose a complete product and price by asking buyers to choose among planned variations of the offer. It estimates each feature’s contribution to preference within that context. The bottle worksheet below lets you follow how a utility, the model’s preference score for an offer, becomes a share of preference. Its coefficients are assumed inputs, not estimates of customer preferences or forecasts of market share.
Define the product decisions before the model
Imagine you are developing a reusable bottle for desk use and commuting. You need a feasible capacity, lid and shelf price. Keep the buying occasion fixed while comparing the alternatives, so the result tells you which configuration to take into a product trial.
Attribute | Offer details |
|---|---|
Capacity | Levels in the example: 350 ml; 500 ml Design consideration: Both must be commercially feasible |
Lid | Levels in the example: Standard; travel lid Design consideration: Describe actual function, not an unverified performance guarantee |
Price | Levels in the example: $20; $25; $30 Design consideration: Same currency, unit and channel |
The bottle team can change capacity, lid and shelf price. Those are meaningful dimensions because buyers can recognize the levels and the business can deliver them. “Premium, sustainable and easy-clean” would bundle several mechanisms into one attribute and leave the recommendation unclear about which change matters.
Keep the purchase situation constant. A bottle bought for a commute may face different alternatives from one used at a desk. If channel, tax or delivery changes with price, it becomes part of the offer. A conjoint price coefficient should not be asked to explain an unrecorded channel difference.
Use levels that span the credible decision space. Too narrow a range can miss the tradeoff the team needs; implausible extremes can produce choices unrelated to the intended launch. The tested range also bounds interpretation. A model learned from $20–$30 offers has no automatic evidence for a $60 version.
Restrictions deserve a product reason. A physically impossible lid/capacity pair can be removed, but too many restrictions can make effects travel together. If the travel lid always appears only on the larger bottle, the study needs to explain how it can distinguish their contributions. Ask the designer to show the information supplied by the planned variations.
Make the task resemble a decision people can answer
This example uses choice-based conjoint: respondents select among complete offers. It is a discrete-choice task. If a proposal contrasts conjoint with “discrete choice,” ask which task, planned variations and analysis it means; the labels alone do not specify different studies.
Offer feature | Offer details |
|---|---|
Capacity | Offer A: 350 ml Offer B: 500 ml Offer C: 500 ml |
Lid | Offer A: Standard Offer B: Travel Offer C: Standard |
Price | Offer A: $20 Offer B: $30 Offer C: $25 |
Ask which offer the person would choose on a relevant next purchase occasion. Include “none of these” when rejection is realistic. Forced choice records the least unsuitable offer even if the person would buy nothing, so the presence or absence of none is part of the inference.
The A/B/C comparison is one task with no response selected. It is not enough to fit a conjoint model; you need planned variation across tasks.
Show enough product information to support the choice, with similar visual finish and explanation across offers. The travel lid should describe an actual function rather than assert unverified leakproof performance. If one card supplies a photograph and another only a phrase, presentation becomes a confound unless it is deliberately part of the study.
One attractive-looking task is not the design. Capacity, lid and price need planned variation across tasks and respondents so their effects can be estimated. The single A/B/C task here cannot recover every coefficient: several product differences occur together. Sawtooth’s choice-based conjoint guidance describes the attribute/level framework.
Pilot the whole sequence. People may understand the first task but lose attention after repeated similar offers. Ask where distinctions become difficult and inspect position-based answering. The useful design balances the comparisons required for estimation with a task the intended audience can sustain.
Trace each offer into its utility and share
Use the bottle choice worksheet below to reproduce the comparison in a spreadsheet. Every assumed coefficient is visible. The worksheet does not estimate a model or contain respondent data.
The worksheet uses the same capacity, lid and price attributes as the task above. The coefficients are assumed rather than fitted: offer constant 2; 500 ml versus 350 ml adds 0.5; travel versus standard lid adds 1; price contributes −0.1 per dollar. None has utility 0. This connection lets you trace each card into the calculation.
Task alternative | Offer details |
|---|---|
A: 350 ml, standard lid, $20 | Assumed utility calculation: 2+0+0−.1×20 Utility: 0 Illustrative share: 21.5% |
B: 500 ml, travel lid, $30 | Assumed utility calculation: 2+.5+1−.1×30 Utility: 0.5 Illustrative share: 35.5% |
C: 500 ml, standard lid, $25 | Assumed utility calculation: 2+.5+0−.1×25 Utility: 0 Illustrative share: 21.5% |
None | Assumed utility calculation: Fixed reference Utility: 0 Illustrative share: 21.5% |
The logit calculation is exp(utility)/sum(exp(all utilities)). Lower only B’s price from $30 to $25 and its utility rises to 1: the illustrative shares become 17.5% A, 47.5% B, 17.5% C and 17.5% none. The utility difference follows from the explicit price coefficient; it is not measured willingness to pay. Inspect the worksheet’s assumed cards and coefficients before changing prices.
In a real study, a designed set of tasks and an estimation method would provide utilities with appropriate uncertainty and model checks. The single task and assumed coefficients here cannot perform that estimation. Costs and validation remain necessary before choosing a commercial offer.
Those percentages are illustrative shares of preference, not market shares. They omit awareness, distribution, stock and other market conditions. Sawtooth's simulator guidance explains this important boundary.
Utility is on the model’s preference scale. The zero for A does not mean “no value” in an everyday sense; it is the result of this supplied coding and constant. B’s utility 0.5 is higher than A’s 0, so its exponentiated weight is higher. All alternatives, including none, contribute to the denominator.
The none option matters mathematically as well as conceptually. Its assumed zero utility produces the same displayed share as A and C at the baseline. Changing that assumed reference would change the shares even when the product cards stay fixed. A real study must justify how rejection is represented, rather than treat a convenient none coefficient as established behavior.
Read the numbers as a demonstration of how a model works. The 47.5% result after discounting B is a consequence of a coefficient supplied by the author. The worksheet has no response records from which to infer that real buyers react that way.
Build the bottle choice worksheet
In a blank spreadsheet, put the following tab-separated cells in A1. Each line is a row. Keep the header and four alternatives together; the None row stays in the denominator.
Alternative Capacity_ml Lid Price
A 350 standard 20
B 500 travel 30
C 500 standard 25
None
Add the headers Utility in E1, Weight in F1 and Share in G1. Enter the formulas below. They use a decimal point and comma-separated function arguments; spreadsheet locale settings can use different separators.
Cell or range | Enter or do |
|---|---|
E2 | =2+IF(B2=500,0.5,0)+IF(C2="travel",1,0)-0.1*D2 |
E3:E4 | Fill E2 down through E4 |
E5 | =0 |
F2 | =EXP(E2) |
F3:F5 | Fill F2 down through F5 |
G2 | =F2/SUM($F$2:$F$5) |
G3:G5 | Fill G2 down through G5; format G2:G5 as percentages with one decimal place |
The baseline reproduces 21.5%, 35.5%, 21.5% and 21.5%. Change only D3 from 30 to 25: the worksheet recalculates 17.5%, 47.5%, 17.5% and 17.5%. Restore D3 to 30 before trying another change. Copy the sheet for each scenario and record which input changed, so the recommendation can be traced back to the exact alternatives.
These formulas supply the same assumed offer constant, capacity effect, lid effect, linear price effect and fixed None reference as the example. Changing the capacity or lid coding requires updating the formula deliberately. Keep price comparisons within the credible $20–$30 study range; a spreadsheet will calculate outside it without supplying evidence there.
Understand the tradeoff without inventing evidence
The assumed travel-lid contribution is 1 utility unit and the price coefficient is −0.1 per dollar. Holding the rest fixed, a $10 increase offsets that utility addition in the supplied arithmetic. This demonstrates a marginal tradeoff under a constant linear price term. It is not a measured $10 willingness-to-pay premium for the lid.
The distinction matters when an analyst reports a dollar translation. Ask whether price was modeled linearly, whether the tradeoff is within the tested range and how uncertainty or segment variation affects it. A dollar figure can be convenient while concealing the assumptions that created it.
Shares are also relative to the displayed set. The $25 B scenario competes against A at $20, C at $25 and none. If C is removed or a new competitor added, the denominator and question change. A larger share after removing a rival does not establish that buyers value B more.
For a real estimated study, describe utilities, simulated preference shares and market outcomes separately. Awareness, distribution, stock, promotion and purchase friction lie outside the simple choice calculation. A calibrated market model may introduce additional evidence; this worksheet adds none.
Do not transfer a utility coefficient from one attribute coding or product context into another. The constant, reference levels and price units form one model. Reading an isolated coefficient without those definitions can reverse or magnify its apparent meaning.
Read effect size in the level range actually tested
A utility contribution describes a level relative to its coding reference. In the supplied model, moving from 350 ml to 500 ml adds 0.5 and moving from standard to travel lid adds 1. These differences are interpretable within the supplied model. The model’s arbitrary reference zero is not an absolute measure of whether the product has value.
A simple range summary illustrates why attribute importance depends on the levels. Capacity spans 0.5 utility, lid spans 1 and price spans 1 across $20–$30. Dividing those ranges by their total 2.5 gives 20%, 40% and 40%. This is arithmetic from assumed coefficients, not estimated customer importance. It compares the ranges we chose to test.
Now narrow the price range to $20–$25 without changing the coefficient. Its utility range becomes 0.5; the same range summary becomes 25% capacity, 50% lid and 25% price. Buyers did not change. The design range changed. This is why a reported importance percentage should travel with the attribute levels, rather than become a universal statement that one feature matters twice as much.
In a real study, inspect the coding and price treatment before comparing effect summaries. A nonlinear price effect or different segment sensitivity changes the interpretation. The researcher should explain the ranking that supports the specific configuration decision, with appropriate uncertainty.
See what the logit denominator assumes
The ratio of B’s share to A’s is exp(.5)/exp(0), about 1.65. The common denominator cancels. Removing C changes their individual shares but leaves that ratio unchanged under this formula. That is a property of the assumed calculation, not evidence about how real shoppers respond when an alternative disappears.
This matters when competitors are close substitutes or the choice set changes substantially. A simulator can return a neat answer for a scenario its underlying assumptions do not capture well. Ask whether the relevant new configuration and competitor context were checked against appropriate held-out evidence.
Use simulations as controlled comparisons: save the exact set, change one feasible input, and explain which model assumption drives the result. Do not add a speculative competitor and treat the resulting share as independently validated demand. The worked calculation helps you learn the dependency before a real decision rests on it.
Compare configurations with the commercial constraints
Begin with configurations the team can manufacture and sell. For each candidate, retain the modeled preference under a defined competitive set, applicable cost and channel condition. A larger bottle may appeal while consuming more shelf space or creating a tooling cost. A feature with a high preference contribution may be uneconomical at the required price.
State the objective. A penetration strategy, contribution objective and premium portfolio strategy can choose different offers from the same preference model. The model supplies the tradeoff; the business supplies what it is trying to optimize. A single “highest share” badge collapses those decisions.
Inspect relevant buyer groups when a different configuration would lead to a different action. Use suitable uncertainty and validation, rather than assume every segment-specific maximum is a reliable tier. More tiers also introduce operations and communication costs the survey did not estimate.
Test the stability of the recommendation under plausible parameter uncertainty and alternatives. Then evaluate prediction on tasks not used to fit the model. Reconstructing training choices is useful for diagnosis, while held-out performance asks whether the model predicts new choices in the relevant task.
The next action could be one prototype and a paid test at the intended shelf price. If every winning configuration requires an infeasible discount, the completed recommendation is to revise the product or economics. Both outcomes are more useful than a scenario comparison whose largest percentage has no owner.
Synthetic conjoint analysis: inspect the response source and fit
A conjoint study can use responses from people or a modeled population. The choice task may look the same, but the response source changes what you need to evaluate. Inspect how the intended buyers are represented, how their choices were generated or collected, and how those choices become the fitted model.
For a synthetic study, request the audience grounding and the actual choice records used for estimation, with their tasks and response bases. Then inspect the estimation approach and a check on choices that were kept out of fitting. A table of plausible coefficients does not establish that a model was estimated from those choices. The assumed coefficients in this bottle example teach the arithmetic; a commissioned study needs its own record.
Ask the supplier to show one complete chain: task card, eligible response, fitted output, configuration comparison and proposed action. Keep the no-choice treatment and feasible combinations visible. If the suggested travel-lid bottle wins only after a price reduction, take that configuration into the relevant cost comparison before recommending it.
Our task-level validation comparisons illustrate why evidence should stay tied to a particular question and measure. Those results concern the displayed tests; evaluating a new conjoint specification requires evidence relevant to its population, design and intended recommendation.
The delivered study should let your team inspect which choice data produced the estimate, whether the recommendation survives plausible uncertainty, and what would change when the alternatives or prices change. Report fitted preferences, within-set scenario shares and any separately supported market prediction in their own terms.
Know what should be in the delivered study
Request the audience definition, full tasks, attribute coding, restrictions, quality rules, estimation approach and held-out checks. The simulator should preserve the exact scenario and assumptions behind a result, so another reviewer can reproduce the chosen configuration.
Ask for a report example that reconciles product, price and feasibility. It should name what to develop, why it wins under the stated objective, what assumption matters most and which product or market evidence remains necessary. A model can produce precise decimals without resolving those questions.
For synthetic choices, examine task-relevant grounding and independent validation. A statistical estimation procedure does not establish that the modeled population represents your buyer. Keep that source label on both the utilities and the recommendation.
Sapien can compare modeled complete offers and explain buyer-group differences. Confirm the required conjoint design and analysis in scope; choose the study design and validation needed for your configuration decision. If the task is only benefit priority, our MaxDiff and conjoint guide is the more direct starting point.
Further reading
If you are choosing a price or bundle, start with our pricing research guide. It helps separate the commercial choice from the technique used to investigate it.
Bring the product decision, feasible features and price options, intended buyers and constraints to a study-scoping conversation with Sapien. Agree whether the task calls for a conjoint design or a direct comparison of complete offers, and confirm the analysis and validation required before commissioning it.
For a conjoint proposal, ask to inspect a genuine worked study with its tasks, response source, fitted results and recommendation. Use the worksheet here to understand the calculation; evaluate a supplier's actual study to decide whether its evidence supports your configuration choice.
