2. Scoring method
3. Column mapping
Choose the structural columns, then select which columns are attributes.
Rows count as chosen when the mapped chosen column is greater than or equal to this value.
Select an attribute or level to rename it. Drag a level to reorder it.
Conjoint analysis overview
Part-worth utilities
Attribute importance
Levels and scores
| Attribute | Level | Utility | Zero-centered utility | 95% CI lower | 95% CI upper | Importance % |
|---|
2. Scoring method
3. Column mapping
Map the structural fields used by the MaxDiff scorer.
Best and worst flags count as selected when the value is greater than or equal to this threshold.
Model summary
Item scores
Best vs worst percentages
Items and scores
| Item | Appearances | Best selection rate (%) | Worst selection rate (%) | Descriptive best − worst (%) | Best-minus-worst score (%) |
|---|
Preference simulator
Build competing profiles and estimate their share of preference using a completed choice-based conjoint Hierarchical Bayes model. Results describe stated preference within the tested offer space—not expected market share or sales.
Scenarios and profiles
Every product profile must use one tested level from each attribute.
Understanding Hierarchical Bayes (HB) estimates
HB turns repeated choices into estimates of which product features people prefer. It combines each respondent’s choices with the overall pattern across the sample, producing more stable results when each person answers only a limited number of tasks.
The 30-second explanation
The model reviews which profile was selected in every task and which alternatives were rejected.
It estimates a preference pattern for each respondent using all of that person’s tasks.
Clear individual signals remain distinctive; limited or noisy signals are pulled toward the population pattern.
Tradeoffs.app averages the retained population estimates across the independent chains to produce the displayed part-worths.
Why this helps: estimating each respondent independently would be unstable with only a few tasks. Estimating one aggregate model would ignore meaningful differences between people. HB balances both sources of information.
How to read the results
A relative preference score for a level. Higher is more preferred within the study; lower is less preferred. The distance between levels matters more than the absolute number.
Utilities are shifted so the levels within each attribute average to zero. Positive means above the attribute average and negative means below it. Zero does not mean “no value.”
The utility range within an attribute as a share of all attribute ranges. It shows differentiation across the tested levels—not a universal percentage of the real-world decision.
A separate opt-out constant after accounting for the profiles shown. It is not an attribute level and is excluded from zero-centering and importance.
What the model accounts for
Included in the model
- All mapped attributes and levels, evaluated together
- Competition among the alternatives in each task
- Repeated tasks completed by the same respondent
- Differences in preferences across respondents
- A separate tendency to select None when present
Not automatically included
- Demographics, segments, or other respondent characteristics
- Interactions such as “the bonus matters more at a high price”
- Task order, fatigue, learning, or survey-speed effects
- Differences in response consistency or choice scale
- Awareness, availability, competitive context, and market execution
The current CBC model estimates main effects only. All non-None attributes are treated as categorical levels; it does not impose a linear price curve or a monotonic relationship.
What the HB settings control
Most settings change calculation reliability and runtime, not the underlying business question.
| Setting | Business meaning | Practical guidance |
|---|---|---|
| Chains | Independent attempts to reach the same answer | Use four for reporting. One chain cannot assess chain agreement. |
| Iterations | Retained estimates per chain | More draws improve precision only when the sampler is mixing adequately. |
| Burn-in | Early estimates discarded while the model settles | The default is a reasonable starting value; more is not automatically better. |
| Population covariance | Whether preference differences may be correlated | Diagonal is the stable default. Full covariance needs more choice tasks per respondent and stronger diagnostics. |
| Thinning | Keeps every nth estimate | Keep every draw unless memory is constrained. |
| Proposal controls | How the calculation explores plausible answers | Keep the calibrated defaults unless diagnostics identify a sampling problem. |
Model-health checks in plain English
Green model health means the calculation is stable. It does not guarantee that the sample, experiment, or business assumptions are valid.
Limitations to include in reporting
- Stated preference is not purchase behavior. Results describe choices in the study context.
- Utilities are relative. They should not be compared directly across different studies or samples.
- Importance depends on the tested range. Changing the included levels can change importance.
- Main effects can miss combinations. The model does not estimate attribute interactions.
- None is context-sensitive. Wording, realism, category involvement, and offer quality can alter opt-out behavior.
- Convergence is necessary, not sufficient. A stable model can still be based on weak or incorrectly mapped data.
- Current results emphasize averages. Respondent preferences are estimated internally, but the Conjoint results display the population-average part-worths.
- Uncertainty is not shown per level. The current utility table reports posterior means rather than level-level credible intervals.
- Validation is in-sample unless holdouts exist. RLH alone does not establish out-of-sample predictive accuracy.
- The sample defines the market estimate. Unrepresentative recruiting or weighting gaps carry into the results.
Before presenting the findings
- 1.Confirm column mapping, None detection, chosen-value rules, attributes, and levels.
- 2.Review pooled exposure, the six-appearances-per-level HB diagnostic, pairwise balance, within-task overlap, and matrix rank.
- 3.Check respondent count, completed tasks, exclusions made before upload, and sample representativeness.
- 4.Require acceptable chain agreement, effective sample sizes, acceptance behavior, and simulation error.
- 5.Look for implausible or contradictory utilities that could signal coding, design, or respondent-quality issues.
- 6.Frame conclusions within the tested attributes, levels, sample, and choice-task context.
“Within the tested range, respondents showed a stronger relative preference for X than Y.”
“X will cause a specific increase in sales” unless separate market evidence supports that claim.