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Fit a discrete-choice model from survey responses, rank what drives decisions, and convert results into dollar willingness-to-pay by feature and segment. This example has been tested and validated with Claude.

When to use this

You need a defensible dollar figure attached to a specific feature (not just a popularity ranking) or you’re pricing a product with several moving parts. Conjoint analysis shows what people actually trade off when choosing between whole configurations, which a preference survey can’t.
This guide covers choice-based conjoint (CBC), the variant most survey platforms default to. Two other flavors exist — adaptive conjoint and menu-based conjoint — but are not covered here.
For the full walkthrough, including a side-by-side comparison of plain vs. engineered attempts, the exact bugs that replicated across two independent runs, and worked WTP numbers by segment, see the full conjoint analysis guide.

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Prompt

Replace the bracketed placeholders with your own attribute names, filename, and threshold. Every column name must match your file’s literal header row, not a spreadsheet column letter.

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Setup

Code execution must be enabled for step 4’s verification to run. It’s on by default for Team and Enterprise accounts. Free, Pro, and Max users: enable it under Settings > Capabilities.
If your survey data lives in Sprig, skip the export, the Sprig MCP connects live surveys and responses directly into Claude so the analysis runs on current data.

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How it works

1

Defensive data loading

Checks that “None,” “NA,” and similar strings aren’t silently read as missing values, a default CSV behavior that dropped a third of a 600-respondent dataset in plain-prompt testing. Reports row count before and after to surface any loss.
2

Proper discrete-choice model

Fits a conditional/multinomial logit model with explicit, stated reference levels rather than alphabetical defaults. Models each choice task’s options against each other as a set ( not as independent yes/no rows) and includes any “none of these” option.
3

Scaled importance ranking

Calculates each attribute’s importance as its utility range ÷ total range, in code at full precision. Raw coefficient size is never used for ranking. In plain-prompt testing, skipping this step ranked price (the biggest real driver of choice) dead last.
4

Segment size check

Calculates the minimum respondent floor for your design before running any segment model. Segments below the floor are flagged, excluded from their own estimate, and kept only in the pooled model.
5

Price coefficient check

Confirms the price coefficient is negative and non-zero before converting anything to dollars. A positive price coefficient means the model is misspecified; the prompt stops and reports it rather than continuing.
6

Significance test on stated-preference gap

If your survey includes a stated-preference calibration question, the prompt compares model-implied WTP to what respondents said they’d pay and tests whether the gap is real. Results that don’t clear the bar (gap ÷ combined SE ≥ 1.96) are reported as directional.
7

Independent verification

Every number is recomputed through a genuinely different implementation before it’s reported. Any mismatch is flagged rather than silently corrected.
8

Output

A verified importance table, a WTP table with confidence intervals, a grouped bar chart by attribute and segment, and a 2–3 sentence summary that separates confirmed findings from directional ones.
9

Output (Continued)

A verified importance table, a WTP table with confidence intervals, a grouped bar chart by attribute and segment, and a 2–3 sentence summary that separates confirmed findings from directional ones.
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Output (Continued)

A verified importance table, a WTP table with confidence intervals, a grouped bar chart by attribute and segment, and a 2–3 sentence summary that separates confirmed findings from directional ones.

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Things to check before trusting any conjoint output

  • Assume alphabetical reference levels until proven otherwise. Both pandas.get_dummies and statsmodels’ formula interface default to alphabetical order with no warning. The same backwards reference levels appeared in two independently-written plain-prompt attempts.
  • Verify your row count after loading. If it’s lower than expected, a valid category label was probably read as missing. Check what the default CSV reader did with “None” or “NA” in your columns.
  • A negative price coefficient is a prerequisite, not an assumption. If price came out positive or near zero, the model isn’t usable for WTP conversion, the prompt requires this check before proceeding.
  • Check segment n before splitting. More segments on a fixed sample means smaller cells and wider intervals. Flag and exclude any segment below your design floor rather than reporting a noisy estimate as a finding.