Conjoint Question

When and Why to Use

Use this to run a CBC study where each respondent picks one option from a small set of competing product concepts across a sequence of tasks. Ideal for:

• Measuring how attribute levels (brand, price, feature, packaging) drive choice
• Estimating individual-level utility scores via Hierarchical Bayes
• Comparing attribute importance and, with purchase follow-ups, marginal purchase lift

For background on the method, see Choice-Based Conjoint. For the model that produces utility scores and simulated share, see Utility and simulated share methodology.

How Task Sets Work

task_sets is a mapping of task-set id to an ordered list of tasks, where each task is a list of concept ids. Each respondent is assigned one task set using least-fill balancing, which keeps per-task-set exposure roughly even across the fielding. Each task in the assigned set is then presented as a structured comparison with one concept choice.

The simplest way to generate balanced concepts and task sets is the Conjoint Analysis Design Tool, which produces a code block you can paste straight into the survey alongside your screener.

Dict-Backed vs Media-Backed Concepts

Concepts can be defined in two shapes:

• Dict-backed concepts — Python dicts whose concept_id_tag key (default id) identifies the concept; attributes are displayed as ordered comparison rows. required_tags sets the displayed row order and identifies the fields required on every concept and retained for reporting.
• Media-backed concepts — MediaItem objects rendered using the same labels, sizing, and media tags as other media-bearing questions. Use image_label_field, show_image_label, and image_size to configure rendering.

Configuration Options

Option	Type	Required	Default	Description
question	str	yes	-	The question to ask for each conjoint task
concepts	list[dict] or list[MediaItem]	yes	-	Concept dictionaries or media items to use in tasks
task_sets	dict[str, list[list[str]]]	yes	-	Mapping of task-set id to tasks, where each task contains concept ids
concept_id_tag	str	no	'id'	Tag containing the concept id referenced by task_sets
required_tags	list[str] or None	no	None	Concept tags to require and report with selected dict-backed concepts
dont_know_option	str	no	''	Optional uncertainty choice, separate from explicit none
randomize	bool	no	False	Randomize option order within each task
image_label_field	str or None	no	None	Media field to use as the display label for media concepts
show_image_label	bool	no	True	Whether to show media labels
image_size	tuple[int, int] or None	no	None	Bounding box size for media options
number_seconds	int	no	0	Seconds to wait before allowing the respondent to continue
tags	dict[str, Any] or None	no	None	Tags attached to each conjoint task question
id	str or None	no	None	Optional stable identifier for this question
none_option	str	no	''	Completed no-purchase choice beneath the concepts; cannot be combined with anchor_question
anchor_question	str or None	no	None	Yes/no purchase follow-up after each concept choice; supports {selected.field} piping
anchor_labels	dict or None	no	None	Display labels with exactly the keys yes and no

Respondent comparison

On a wide screen, concepts appear side by side with matching attribute rows. The None of these choice sits beneath the concepts when none_option is configured.

On mobile, the concepts stack vertically while retaining each attribute label and the separate None of these choice.

None, Don't Know, and Purchase Follow-ups

Choose the behavior that matches your study:

• Set none_option="None of these" for an explicit no-purchase alternative beneath the concepts.
• Set anchor_question to ask whether the respondent would buy the concept they just selected. The platform asks and saves a separate yes/no follow-up after every concept choice.
• Use dont_know_option="Don't know" only for uncertainty. It is separate from a completed no-purchase choice and skips the purchase follow-up.

Explicit none and purchase follow-ups are mutually exclusive. For a purchase follow-up, omit none_option and pass these arguments in your s.conjoint_question(...) call:

anchor_question="Would you buy {selected.brand} for {selected.price}?",
anchor_labels={"yes": "Yes, I would buy it", "no": "No, I would not"},

{selected.price} and other {selected.field} placeholders insert values from the selected concept dictionary or media tags. Every concept must contain the referenced field. Bare {selected} inserts the concept ID. Do not use a Python f-string: the platform substitutes these values when the respondent chooses a concept.

The answer applies to that choice in that task, even if the same concept appears again. Answering No preserves the forced concept choice and records a negative purchase follow-up; it does not turn the response into Don't know.

The follow-up can also use a simple prompt without placeholders, as in this example:

Example Code

Dict-backed concepts:

from survey import Survey

s = Survey(**globals())

concepts = [
    {"id": "A", "brand": "Apple",   "color": "Black",  "network": "5G", "price": "$800"},
    {"id": "B", "brand": "Apple",   "color": "Blue",   "network": "5G", "price": "$1000"},
    {"id": "C", "brand": "Samsung", "color": "Black",  "network": "4G", "price": "$600"},
    {"id": "D", "brand": "Samsung", "color": "Silver", "network": "5G", "price": "$800"},
]

task_sets = {
    "TS001": [["A", "C"], ["B", "D"], ["A", "D"], ["B", "C"]],
    "TS002": [["A", "B"], ["C", "D"], ["A", "C"], ["B", "D"]],
}

s.conjoint_question(
    question="Which product would you choose?",
    concepts=concepts,
    task_sets=task_sets,
    required_tags=["brand", "color", "network", "price"],
    none_option="None of these",
    randomize=True,
    tags={"study": "smartphone_cbc"},
)

s.complete()

Notes

• Each task response includes the selected concept and its reporting attributes.
• If dont_know_option is selected, it is treated as a fixed non-concept option (no concept tags are attached).
• The Conjoint Analysis Design Tool produces both the concepts and task_sets structures; paste its output directly into the survey instead of writing them by hand.
• See Choice-Based Conjoint for end-to-end study design and Utility and simulated share methodology for the reporting outputs.