Experiment
add_neither_option object
- boolean
- null
additional_notes_max_workers object
- integer
- null
all_combinations_index object
- string[][]
- null
alternative_shuffle_seed object
- integer
- null
attribute_count object
- integer
- null
Possible values: >= 2 and <= 10
attribute_shuffle_seed object
- integer
- null
batch_size object
- integer
- null
binary_choice object
- boolean
- null
100check_for_continuous_variables object
- boolean
- null
concept_description object
- string
- null
concept_statements object
- object[]
- null
confidence_level object
- string
- null
0cost_amount object
- number
- null
United Statescoverage_probability object
- number
- null
128decision_time_seed object
- integer
- null
do_research object
- boolean
- null
dv_llm_model object
- LLMModel
- null
Enum representing the llm model types used in the experiments.
Enum representing the llm model types used in the experiments.
Possible values: [azure-openai-gpt4, azure-openai-gpt4o-mini, azure-openai-gpt35, azure-openai-gpto3-mini, azure-openai-completions, sonar, databricks-sonnet4, databricks-claude-sonnet-4, databricks-llama4-maverick, databricks-llama3.3-70B, databricks-llama3.3-405B, bedrock:xai.grok-4.3]
effect_encoding object
- boolean
- null
falsePossible values: [MLE, HB]
HBPossible values: [concept_testing, conjoint]
conjointexpr_llm_model object
- LLMModel
- null
Enum representing the llm model types used in the experiments.
Enum representing the llm model types used in the experiments.
Possible values: [azure-openai-gpt4, azure-openai-gpt4o-mini, azure-openai-gpt35, azure-openai-gpto3-mini, azure-openai-completions, sonar, databricks-sonnet4, databricks-claude-sonnet-4, databricks-llama4-maverick, databricks-llama3.3-70B, databricks-llama3.3-405B, bedrock:xai.grok-4.3]
[]generate_additional_notes object
- boolean
- null
hb_folder object
- string
- null
hb_run_id object
- string
- null
image_name1 object
- string
- null
image_name2 object
- string
- null
include_price_brand object
- boolean
- null
is_hb_run object
- boolean
- null
is_private object
- boolean
- null
truelatent_variables_llm_model object
- LLMModel
- null
Enum representing the llm model types used in the experiments.
Enum representing the llm model types used in the experiments.
Possible values: [azure-openai-gpt4, azure-openai-gpt4o-mini, azure-openai-gpt35, azure-openai-gpto3-mini, azure-openai-completions, sonar, databricks-sonnet4, databricks-claude-sonnet-4, databricks-llama4-maverick, databricks-llama3.3-70B, databricks-llama3.3-405B, bedrock:xai.grok-4.3]
latent_variables_measurement_count object
- integer
- null
latent_variables_scale_labels_count object
- integer
- null
latent_variables_traits_count object
- integer
- null
level_count object
- integer
- null
Possible values: >= 2 and <= 10
levels_llm_model object
- LLMModel
- null
Enum representing the llm model types used in the experiments.
Enum representing the llm model types used in the experiments.
Possible values: [azure-openai-gpt4, azure-openai-gpt4o-mini, azure-openai-gpt35, azure-openai-gpto3-mini, azure-openai-completions, sonar, databricks-sonnet4, databricks-claude-sonnet-4, databricks-llama4-maverick, databricks-llama3.3-70B, databricks-llama3.3-405B, bedrock:xai.grok-4.3]
levels_per_trait object
- integer
- null
1lv_based_nl_summary_max_workers object
- integer
- null
lv_statement_llm_model object
- LLMModel
- null
Enum representing the llm model types used in the experiments.
Enum representing the llm model types used in the experiments.
Possible values: [azure-openai-gpt4, azure-openai-gpt4o-mini, azure-openai-gpt35, azure-openai-gpto3-mini, azure-openai-completions, sonar, databricks-sonnet4, databricks-claude-sonnet-4, databricks-llama4-maverick, databricks-llama3.3-70B, databricks-llama3.3-405B, bedrock:xai.grok-4.3]
lv_statement_max_workers object
- integer
- null
falsetruemax_length object
- integer
- null
mnp_model object
- boolean
- null
mood_statement_seed object
- integer
- null
non_us_target_population object
- object
- null
null_levels object
- boolean
- null
paper_data object
- string[][]
- null
persona_nl_llm_model object
- LLMModel
- null
Enum representing the llm model types used in the experiments.
Enum representing the llm model types used in the experiments.
Possible values: [azure-openai-gpt4, azure-openai-gpt4o-mini, azure-openai-gpt35, azure-openai-gpto3-mini, azure-openai-completions, sonar, databricks-sonnet4, databricks-claude-sonnet-4, databricks-llama4-maverick, databricks-llama3.3-70B, databricks-llama3.3-405B, bedrock:xai.grok-4.3]
128population_group object
- GroupLabel
- null
Possible values: [Urban Singles, Suburban Families, Career-Oriented Professionals, Young College Students, Empty Nesters, Retired Widows/Widowers, Young Parents on a Budget, High-Income Dual-Earner Couples, Tech-Savvy Millennials, Blue-Collar Workers, Single Mothers, Older Retirees, Highly Educated Elites, Affluent Empty Nesters, Graduate Students, Large Suburban Families, Hispanic Professionals, Educated Single Women]
population_seed object
- integer
- null
population_traits object
- PopulationTraits
- null
pre_cooked_attributes_and_levels_lookup object
- object[][]
- null
items object
- string
- string[]
128price_generation_seed object
- integer
- null
prob_to_discrete_seed object
- integer
- null
prompt_type object
- string
- null
0r_squared object
- integer
- null
realworld_products object
- object[]
- null
respondent_dependent_variable object
- string
- null
respondent_instruction_for_concept object
- string
- null
respondent_instructions_preamble object
- string
- null
response_type object
- SurveyResponseType
- null
Possible values: [probabilistic, discrete]
sample_size object
- integer
- null
score_llm_model object
- LLMModel
- null
Enum representing the llm model types used in the experiments.
Enum representing the llm model types used in the experiments.
Possible values: [azure-openai-gpt4, azure-openai-gpt4o-mini, azure-openai-gpt35, azure-openai-gpto3-mini, azure-openai-completions, sonar, databricks-sonnet4, databricks-claude-sonnet-4, databricks-llama4-maverick, databricks-llama3.3-70B, databricks-llama3.3-405B, bedrock:xai.grok-4.3]
spearman_correlation object
- number
- null
state object
- string
- null
survey_design_filelink object
- string
- null
survey_editable_lines object
- object
- null
survey_prompt object
- string
- null
target_behavior_context object
- boolean
- null
target_population object
- TargetPopulation
- null
age object
- integer[]
- null
education_level object
- EducationLevelEnum[]
- null
Possible values: [Some College, High School Diploma, Less than high school, High School but no diploma, Bachelors, Associates, Masters, PhD]
gender object
- GenderEnum[]
- null
Possible values: [Male, Female]
household_income object
- integer[]
- null
number_of_children object
- ChildrenEnum[]
- null
Possible values: [0, 1, 2, 3, 4+]
racial_group object
- RacialGroupEnum[]
- null
Possible values: [Mixed race, White, African American, Asian or Pacific Islander, Other race, American Indian or Alaska Native]
state object
- USStatesEnum
- null
Possible values: [Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Hawaii, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, Wisconsin, Wyoming]
tasks_per_respondent object
- integer
- null
title object
- string
- null
token_count object
- integer
- null
total_number_of_tasks object
- integer
- null
trait_prediction_llm_model object
- LLMModel
- null
Enum representing the llm model types used in the experiments.
Enum representing the llm model types used in the experiments.
Possible values: [azure-openai-gpt4, azure-openai-gpt4o-mini, azure-openai-gpt35, azure-openai-gpto3-mini, azure-openai-completions, sonar, databricks-sonnet4, databricks-claude-sonnet-4, databricks-llama4-maverick, databricks-llama3.3-70B, databricks-llama3.3-405B, bedrock:xai.grok-4.3]
use_api object
- boolean
- null
use_batching object
- boolean
- null
falseuse_halton_draws object
- boolean
- null
use_multiprocessing object
- boolean
- null
falsefalseuse_threading object
- boolean
- null
falseuser_uploaded_persona_filelink object
- string
- null
why_prompt object
- string
- null
July 2026{
"add_neither_option": true,
"check_for_continuous_variables": true,
"concept_description": "",
"concept_statements": [
{
"labels": [
"Strongly Disagree",
"Disagree",
"Neutral",
"Agree",
"Strongly Agree"
],
"statement": "This smoothie would provide me with sustained energy throughout the day."
},
{
"labels": [
"Strongly Disagree",
"Disagree",
"Neutral",
"Agree",
"Strongly Agree"
],
"statement": "The protein content of this product meets my nutritional needs."
},
{
"labels": [
"Strongly Disagree",
"Disagree",
"Neutral",
"Agree",
"Strongly Agree"
],
"statement": "The flavor varieties offered are appealing to me."
},
{
"labels": [
"Strongly Disagree",
"Disagree",
"Neutral",
"Agree",
"Strongly Agree"
],
"statement": "I would purchase this smoothie as a healthy meal replacement option."
}
],
"country": "USA",
"do_research": false,
"effect_encoding": true,
"ensure_25_trait_level_count": false,
"experiment_type": "conjoint",
"external_personas": [
"https://uk.linkedin.com/in/samgadsby",
"https://www.linkedin.com/in/bencheatham",
"https://uk.linkedin.com/in/simonmelaniphy",
"https://uk.linkedin.com/in/alex-velinov",
"https://www.linkedin.com/in/martinkronstad",
"https://uk.linkedin.com/in/pshannon"
],
"hb_folder": "",
"hb_run_id": "",
"image_name1": "d78f470a-90ad-4b1e-a322-cb4cc9cc631b.png",
"image_name2": "",
"is_private": false,
"match_population_distribution": false,
"mnp_model": true,
"non_us_target_population": {
"Age": [
18,
92
],
"Education_Level": [
"Masters",
"Bachelors",
"Some College",
"PhD",
"High School but no diploma",
"High School Diploma",
"Associates",
"Less than high school"
],
"Gender": [
"Female",
"Male"
],
"Home_Ownership": [
"Own",
"Rent",
"Other"
],
"Household_Size": [
"4",
"2",
"1",
"3",
"More than 4 people"
],
"Household_With_Children": [
"Yes",
"No"
]
},
"null_levels": true,
"population_group": "Urban Singles",
"population_traits": {
"Big five conscientiousness": [
"Flexible",
"Balanced",
"Structured"
],
"Price Sensitivity": [
"Highly Price Sensitive",
"Value Seeker",
"Premium Acceptor",
"Price Indifferent"
],
"Risk Aversion": [
"Highly Risk Averse",
"Cautious",
"Risk Tolerant",
"Risk Seeking"
],
"Vehicles In Household": [
"0",
"1",
"2",
"3",
"4",
"4+"
]
},
"pre_cooked_attributes_and_levels_lookup": [
[
"Price",
[
"599 USD",
"450 USD",
"799 USD",
"900 USD",
"499 USD"
]
],
[
"Display Size",
[
"6.1-inch",
"6.7-inch",
"6.3-inch"
]
],
[
"RAM",
[
"6 GB",
"12 GB",
"8 GB"
]
],
[
"Internal Storage",
[
"128 GB",
"256 GB"
]
],
[
"Battery Capacity",
[
"5000 mAh",
"4492 mAh",
"3274 mAh",
"3692 mAh",
"4000 mAh"
]
],
[
"Main Camera",
[
"50 MP + 12 MP + 10 MP",
"48 MP",
"48 MP + 48 MP",
"48 MP + 12 MP",
"50 MP + 8 MP + 5 MP"
]
],
[
"Chipset",
[
"A19",
"Tensor G4",
"Snapdragon 8 Elite Gen 5",
"Exynos 1380",
"A18"
]
],
[
"Operating System",
[
"Android 15",
"Android 16",
"iOS 26"
]
],
[
"Weight",
[
"177 grams",
"188 grams",
"189 grams",
"167 grams"
]
],
[
"Product",
[
"Samsung (Galaxy A57 5G)",
"Samsung (Galaxy S26)",
"Apple (iPhone 17e)",
"Apple (iPhone 17)",
"Google (Pixel 10a)"
]
]
],
"realworld_products": [
{}
],
"respondent_instruction_for_concept": "View the concept (image) and rate the following statements based on your first impression. Use only the info shown. No right or wrong answers.",
"response_type": "discrete",
"survey_editable_lines": {
"1": "You are tasked with answering a conjoint survey question related to examining factors that influence purchase of facial moisturizer as the following individual:",
"4": "Imagine you are in a situation where You need to purchase a facial moisturizer for your daily skincare routine from an online retailer that provides detailed product specifications.",
"5": " - Consider the different combinations of factors and select the option that would most likely lead to the maximize level of purchase of facial moisturizer.",
"6": " - When evaluating each option, consider how the combination of factors would realistically interact to affect purchase of facial moisturizer.",
"7": " - Choose the scenario where the presented conditions would be most conducive to maximizing purchase of facial moisturizer.",
"8": "",
"9": "Answer the survey question as this person, following these steps:",
"10": "Before selecting an option, consider the following:\n1. Consider how your demographic traits and lifestyle influence your priorities for the decision.\n2. Evaluate the options in the survey question based on these priorities.",
"12": "Please read the descriptions of facial moisturizers (50ml) carefully. Then, please select which facial moisturizer you would purchase."
},
"target_population": {
"age": [
18,
95
],
"education_level": [
"Less than high school",
"High School but no diploma",
"High School Diploma",
"Some College",
"Associates",
"Bachelors",
"Masters",
"PhD"
],
"gender": [
"Male",
"Female"
],
"household_income": [
0,
150000
],
"number_of_children": [
"1",
"2",
"3",
"4+"
],
"racial_group": [
"White",
"African American",
"American Indian or Alaska Native",
"Asian or Pacific Islander",
"Mixed race",
"Other race"
]
},
"use_external_personas": false,
"use_halton_draws": false,
"use_population_group": false,
"user_uploaded_persona": false,
"why_prompt": "What are the key factors influencing consumers' purchase consideration and preference for upgrading their mobile devices, such as from an iPhone 12 to an iPhone 15 or Samsung Galaxy S22 to S24?",
"year": "July 2026"
}