https://choicery.ai/ https://choicery.ai/videos/choicery-conjoint-software-demo-poster.webp Choicery conjoint analysis software demo Product demo with narration: building a study, collecting choices, analyzing preferences and simulating the market in Choicery. https://choicery.ai/videos/choicery-conjoint-software-demo.mp4 https://choicery.ai/videos/conjoint-survey-on-laptop-tablet-phone-poster.webp A conjoint project on laptop, tablet and phone The same Choicery project on laptop, tablet and phone: the survey flow being built, a choice task being answered, the market simulator showing the same shares, and finally all three screens side by side. https://choicery.ai/videos/conjoint-survey-on-laptop-tablet-phone.mp4 https://choicery.ai/analytics.html https://choicery.ai/videos/hierarchical-bayes-estimation-explained-poster.webp How Hierarchical Bayes estimation works How Hierarchical Bayes estimation works: every respondent's own choices on the lower level, the population distribution on the upper level, and the sampler moving between them until the individual utilities settle. https://choicery.ai/videos/hierarchical-bayes-estimation-explained.mp4 https://choicery.ai/videos/latent-class-analysis-explained-poster.webp How Latent Class analysis works How Latent Class works: the same choices as in HB, a few classes each with its own utilities, the EM algorithm moving respondents between classes, and BIC choosing how many classes to keep. https://choicery.ai/videos/latent-class-analysis-explained.mp4 https://choicery.ai/videos/ai-profile-analysis-neural-network-explained-poster.webp How the AI-Driven Profile Analysis works How the AI-Driven Profile Analysis works: a respondent's profile enters a neural network, the network outputs that person's part-worth utilities, and the utilities feed a standard choice model trained against the observed choices. https://choicery.ai/videos/ai-profile-analysis-neural-network-explained.mp4 https://choicery.ai/videos/conjoint-interaction-search-explained-poster.webp How Interaction Search works in conjoint analysis How Interaction Search works: every pair of attributes gets its own full model, a likelihood-ratio test against the main-effects base model, and a pair counts only when it clears both the statistical and the practical hurdle. https://choicery.ai/videos/conjoint-interaction-search-explained.mp4 https://choicery.ai/conjoint-maxdiff.html https://choicery.ai/images/discrete-choice-experiment-cbc-task.webp https://choicery.ai/images/choice-based-conjoint-task-stacked.webp https://choicery.ai/images/choice-based-conjoint-task-inline.webp https://choicery.ai/images/conjoint-design-diagnostics-balanced-overlap.webp https://choicery.ai/images/cbc-shop-online-store-choice-task.png https://choicery.ai/images/cbc-shop-store-shelf-choice-task.webp https://choicery.ai/images/dual-response-conjoint-integrated.webp https://choicery.ai/images/dual-response-conjoint-two-block-part-1.webp https://choicery.ai/images/dual-response-conjoint-two-block-part-2.webp https://choicery.ai/images/best-worst-cbc-choice-task.webp https://choicery.ai/images/best-worst-cbc-integrated-purchase-question.webp https://choicery.ai/images/best-worst-cbc-two-block-purchase-question.webp https://choicery.ai/images/maxdiff-task-classic.webp https://choicery.ai/images/maxdiff-task-sideways.webp https://choicery.ai/images/maxdiff-task-numbered.webp https://choicery.ai/images/maxdiff-task-phone.webp https://choicery.ai/images/anchored-maxdiff-task.webp https://choicery.ai/images/anchored-maxdiff-calibration-screen.webp https://choicery.ai/images/choice-based-conjoint-task-phone.webp A choice-based conjoint task on a phone Phone demo: scroll down through the pizza ovens and select Option 2. https://choicery.ai/videos/choice-based-conjoint-task-phone-demo.mp4 https://choicery.ai/videos/dual-response-conjoint-integrated-poster.webp Integrated dual-response conjoint Integrated dual response: the respondent picks an option and answers the buy or no-buy question on the same page. https://choicery.ai/videos/dual-response-conjoint-integrated.mp4 https://choicery.ai/videos/dual-response-conjoint-two-block-poster.webp Two-block dual-response conjoint Two-block dual response: the respondent answers the choice tasks first, then sees each chosen product again with the buy or no-buy question. https://choicery.ai/videos/dual-response-conjoint-two-block.mp4 https://choicery.ai/faq.html https://choicery.ai/features.html https://choicery.ai/images/survey-flow-editor.webp https://choicery.ai/images/survey-page-tools.webp https://choicery.ai/images/survey-question-editor.webp https://choicery.ai/images/conjoint-analysis-hub-utilities-importance.png https://choicery.ai/images/conjoint-market-simulator-share-of-preference.webp https://choicery.ai/images/conjoint-willingness-to-pay-simulation.webp https://choicery.ai/images/conjoint-reservation-price-demand-curve.webp https://choicery.ai/images/survey-data-export.webp https://choicery.ai/images/conjoint-analysis-export-replication-package.webp https://choicery.ai/images/julia-hb-replication-package-files.webp https://choicery.ai/images/conjoint-design-efficiency-output.webp https://choicery.ai/how-it-works.html https://choicery.ai/videos/build-and-publish-conjoint-survey-walkthrough-poster.webp Building and publishing a conjoint survey Screen recording: a new project, an age and a gender question, a CBC with three attributes and product images, the generated design, the participant preview, and publishing the survey with its link. https://choicery.ai/videos/build-and-publish-conjoint-survey-walkthrough.mp4 https://choicery.ai/videos/hierarchical-bayes-covariates-walkthrough-poster.webp Hierarchical Bayes with covariates, screen recording Screen recording: hierarchical Bayes with a linear price and age and gender as covariates, the results with covariate effects, and the Julia replication package under Export. https://choicery.ai/videos/hierarchical-bayes-covariates-walkthrough.mp4 https://choicery.ai/videos/ai-profile-analysis-walkthrough-poster.webp AI-Driven Profile Analysis, screen recording Screen recording: AI-Driven Profile Analysis with age and gender, showing a curved age effect on price sensitivity and on the preferred temperature. https://choicery.ai/videos/ai-profile-analysis-walkthrough.mp4 https://choicery.ai/videos/latent-class-analysis-walkthrough-poster.webp Latent Class analysis, screen recording Screen recording: Latent Class with age and gender as membership covariates, the model comparison, segment sizes, importances and covariate profile. https://choicery.ai/videos/latent-class-analysis-walkthrough.mp4 https://choicery.ai/videos/conjoint-interaction-search-walkthrough-poster.webp Interaction search in conjoint data, screen recording Screen recording: interaction search over all attribute pairs, with color by price as the significant interaction. https://choicery.ai/videos/conjoint-interaction-search-walkthrough.mp4 https://choicery.ai/videos/conjoint-survey-analysis-walkthrough-poster.webp Survey Analysis of conjoint results, screen recording Screen recording: Survey Analysis with importances and part-worth utilities grouped by gender and covariate forest plots. https://choicery.ai/videos/conjoint-survey-analysis-walkthrough.mp4 https://choicery.ai/videos/conjoint-market-simulator-walkthrough-poster.webp Conjoint market simulator, screen recording Screen recording: the market simulator with one own pizza oven and two competitors. Choice share with confidence intervals, willingness to pay including sampling of scenarios, reservation price, price elasticity, sensitivity, and two personas from the AI-Driven Profile Analysis. https://choicery.ai/videos/conjoint-market-simulator-walkthrough.mp4 https://choicery.ai/market-simulation.html https://choicery.ai/videos/conjoint-market-simulation-choice-share-poster.webp Choice share in a conjoint market simulator Choice Share in the simulator: four pizzas set up as a market, Share of Preference chosen, each respondent's probabilities averaged over 300 people, our pizza leading with 32.8 percent, and 95 percent intervals from 200 resamples showing that the lead over the runner-up is small. https://choicery.ai/videos/conjoint-market-simulation-choice-share.mp4 https://choicery.ai/videos/conjoint-willingness-to-pay-simulator-poster.webp Willingness to pay in a conjoint market simulator Willingness to pay in the simulator: delivery time of our pizza changes from 35 to 20 minutes, the price rises until the share is back at 32.8 percent, 20-minute delivery is worth 1.77 dollars, and Robust WTP repeats the calculation in 300 random markets. https://choicery.ai/videos/conjoint-willingness-to-pay-simulator.mp4 https://choicery.ai/videos/conjoint-reservation-price-simulator-poster.webp Reservation price in a conjoint market simulator Reservation price in the simulator: one respondent's price limit where our pizza stops beating the best alternative, the limits of all 300 respondents, a median reservation price of 10.72 dollars and a demand curve showing that 48 percent would still buy at 11 dollars. https://choicery.ai/videos/conjoint-reservation-price-simulator.mp4 https://choicery.ai/videos/conjoint-price-elasticity-simulator-poster.webp Price elasticity in a conjoint market simulator Price elasticity in the simulator: our pizza simulated at 8.50 and 17 dollars with the arc elasticity from the midpoint formula, then at ten prices along one curve, green where demand is inelastic and red where it is elastic, and a log-log fit with a slope of minus 1.23. https://choicery.ai/videos/conjoint-price-elasticity-simulator.mp4 https://choicery.ai/videos/conjoint-sensitivity-analysis-simulator-poster.webp Sensitivity analysis in a conjoint market simulator Sensitivity analysis in the simulator: delivery time tested level by level, four simulations per product for our pizza and competitor A, 20-minute delivery adds 5.65 points for our pizza and 65 minutes would cost almost 15 points. https://choicery.ai/videos/conjoint-sensitivity-analysis-simulator.mp4 https://choicery.ai/videos/conjoint-persona-simulation-poster.webp Persona simulation with conjoint data Persona simulation: a profile model turns a person's profile into part-worths, a student persona picks our pizza with 50.2 percent and cares most about price, and a family vegetarian persona picks a different pizza in the comparison. https://choicery.ai/videos/conjoint-persona-simulation.mp4 https://choicery.ai/pricing-methods.html https://choicery.ai/images/van-westendorp-price-questions.webp https://choicery.ai/images/van-westendorp-price-map.webp https://choicery.ai/images/gabor-granger-question.webp https://choicery.ai/images/gabor-granger-demand-curve-revenue.webp https://choicery.ai/images/gabor-granger-detailed-results.webp https://choicery.ai/images/kano-question-pair.webp https://choicery.ai/images/kano-better-worse-chart.webp https://choicery.ai/videos/van-westendorp-price-analysis-explained-poster.webp How the Van Westendorp analysis works How the Van Westendorp analysis works: four price questions per respondent, four cumulative curves, each crossing read from its own two questions, and the price map with the acceptable range shaded. https://choicery.ai/videos/van-westendorp-price-analysis-explained.mp4 https://choicery.ai/videos/gabor-granger-price-analysis-explained-poster.webp How the Gabor-Granger analysis works How the Gabor-Granger analysis works: one 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