Vahid Karimi Motahhar, Ph.D. in Marketing

Each attribute gets a percentage showing its weight in the decision. If Price shows 40%, it accounts for 40% of preference variation. Includes a color-coded legend — High (>30%) means a critical differentiator, Medium (15–30%) a competitive advantage, and Low (<15%) nice-to-have.

Utilities are “preference points.” One level per attribute is the baseline (zero). Positive = preferred more, negative = preferred less. The report includes a worked example: “If Premium Brand = +2.5 and Generic = −1.2, customers prefer premium by 3.7 points — a strong preference.” Per-attribute bar charts and tables with a built-in “Understanding Utilities” guide.

Tests whether the effect of one attribute depends on the level of another — for example, whether brand preference changes at different price points. Helps you spot combinations that are worth more (or less) than the sum of their parts. Available on Academia and Professional tiers.

How much of one feature are customers willing to give up to get more of another? Calculates marginal substitution ratios between attributes and translates abstract utility numbers into practical guidance for product configuration and resource allocation.

An interactive tool. Pick “Change A” (e.g., Brand X → Brand Y) and “Change B” (e.g., standard → premium material), compare utility differences side by side. If a price attribute is present, both changes get translated into dollar equivalents — so you can say “this brand switch is worth a $15 price drop.” Includes baseline price sliders for each side.

If your survey includes demographic questions, this tab shows respondent breakdown — age groups, gender splits, income brackets, or whatever custom demographics you defined during study design. Visualized as distribution charts so you can verify your sample composition before interpreting conjoint results.

Define competing products and predict real-world outcomes. Four built-in scenarios: Competitive Market Share (how products compete), Product Mix & Inventory (optimal quantities from demand), What-If Scenarios (test hypothetical configs), and Portfolio Optimization (maximize market capture). Plus a Disruption Simulator: add a new entrant and see exactly which products lose share, with before/after pie charts and a detailed impact table. Includes a “What is Market Simulation?” guide and “Understanding the Results” boxes.

Automatically identifies the product configuration that maximizes customer preference. Shows the ideal level for each attribute and provides strategic pricing guidance — not just what customers want, but how to position and price it.

Converts utility values into dollar amounts. How much would customers pay for each feature upgrade? What is the brand equity in monetary terms? Maps price sensitivity across all attribute levels. Requires a price attribute with numeric levels in your design.

Compare two full product configurations and calculate the implied switching cost — the price difference needed to make a customer indifferent between them. Useful for competitive pricing, upgrade bundling, and estimating how much it costs a competitor to poach your customers.

The technical layer: log-likelihood, convergence status, estimation method details, and per-respondent raw data. Export responses to CSV (Academia and Professional tiers) for further analysis in R, Python, or Excel. For researchers and instructors who want to verify the statistical foundation.