Conjoint Analysis & Simulation Tool

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Analyze choice-based conjoint (CBC) data to understand how customers value product attributes. Upload experiment data, estimate individual-level preferences (part-worth utilities), simulate market shares, and optimize product configurations for maximum profit.

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Upload Data

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Configure Attributes

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Estimate Model

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Analyze Results

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Simulate Markets

TEST OVERVIEW & EQUATIONS

Choice-based conjoint (CBC) analysis reveals how customers trade off product attributes when making purchase decisions. Each respondent completes multiple choice tasks where they select their preferred alternative from a set of products with varying attribute levels (brand, features, price, etc.).

The tool estimates individual-level part-worth utilities using multinomial logit (MNL) regression. For each respondent $i$, the utility of alternative $j$ in task $t$ is:

$$ U_{ijt} = \alpha_{j,i} + \sum_{a \in \text{cat}} \sum_{\ell} \beta_{a,\ell,i} \cdot I(\text{level}(a) = \ell) + \sum_{b \in \text{numeric}} \beta_{b,i} \cdot x_b + \beta_{\text{price},i} \cdot p_j $$

where $\alpha_{j,i}$ are alternative-specific constants (for competitor and "None" alternatives), $\beta$ coefficients represent part-worth utilities for each attribute level, and $\beta_{\text{price},i}$ captures price sensitivity.

The probability respondent $i$ chooses alternative $j$ follows the multinomial logit:

$$ P(y_{it} = j) = \frac{\exp(U_{ijt})}{\sum_{k \in C_{it}} \exp(U_{ikt})} $$

Key Concepts

Part-worth utilities: The value (in utility units) a customer assigns to each attribute level. Higher values = stronger preference.
Attribute importance: The range of utilities within an attribute divided by the sum of all ranges. Shows which attributes drive choice most.
Price coefficient: Usually negative; larger magnitude = more price-sensitive. Used for willingness-to-pay calculations.
Individual-level estimation: Each respondent gets their own coefficients, enabling segmentation and personalized simulations.
None alternative: Models the "no purchase" option with its own constant term per respondent.
Competitor alternatives: Real products with known attribute profiles, modeled via alternative-specific constants.

Understanding the "None" Option (Opt-Out Modeling)

How it works: The "None" alternative is modeled as a constant utility term (ASC_None) with no product attributes. This represents a respondent's baseline preference for "doing nothing" or "not purchasing."

Why constant-only? Since "None" has no tangible product features (no brand, no price, no screen size, etc.), its utility doesn't vary with attribute changes. It serves as a fixed outside option that respondents can always choose instead of any product.

Interpretation:

Negative ASC_None (e.g., -2.5): Respondents generally prefer buying something over nothing. The more negative, the stronger the "must have a product" preference.
Positive ASC_None (e.g., +1.2): Respondents prefer doing nothing unless a product offers substantial utility. Indicates high barriers to purchase.
ASC_None ≈ 0: Indifference between "None" and a product with baseline attribute levels. Market is highly competitive for attention.

In market simulation: When you change product configurations (e.g., lower price, better brand), the "None" utility stays constant while product utilities change. This allows realistic "no purchase" share predictions that reflect actual market opt-out rates.

When to Use Conjoint Analysis

Use conjoint when you need to understand customer preferences for multi-attribute products or services. Common marketing applications:

Product design: Which features drive purchase intent? What's the optimal feature set?
Pricing strategy: How much can we charge for premium features? What's willingness-to-pay?
Market simulation: If we launch Product A vs Product B, what market share will each capture?
Competitive positioning: How do customers value our brand vs competitors?
Segmentation: Do different customer segments value attributes differently?

MARKETING SCENARIOS

Load a marketing use case:

Use presets to auto-load realistic CBC study data. The download button exposes the exact dataset so you can modify it in Excel before re-uploading.

INPUTS & SETTINGS

Step 1: Upload Your Data

Upload long-format CBC data where each row represents one alternative in one choice task for one respondent. Required columns: respondent_id, task_id, alternative_id, chosen (0/1), plus attribute columns. Or select a Marketing Scenario above to load example data.

Drag & drop CBC file

CSV with respondent_id, task_id, alternative_id, chosen, and attribute columns

No file uploaded.

Column Mapping

Map your CSV columns to the required CBC data structure fields.

📖 What do these fields mean? (Click to expand)

Your CBC data should be in "long format" — one row per alternative that was shown to a respondent in each choice task.

Field	What It Is	Example Values
Respondent ID	Unique identifier for each person who took the survey. Typically auto-generated by your survey platform.	R001, R002, 12345, email@domain.com
Task ID	Which choice task (question) this row belongs to. Each respondent sees multiple tasks (typically 10-15), each showing a set of products to choose from.	1, 2, 3... or Task_1, Task_2...
Choice Option	Identifies which product/option this row represents within the task. Could be product names (iPhone, Samsung), generic labels (Option A, Option B), or "None".	iPhone, Samsung, BrandX, None, Option_1, Option_2
Chosen (0/1)	Did the respondent pick this option? 1 = Yes, they chose this. 0 = No, they didn't choose it. Exactly ONE option per task should have chosen=1.	0 or 1

💡 Example: If respondent R001 saw 12 tasks, and each task had 4 options to choose from, there would be 48 rows (12 × 4) for that respondent. In each task's 4 rows, exactly 1 row should have chosen=1.

Respondent ID Column

Task ID Column

Choice Option Column (Alternative ID)

Chosen (0/1) Column

Step 2: Configure Attributes

Configure each attribute's type and identify special alternatives (None, Competitors). The tool will automatically dummy-code categorical attributes and create appropriate model terms.

📖 Understanding Attribute Types (Click to expand)

Each product attribute in your conjoint study needs to be classified so the model knows how to treat it:

Type	When to Use	Examples
Categorical	Named categories with no inherent order. Each level gets its own utility estimate.	Brand (Apple, Samsung, LG), Color (Red, Blue, Black), Style (Modern, Classic)
Numeric (Linear)	Continuous numbers where the effect is consistent. Every unit increase has the same impact.	Screen size (inches), Storage (GB), Year of manufacture
💲 Price (Special)	Used for dollar amounts like price. Why is this special? Because price coefficients are used to calculate: 💵 Willingness-to-Pay (WTP): How many $ a feature is worth 📊 Profit calculations: In market simulations Mark your price column as "Price" so these calculations work correctly.	Price ($), Monthly fee, Annual cost
Numeric (Quadratic)	When there's a "sweet spot" — too little OR too much is bad. The model fits a curve instead of a straight line.	Sweetness level (people like medium, not extreme), Delivery time (same-day good, but too fast = suspicious?)

🎯 Quick Rule: If it's text (brand names, features) → Categorical. If it's a number → Numeric Linear (or Price if it's money).

Special Alternatives

📖 When and Why to Use Special Alternatives (Click to expand)

In CBC studies, some choice options are "special" because they don't follow the normal attribute-based utility structure:

🚫 "None" / Opt-Out Alternative

What it is: A "none of these" or "I wouldn't buy any" option that lets respondents decline all products in a task.

Why it matters: Without this, you force respondents to pick something even if they hate all options. The "None" option:

Captures realistic "no purchase" behavior
Provides a benchmark for how good products need to be to win a sale
Enables more realistic market share predictions

For the Smartphone example: If your data has alternatives like "iPhone", "Samsung", "BrandX", and "None", select "None" in the dropdown.

🏢 Competitor Alternatives

What they are: Real branded products (not generic "Option A/B/C") that appeared consistently across choice tasks with their actual market attributes.

When to mark as competitor:

✅ A known brand (iPhone, Toyota Camry) that always appears with its real specs
✅ Products you're comparing against but not designing/optimizing
❌ NOT generic options with varied attribute combinations

Why it matters: Competitors get a "brand-specific constant" that captures overall brand value beyond their individual attributes. This is useful for simulation when you want to see how your new product competes against established brands.

For the Smartphone example: If "iPhone" always appears with real iPhone specs (not hypothetical combinations), mark it as a competitor. If all brands (iPhone, Samsung, BrandX, BrandY, BrandZ) have varied attribute combinations in the study, leave them unchecked—they're design options, not fixed competitors.

🤔 Not sure? If your conjoint study uses hypothetical products where attributes vary (the typical setup), you probably don't need to mark any competitors. Just identify "None" if it exists.

"None" / Opt-Out Alternative (optional)

Select if your study includes a "no purchase" or "none of these" option.

Competitor Alternatives (optional — usually not needed)

Only check these if certain brands appeared with fixed, real-world attributes (not hypothetical combinations). For typical CBC designs where all attributes vary, leave these unchecked.

Analysis Settings

Significance level (α) for coefficient tests

Used for highlighting significant coefficients in the results table.

VISUAL OUTPUT

Shows which attributes drive choice decisions most. Higher bars = more influential attributes in purchase decisions.

How to read this chart

Each bar represents the relative importance of one attribute in driving customer choice. Bars are sorted from most to least important and sum to 100%.

Height = the % of total choice variation explained by that attribute.
Dashed line at 10% = practical threshold — attributes below this have minimal impact on decisions.
A dominant attribute (e.g., 50%+) means choices are largely driven by one factor; evenly spread bars suggest more complex trade-offs.

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Marketing Insight

Attributes with <10% importance have minimal impact on purchase decisions—consider removing them from your product strategy or marketing communications. Focus resources on high-importance attributes where you have competitive advantages.

Average utility for each attribute level. Higher bars = more preferred. Compare within attributes to see which levels win.

How to read this chart

Each bar shows the average part-worth utility for one attribute level, grouped by attribute and color-coded.

Positive bars (right of zero line) = preferred above baseline; negative bars (left) = less preferred.
Compare within an attribute — the level with the longest bar to the right is most preferred for that attribute.
Do not compare across attributes — a utility of +2 for Brand and +2 for Screen Size do not mean "equal preference." Importance scores handle that comparison.
Error bars show standard deviation across respondents — wide bars indicate heterogeneous preferences (potential segmentation opportunity).

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Interpretation Guide

Positive utilities indicate preference above the baseline; negative values indicate dispreference. A feature with +2.5 utility adds 2.5 units to total product utility. Combine high-utility levels from each attribute to design optimal products.

Distribution of price coefficients across respondents. More negative = more price-sensitive.

How to read this chart

This histogram shows the distribution of individual-level price coefficients across all respondents.

X-axis = price coefficient value. More negative = more price-sensitive.
Blue dashed line = mean coefficient; green dotted line = median. A large gap between them suggests outliers or skew.
Black dashed line at 0 = divides price-sensitive (left) from price-insensitive or positive-coefficient respondents (right).
Spread matters: a wide distribution justifies segment-based pricing; a tight cluster suggests uniform sensitivity.
Some positive coefficients are normal in small samples — they may reflect prestige effects or noisy estimation.

⚠️

Common Mistake

Don't assume all customers have the same price sensitivity! A wide distribution justifies segmentation analysis and potentially tiered pricing strategies (economy, standard, premium).

MODEL RESULTS

Total respondents estimated: –

Mean tasks per respondent: –

Mean pseudo-R² (McFadden): – ?

Estimation time: –

Interpretation Aid

Pseudo-R²: Measure of model fit per respondent. Values 0.2–0.4 are typical for CBC studies; higher values indicate stronger predictive power.

Tasks per respondent: More tasks (12–20) produce more stable coefficient estimates. Fewer than 8 tasks may yield unreliable utilities.

APA-Style Report

Estimate the model to see the APA-style statistical report.

Managerial Interpretation

Business-focused interpretation will appear here after estimation.

Aggregated Part-Worth Utilities

Attribute	Level	Mean Utility	Std. Dev.	Min	Max
Estimate the model to view aggregated part-worth utilities.

How to Interpret Part-Worth Utilities

Mean Utility: Average part-worth value across all respondents. Higher utilities indicate stronger preference.

Categorical attributes (brand, camera): Each level gets a coefficient relative to a baseline (usually the first alphabetically). Positive values mean preference over baseline; negative means less preferred.
Linear attributes (price, screen_size): Single coefficient per attribute. Positive = "more is better" (e.g., bigger screen); negative = "less is better" (e.g., lower price).
None alternative (ASC_None): The constant utility for choosing "no purchase." This has no product attributes—it's a fixed outside option. Negative values mean respondents strongly prefer buying something; positive values indicate high barriers to purchase (people prefer doing nothing unless products offer substantial value).
Competitor alternatives (ASC_Competitor_*): Positive = brand loyalty to competitor; negative = preference for your brands.

Std. Dev.: Variation across respondents. High std dev (>0.5) suggests heterogeneous preferences—good candidates for segmentation.

Min/Max: Range of individual utilities. Wide ranges indicate some respondents strongly prefer/dislike this level.

MARKET SIMULATION

Price Configuration

First, specify how price should be treated in the market simulation.

Price Treatment

If price was varied as an attribute, utilities can be translated into willingness-to-pay (WTP) values.

Cost Structure

Define base costs and incremental costs for attribute levels to calculate profitability.

Base Cost (per unit)

Fixed cost regardless of attribute configuration (e.g., core manufacturing cost).

Incremental Costs by Attribute Level

Specify additional cost for each attribute level. Default is 0 (no cost impact). Positive values increase cost, negative values decrease cost relative to baseline.

Scenario Builder

Define a competitive scenario with multiple product alternatives. The tool will compute predicted market shares using the estimated individual utilities.

Simulation Options

Force all to choose (exclude "None" option from simulation) Ignore competitors (exclude competitor products from simulation)

By default, competitors and "None" (if present in study) are included in simulations. Use these options to run "must purchase" or "our brands only" scenarios.

Total market size (customers)

Used to convert share percentages into customer counts and total profit dollars.

Simulation Results

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Reading Your Results

Market share shows what % of customers prefer each option. If "None" captures >30% share, customers may not find current products compelling—consider improving core features or adjusting prices. Compare profit (not just share) to identify the optimal product configuration.

Market Share by Alternative

Predicted preference share based on individual-level utilities averaged across all respondents.

How to read this chart

Bars represent predicted preference share and sum to 100%.
Blue = your products, orange = competitors, grey = None (opt-out).
If "None" exceeds 30%, many customers don't find any product compelling — consider improving features or lowering prices.

Total Profit by Alternative

Profit = Share × Market Size × (Price - Cost). Identifies which products maximize revenue.

How to read this chart

Profit combines market share AND margin, so the share leader is not always the profit leader.
Products with low share but high margin can still be very profitable.
Negative profit means cost exceeds price for that product at its predicted share level.

Product	Market Share (%)	Customer Count	Price	Cost	Margin	Total Profit

PRODUCT OPTIMIZATION

Brute-Force Configuration Search

Explore all possible combinations of attribute levels to find the product configuration that maximizes market share or profit. Useful for new product design decisions.

📖 How does brute-force handle numeric attributes? (Click to expand)

Brute-force optimization tests all combinations of attribute levels. But continuous attributes (like price or screen size) have infinite possible values! Here's how the tool handles this:

Attribute Type	What Values Are Tested
Categorical (Brand, Color)	All unique values that appeared in your CBC data.
Price (when optimizing profit)	A user-defined grid: you specify min, max, and step size. For example, $499–$899 with $50 steps tests 9 price points.
Other Numeric (Screen Size, Storage)	Only the discrete values that appeared in your CBC study. If your study used Screen Size = [5.5", 6.0", 6.5"], only those 3 values are tested.

⚠️ Limitation: The true optimal might fall between tested values (e.g., optimal price = $572, but we only test $550 and $600). For more precision, reduce the price step size or use the Market Simulation to manually test specific configurations.

Select attributes to vary

Optimization goal

Price range to test

Min price

Max price

Price increment

Fixed unit cost

DIAGNOSTICS & ASSUMPTIONS

Model Assumptions & Data Quality Checks

Estimate the model to see diagnostic information.

CBC Study Design Requirements

Sample size: Minimum 100–200 respondents for stable aggregate estimates; 200–500+ recommended for segmentation.
Tasks per respondent: 8–20 choice tasks provide reliable individual-level estimates. Fewer than 8 may yield unstable coefficients.
Alternatives per task: 3–5 alternatives per task is typical. Too few (2) limits information; too many (6+) increases cognitive burden.
Attribute levels: Use 3–5 levels per categorical attribute. Binary attributes provide limited information.
Orthogonality: Experimental design should minimize correlation between attributes. Check design quality in your CBC software.

Model Assumptions

IIA (Independence of Irrelevant Alternatives): MNL assumes adding/removing an alternative affects all others proportionally. Violated when alternatives are perceived as close substitutes.
Linear utility: Utilities are linear combinations of part-worths. May not hold for complex interactions or threshold effects.
Homogeneous preferences (at individual level): Each respondent has stable preferences across tasks. Violated if respondents use different decision rules in different tasks.
No outside information: Respondents base choices only on information presented in the task. Violated if they apply external knowledge about brands or features.

Pseudo-R2 Distribution Across Respondents

Distribution of individual-level McFadden pseudo-R2 values. Higher values = better model fit for that respondent. Very low R2 may indicate random or inattentive responses.

How to read this chart

X-axis: Pseudo-R2 value (0 = no better than random, 1 = perfect prediction).
Blue dashed line: Mean R2 across all respondents.
Left tail (near 0): Respondents whose choices appear random — consider flagging or removing.
Right tail (0.5+): Respondents with very consistent, predictable preferences.
A bimodal distribution may suggest two distinct response quality groups.

👨‍🏫 Professor Mode: Guided Learning Experience

TEST OVERVIEW & EQUATIONS

MARKETING SCENARIOS

INPUTS & SETTINGS

Step 1: Upload Your Data

Column Mapping

Step 2: Configure Attributes

Special Alternatives

🚫 "None" / Opt-Out Alternative

🏢 Competitor Alternatives

Step 3: Estimate Individual Utilities ? Regularized Logistic Regression Estimates individual-level preferences using penalized likelihood (Ridge Regression). This stabilizes estimates for each respondent, enabling segmentation & personalized simulations similar to Hierarchical Bayes.

Analysis Settings

VISUAL OUTPUT

Attribute Importance

Part-Worth Utilities

Price Sensitivity Distribution

MODEL RESULTS

APA-Style Report

Managerial Interpretation

Aggregated Part-Worth Utilities

Individual Respondent Utilities

SEGMENTATION ANALYSIS

K-Means Clustering on Utilities

Elbow Method: Finding Optimal K

Segment Sizes

Segment Profiles: Attribute Importance

Segment-Level Mean Utilities

MARKET SIMULATION

Price Configuration

Cost Structure

Incremental Costs by Attribute Level

Scenario Builder

Simulation Results

Market Share by Alternative

Total Profit by Alternative

Willingness-to-Pay (WTP) Analysis

PRODUCT OPTIMIZATION

Brute-Force Configuration Search

Select attributes to vary

Price range to test

Top 10 Configurations

DIAGNOSTICS & ASSUMPTIONS

CBC Study Design Requirements

Model Assumptions

Pseudo-R2 Distribution Across Respondents

Step 3: Estimate Individual Utilities ?
Regularized Logistic Regression
Estimates individual-level preferences using penalized likelihood (Ridge Regression). This stabilizes estimates for each respondent, enabling segmentation & personalized simulations similar to Hierarchical Bayes.