TURF vs Conjoint: When to Use Each

Different Questions, Different Methods

TURF analysis and conjoint analysis both help optimize product offerings, but they answer fundamentally different questions. Understanding which question you're asking determines which method you need.

TURF answers: "Which combination of items covers the most customers?" It selects from a list of discrete items (flavors, features, SKUs) to maximize audience reach.

Conjoint answers: "How do product attributes trade off against each other?" It measures how features, pricing, and configurations interact to drive preference.

TURF is a selection tool. Conjoint is a configuration tool. They solve different problems and can be used together when a project requires both.

Head-to-Head Comparison

When to Choose TURF

Selecting Items from a List

You have 20 flavors and shelf space for 8. You have 30 features and engineering capacity for 10 this quarter. You have 15 menu items and want to know the minimum set that satisfies 90% of guests. These are TURF problems.

The items are discrete, standalone options. A customer picks one (or a few). The items don't combine into a single product. You want the set that covers the broadest audience.

Portfolio Rationalization

TURF excels at the "what should we cut?" question. By calculating reach at every portfolio size, it shows exactly how much customer coverage you lose for each item removed. This makes rationalization data-driven rather than political.

Simple Acceptance Data

TURF works with Yes/No acceptance data, which is fast to collect and easy for respondents. A 20-item acceptance survey takes 2-3 minutes. The data collection requirements are minimal compared to conjoint.

Items Are Substitutable

TURF assumes customers pick from the set (one or a few). If customers would buy multiple items simultaneously as a bundle, TURF's reach metric is less meaningful. For bundled products, conjoint's product-profile approach is more appropriate.

When to Choose Conjoint

Feature-Price Trade-offs

"Is unlimited storage worth a $30 price increase?" is a trade-off question. TURF can't answer it because it doesn't model how features interact with price. Conjoint presents complete product profiles where price and features are bundled together, forcing the trade-off that reveals willingness to pay.

Configuring a Single Product

When you're designing one product (not selecting from a portfolio), conjoint is the right tool. A SaaS company deciding between four pricing tiers with different feature sets needs conjoint's attribute/level structure. TURF would tell you which individual features are most widely accepted, but not how to bundle them or what to charge.

Competitive Simulation

Conjoint's market simulator lets you model competitive scenarios: "If we launch Product A at $79 and Competitor X keeps their current offering, what share do we capture?" TURF doesn't model competition because it works with acceptance, not comparative choice.

Willingness to Pay

Conjoint produces dollar-value estimates for every feature. TURF produces reach percentages. If your decision requires pricing guidance, conjoint is the only option.

When They Work Together

TURF and conjoint are complementary, not competing. The most common combined workflow:

Phase 1: TURF for Portfolio Selection

Start with a large candidate list. Use TURF to identify which items/features/flavors to offer. This narrows the field.

Example: A snack company tests 20 flavor candidates with TURF. The optimal 8-flavor portfolio is identified.

Phase 2: Conjoint for Product Configuration

Take the TURF-selected items and use conjoint to optimize how they're configured, bundled, and priced.

Example: The 8 flavors become attribute levels within a conjoint study that also includes package size, price, and branding. Conjoint reveals the optimal flavor-size-price combinations and predicts market share.

Why This Sequence Works

TURF is cheap and fast (binary acceptance survey, 200 respondents, 3-minute survey). It's an efficient screening tool for reducing a large list to a manageable set. Running conjoint on the full 20 flavors would require an impossibly complex design. Running it on the TURF-selected 8 is feasible.

Another Combined Use Case: Feature Tier Design

1. MaxDiff to rank 25 candidate features by importance
2. TURF on MaxDiff data to identify the 5 features that cover the broadest free-tier audience
3. Conjoint with the top 7 features + price to optimize the paid tier bundles

Each method handles the part of the problem it's best suited for. MaxDiff ranks. TURF selects. Conjoint configures and prices.

Common Mistake: Using the Wrong Method

Using TURF When You Need Trade-Offs

A SaaS company runs TURF on 15 features and selects the top 8 for the product. But the question was never "which features are most widely acceptable?" It was "which features justify a higher price tier?" That's a conjoint question. TURF tells you what people want. Conjoint tells you what people will pay for.

Using Conjoint When You Need Portfolio Selection

A CPG company runs conjoint to decide which 6 of 15 flavors to produce. Conjoint tests flavor as an attribute with 15 levels, alongside package size, price, and branding. The design has 15 levels for one attribute, which bloats the sample size requirement and doesn't directly answer the portfolio selection question. TURF would answer it more efficiently with simpler data collection.

Using Neither When You Need MaxDiff

Both TURF and conjoint are overkill if you just need a ranked list of priorities. For "which features matter most?", MaxDiff gives you a clean priority ranking in a shorter survey with a smaller sample than either alternative.

Decision Flowchart

Start here: What's your research question?

1. "Which items from this list should we offer?" → TURF
2. "How should we configure and price our product?" → Conjoint
3. "Which items are most important to our customers?" → MaxDiff
4. "Which items should we offer AND how should we price them?" → TURF first, then Conjoint
5. "What's the price sensitivity for this single product?" → Van Westendorp

Frequently Asked Questions

Can TURF and conjoint use the same survey data?

Not directly, because they require different data structures. TURF needs item-level acceptance; conjoint needs multi-attribute choice tasks. You can collect both in one survey (a TURF acceptance section + a conjoint section), but the data feeds separate analyses.

Which is more expensive to run?

TURF is typically 40-60% cheaper. It requires shorter surveys (lower per-respondent cost), smaller samples, and simpler analysis. A TURF project might cost $5,000-$12,000 total, while a conjoint project runs $10,000-$25,000.

Can I derive TURF results from conjoint data?

Yes, with an extra step. Run the conjoint analysis to produce utility scores. Define product profiles, predict each respondent's choice probability, and create a binary acceptance matrix from the predictions. Run TURF on that matrix. This approach works but requires more analytical effort than running TURF on directly collected acceptance data.

Related Guides

• TURF Analysis: Complete Guide -- Full TURF methodology
• Conjoint Analysis: Complete Guide -- Full conjoint methodology
• MaxDiff vs Conjoint -- Another method comparison
• TURF for Product Development -- TURF in practice
• Van Westendorp Pricing Model -- Simple pricing research alternative

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