Advanced Surveys: Pricing & Feature Prioritization

"How much would you pay for this feature?"

This question produces unreliable data. Users do not know. They guess. They anchor on whatever number feels safe. And you make product decisions based on fiction.

Advanced survey methods solve this by forcing realistic choices instead of asking for direct answers.

Why Simple Questions Fail

Asking someone to rank a list of 20 items is cognitively difficult and often produces unreliable data. Simple rating scales are not enough when you need to understand complex user preferences or prioritize a long roadmap.

The Problem with Direct Questions

Question Type	Problem	Result
"Rate these 20 features 1-5"	Everything gets rated 4-5	No differentiation
"Rank these 20 features"	Cognitive overload	Random ordering after top 5
"How much would you pay?"	No context for comparison	Anchoring bias, guessing
"Is this feature important?"	Social desirability bias	Everyone says yes

Method 1: MaxDiff (Maximum Difference Scaling)

MaxDiff is a powerful method for prioritizing a long list of features. It simplifies the task by showing respondents small, manageable subsets of items and asking them to choose only the "most" and "least" important from each set.

How It Works

Instead of asking users to rate or rank 20 features, MaxDiff shows them sets of 4-5 features at a time:

Each respondent sees multiple sets with different combinations. The analysis produces a clear, rank-ordered list of what your users value most.

When to Use It

Use Case	Why MaxDiff Works
Feature prioritization	Reveals true preferences across 10-30+ items
Roadmap planning	Creates defensible priority rankings
Messaging testing	Identifies most compelling value propositions
Benefit prioritization	Determines which benefits resonate most

The Output

MaxDiff produces:

• A rank-ordered list from most to least important
• Relative importance scores (how much more important is #1 vs #10?)
• Segment-level analysis (do power users prioritize differently?)

Method 2: Conjoint Analysis

Conjoint Analysis is used to understand how people make trade-offs between different product attributes. It helps answer questions like: "How much more are customers willing to pay for a longer battery life versus a better camera?"

How It Works

Instead of asking directly about value, Conjoint presents users with a series of product concepts with different feature combinations and prices, forcing them to make realistic choices:

By analyzing patterns across many choices, the method reveals the hidden value users place on each individual attribute.

When to Use It

Use Case	Why Conjoint Works
Pricing decisions	Reveals willingness to pay for specific features
Feature trade-offs	Quantifies how much users will sacrifice for X
Product configuration	Identifies optimal feature bundles
Competitive positioning	Compares value of your attributes vs. competitors

The Output

Conjoint produces:

• Part-worth utilities (value of each attribute level)
• Price sensitivity curves
• Optimal product configurations
• Market simulation (predicted share at different price points)

Method 3: Van Westendorp Price Sensitivity Meter

The Van Westendorp Price Sensitivity Meter is a classic technique used specifically to gauge customer perceptions of value and identify an acceptable price range ("willingness to pay").

How It Works

The method asks four core questions:

Question	Label	Measures
"At what price would it be so expensive that you would not consider buying it?"	Too Expensive	Upper limit
"At what price would it be so low that you would doubt its quality?"	Too Cheap	Lower limit
"At what price is it starting to get expensive, but you would still consider it?"	Expensive	Resistance point
"At what price would you consider it a great bargain?"	Bargain	Attraction point

The Analysis

The analysis involves plotting the cumulative frequencies for each question:

The intersection points reveal:

• Optimal Price Point (OPP): Where "too cheap" and "too expensive" cross
• Point of Marginal Cheapness (PMC): Lower bound of acceptable range
• Point of Marginal Expensiveness (PME): Upper bound of acceptable range

When to Use It

Use Case	Why Van Westendorp Works
New product pricing	Establishes acceptable range before launch
Price change evaluation	Tests sensitivity to increases/decreases
Market positioning	Compares perceived value across segments
Competitive pricing	Benchmarks your range against competitors

The Output

Van Westendorp produces:

• Acceptable price range (floor to ceiling)
• Optimal price point
• Indifference price point
• Segment-level price sensitivity

Choosing the Right Method

Question You Need to Answer	Method
"Which features should we build first?"	MaxDiff
"How do users trade off features vs. price?"	Conjoint
"What price range will users accept?"	Van Westendorp
"How much is Feature X worth to users?"	Conjoint
"Which of these 25 benefits resonates most?"	MaxDiff
"Are we priced too high or too low?"	Van Westendorp

Combining Methods

For comprehensive pricing and feature strategy, combine methods:

This sequence builds understanding progressively: first what users want, then what they will pay, then the optimal configuration.

Implementation Considerations

Method	Sample Size	Complexity	Tools Required
MaxDiff	n=100-200	Medium	Specialized survey platform
Conjoint	n=200-500	High	Conjoint software, statistical expertise
Van Westendorp	n=100+	Low	Standard survey + spreadsheet

What This Means for Practice

Stop asking users direct questions about preferences and pricing. Their answers are unreliable.

1. Use MaxDiff for feature prioritization—it handles long lists that ratings cannot
2. Use Conjoint for trade-off analysis—it reveals willingness to pay for specific attributes
3. Use Van Westendorp for price range discovery—four questions identify the acceptable zone
4. Combine methods for comprehensive pricing strategy
5. Get expert help for Conjoint—the statistical complexity is real

The goal is not to ask users what they want. It is to observe what they choose when forced to make trade-offs.