Feature Fatigue
When more becomes less: Understanding how product complexity impacts consumer satisfaction and lifetime value
Background
The Problem
As technology advances, products include more features, but too many can overwhelm consumers and make products hard to use.
The Research
Three studies examine how consumers balance capability and usability when evaluating products, and how these desires shift over time.
Key Finding
Consumers weigh capability more before use and usability more after, leading to "feature fatigue" from choosing overly complex products.
Authors: Debora Viana Thompson, Rebecca W. Hamilton, and Roland T. Rust
Literature Review
Economic Theory
  • More features increase utility
  • Lancaster's model: each positive attribute increases utility
  • Conjoint analysis supports "more is better"
Usability Research
  • "Less is more" due to learning costs
  • Nielsen: learning difficulty, error rates increase with complexity
  • Experts handle complex products better than novices
Research Hypotheses
H1 & H2
More features increase perceived capability but decrease perceived usability
H3
Expertise has a positive effect on perceptions of product usability
H4 & H5
Consumers weigh capability more before use and usability more after use
Study 1: In-Store Experience
Methodology
  • Three lab experiments (undergrads, digital players).
  • Study 1 & 2: Pre-use evaluations (features: low/medium/high; customization).
  • Study 3: Before/after use (clickstreams for usability).
  • Measures: 7-point scales (capability, usability, satisfaction).
  • Analysis: ANOVA, SEM, regression.
  • Analytical model: Optimize features (R = C - D).
Key Findings
  • Capability ratings: Low (M=3.5) to High (M=6.2) ✓ H1
  • Usability ratings: Low (M=5.8) to High (M=4.1) ✓ H2
  • Experts rated usability higher (M=5.2 vs 4.3) ✓ H3
  • 62.3% chose high-feature model pre-use
Study 2: Online Product Ratings
5.9
Capability Rating
High-feature model (vs. 3.2 for low-feature)
✓ H1
3.8
Usability Rating
High-feature model (vs. 5.5 for low-feature)
✓ H2
58%
Pre-Use Choice
Participants choosing high-feature model
✓ H4/H5 pre-use weights
p<0.05
Experts rated usability higher
✓ H3
147 undergraduates participated in a 3 (feature levels) × 2 (involvement) mixed design, supporting H1-H3 and suggesting H4/H5.
Study 3: Before vs After Use
Before Use
  • Capability weight: β=0.65
  • Usability weight: β=0.35
  • Satisfaction: M=5.8
  • 62% chose high-feature model
After Use
  • Capability weight: β=0.42
  • Usability weight: β=0.58
  • Satisfaction: M=4.2
  • 38% chose high-feature model
98 undergraduates participated in a 2 (use: before/after) × 3 (feature levels) mixed design, confirming all five hypotheses.
pre-use capability weight higher (β=0.65 vs. post β=0.42), supporting H4; usability weight lower (β=0.35 vs. post β=0.58), supporting H5.
Capability increased with features (F=45.2, p<0.01), supporting H1; usability decreased (F=32.1, p<0.01), supporting H2; experts higher usability (F=8.9, p<0.01), supporting H3.
Post-use satisfaction dropped (high features M=4.2 vs. pre M=5.8), choices shifted to medium features (from 62% high to 38%), validating weight shifts.

Analytical Model
Key Equations
  • Pre-use capability: C₁ = d·F
  • Post-use capability: C₂ = e·F (e
  • Usability disutility: D = a·F + b·F²
  • Total profit: R = R₁ + w·R₂
  • Optimal features: F* = (d + w·e - a(1+w)) / (2b(1+w))
Key Insights
As w (future emphasis) increases, optimal feature count F* decreases to avoid feature fatigue and maximize customer lifetime value.
Maximizing initial sales alone leads to too many features, potentially decreasing long-term value.
Managerial Implications
1
Test with Prototypes
Use customer-ready prototypes instead of just conjoint analysis to capture post-use evaluations.
2
Specialize Products
Consider more specialized products with limited features rather than loading all into one device.
3
Balance Features
As emphasis on future sales increases, optimal features decrease, enhancing customer lifetime value.
4
Challenge "More is Better"
This research challenges the "more is better" myth, promoting balance between capability and usability.
Discussion Questions
AI and Feature Fatigue
In the AI era, like with ChatGPT, is feature fatigue more severe? Why?
Design Solutions
How can companies avoid feature fatigue in product design?
Sustainability Impact
What impact does feature fatigue have on sustainable consumption?