How We Got Here

What is Buy a Feature?

Buy a Feature is a collaborative decision-making framework that uses participatory budgeting to reveal priorities. Instead of asking stakeholders to rank or rate features in isolation, participants must make trade-offs with limited resources—just like real budgeting decisions.

Buy a Feature was created by Luke Hohmann as part of the Innovation Games® suite of collaborative market research methods. Today, it's widely used in product management, portfolio planning, and Agile/SAFe® organizations to generate data-driven roadmap decisions.

Why It Works

• Forces Trade-Offs – Participants can't vote for everything
• Reveals Silent Priorities – High funding without discussion shows clear conviction
• Identifies Vocal Minorities – Interest doesn't always equal willingness to invest
• Generates Quantitative Data – Not just opinions, but measurable allocation decisions
• Creates Buy-In – When people invest (even fake currency), they're more committed to outcomes

Your Session in Horizon Engage

On November 19, 2025, 15 Horizon Engage trainers participated in a multi-group Buy a Feature session to prioritize 14 potential features for the Horizon Engage platform.

Multi-Group Design

We ran two parallel groups with different facilitators. This approach has several advantages:

• Larger Sample Size – 15 total participants across both groups
• Different Perspectives – Two facilitators bring different facilitation styles
• Consensus Validation – When both groups independently prioritize the same features, we have high confidence in those priorities
• Divergence Insights – Features prioritized by only one group reveal context-dependent needs

Our Analytical Approach

We used a comprehensive three-phase analysis methodology to extract insights from your session:

Phase 1: Quantitative Analysis

1. Temporal Reconstruction We replayed every funding event chronologically to understand how priorities evolved over time. This reveals conviction (early funding that persists) vs exploration (late funding or shifting).
2. Area Under Curve (AUC) Calculation AUC measures sustained funding over time, not just final amounts. A feature that reaches 100% in the first minute and stays there has higher AUC than one that fluctuates.
3. Group-Level Analysis We calculated AUC scores independently for Group 1 and Group 2, then aggregated using weighted averages (8 participants in Group 1, 7 in Group 2).

Phase 2: Qualitative Analysis

1. Thematic Coding We analyzed facilitator notes, feedback forms, and meeting transcripts using rapid inductive coding to identify recurring themes.
2. Frequency & Intensity Rating Each theme was rated for how often it appeared (frequency) and how strongly it was expressed (casual/moderate/strong/urgent).
3. Quote Extraction We extracted 2-3 supporting quotes per theme with source attribution to ground insights in participant voices.
4. Competitive Analysis We isolated mentions of Miro/Mural to understand perceived competitive positioning.

Phase 3: Integration Analysis

1. Cross-Validation We compared quantitative (AUC) and qualitative (theme mentions) data to identify alignment and discrepancies.
2. Pattern Detection We identified silent priorities (high AUC, low mentions), vocal minorities (low AUC, high mentions), and surprising winners/losers.
3. Theme-to-Feature Mapping We connected qualitative themes to specific features to understand the "why" behind the funding decisions.

Why This Matters

Traditional prioritization methods (surveys, voting, ranking exercises) suffer from several problems:

• No Trade-Offs – People vote for everything as "high priority"
• Vocal Minorities – The loudest voices dominate, but may not represent the majority
• Social Desirability Bias – People say what sounds good, not what they'd actually invest in
• Point-in-Time Snapshots – No insight into conviction or how priorities evolved

Buy a Feature combined with multi-group analysis and temporal tracking solves these problems by:

• Forcing real trade-offs through constrained budgets
• Capturing how priorities emerged and stabilized over time
• Validating priorities across independent groups
• Integrating quantitative funding data with qualitative reasoning
• Creating an audit trail that shows how decisions were made