Analogical Ideation

Purpose

Generate novel solution concepts by systematically identifying how analogous problems have been solved in other industries, domains, or contexts, and transferring those solution principles to the current challenge. Analogical ideation is the primary method for overcoming functional fixedness—the tendency of domain experts to generate only domain-typical solutions.

When to Use

Use analogical ideation when:

• The team has domain expertise but struggles to generate non-obvious solutions
• Initial brainstorming produced only incremental variations on existing approaches
• The goal is adjacent or breakthrough innovation (not incremental improvement)
• Cross-domain inspiration is explicitly desired by A2.5 portfolio balance targets

Do NOT use when:

• The problem is so novel that no meaningful analogies exist—use Structured Brainstorming for open exploration first
• Volume is the primary goal and time is very short—use Brainwriting 6-3-5 instead

Sample Size and Duration

Participants: 4–12

Duration: 60–90 minutes (or split: 30 minutes abstraction + domain ID, homework for research, 30 minutes transfer in next session)

Expected output: 10–30 analogy-derived ideas

Prerequisites

• Validated Need Brief (core problem clearly articulated at a functional level, not domain-specific language)
• Facilitator with cross-industry awareness or pre-prepared analogy library
• 4–12 participants (ideally including at least one person from outside the primary domain)
• 60–90 minutes

Complete Procedure

Step 1: Abstract the Problem (10 minutes)

Restate the problem at a functional level, stripping away domain-specific language:

• Domain-specific: “How might we reduce customer churn in B2B SaaS?”
• Functional abstraction: “How might we detect declining engagement early enough to intervene before a relationship ends?”

The functional statement enables mapping to distant domains where the same relational structure exists.

Step 2: Identify Analogous Domains (15 minutes)

Brainstorm domains where a structurally similar problem has been solved:

• “Where else does someone need to detect declining engagement before it's too late?”
• Examples: healthcare (patient adherence monitoring), education (student dropout prediction), agriculture (crop disease early detection), aviation (predictive maintenance), personal relationships (counselling early-warning signs)

Aim for 5–10 analogous domains. Prioritise both near-field (adjacent industries) and far-field (entirely different contexts) for a mix of feasible and novel inspiration.

Step 3: Research Analogue Solutions (15–20 minutes)

For each selected domain, identify how the analogous problem is actually solved:

• Healthcare: wearable sensors + threshold alerts + nurse follow-up
• Aviation: sensor fusion + degradation curves + scheduled intervention windows
• Education: learning management system engagement scoring + automated nudges + advisor outreach

Pre-research by the facilitator accelerates this step. Alternatively, run it as homework between sessions.

Step 4: Transfer Principles (20–30 minutes)

For each analogue solution, ask: “What would this look like applied to our problem?”

• Aviation predictive maintenance → “What if we built a customer health degradation curve that predicts churn 60 days out, like an engine degradation model?”
• Education nudges → “What if the product sent personalised micro-learning nudges when feature adoption stalls, like a learning platform nudges inactive students?”

Generate 2–4 concept ideas per analogue. Capture each as a raw idea with source domain noted.

Step 5: Harvest (5 minutes)

Collect all analogy-derived ideas. Assign unique IDs. Tag each with its source domain for traceability.

Quality Criteria

1. Problem abstracted to functional level (no domain-specific jargon in the core statement)
2. ≥5 analogous domains identified
3. Both near-field and far-field analogues included
4. ≥2 concept ideas per analogue (≥10 ideas total)
5. Each idea tagged with source domain

Theoretical Foundation

Seminal references:

• : Established the structure-mapping theory of analogy: effective analogical transfer requires mapping relational structure (how elements interact) rather than surface features (what elements look like). This principle governs Step~2 of the procedure below.
• : Studied IDEO and other innovation firms, demonstrating that “technology brokering”—systematically transferring solutions across industries—is a primary source of breakthrough innovation. Organisations that expose teams to diverse domains generate more novel solutions than domain-specialist teams.

Contemporary references:

• : Empirical study of 84 product development projects finding that teams using far-field analogies (from distant domains) produced significantly more novel concepts than teams using near-field analogies or no analogies, though near-field analogies produced more immediately feasible concepts. Recommends combining both.

Challenges and Solutions

Challenge 1: Surface-Level Analogies

• Symptoms: “Uber for X” without understanding what structural principle Uber represents
• Solution: Enforce the abstraction step (Step~1). Ask: “What mechanism makes Uber work, and does that mechanism apply here?” Distinguish relational mapping from superficial branding.

Challenge 2: Inability to Find Analogues

• Symptoms: Team cannot think beyond their own industry
• Solution: Provide a stimulus list of 10–15 diverse domains (nature, military, hospitality, gaming, logistics, healthcare, education, finance, agriculture, entertainment). For each, ask: “Does anything like our problem exist here?”

Relationship to Other Methods

Analogical Ideation complements:

• Structured Brainstorming (the referenced method): Use brainstorming for open volume, then analogical ideation for cross-domain stretch
• SCAMPER (the referenced method The “Adapt” operator in SCAMPER is a lightweight version of analogical transfer; use full analogical ideation when deeper cross-domain exploration is needed
• Affinity Mapping (the referenced method): Analogy-derived ideas feed into A2.1 Step~3 clustering