Multi Source Signal Synthesiser

Multi-Source Signal Synthesiser Skill

Purpose

Reconcile user signals from multiple sources — interviews, support tickets, NPS, app reviews, analytics, surveys, synthetic research — into a unified, weighted insight brief that surfaces the underlying need rather than the surface-level request.

Pipeline Context

Primary use: Step 6 (/validate-problems) — when combining:

• Analytics data (from analyst)
• Survey results (from research)
• Interview notes (from PM)
• Synthetic interviews (from step 2)

Also useful at any step where multiple data sources need reconciliation.

Evidence Typing

Every signal must be tagged with evidence type and confidence score:

Conflict resolution: When REAL contradicts SYNTHETIC, REAL wins. Document the delta — the gap between what synthetic research predicted and what real data showed is itself an insight.

Source Weighting (default — adapt to your context)

• Direct research (interviews, usability tests): weight 5
• Analytics data (funnels, cohorts, events): weight 5
• Support tickets (unprompted pain signals): weight 4
• Survey results (structured quantitative): weight 4
• NPS verbatims: weight 3
• App store reviews: weight 2
• Sales call summaries (filtered through sales lens): weight 2
• Synthetic research (AI-generated): weight 1
• Anecdote or single report: weight 1

Process

1. Accept inputs from any combination of source types
2. Tag each signal by source, apply weight, and assign evidence type + confidence
3. CONVERGENCE: same underlying need appearing across 3+ sources
   • Calculate combined confidence: highest individual confidence × (1 + 0.1 × number of confirming sources)
   • Cap at 1.0
4. DIVERGENCE: contradictory signals suggesting user segmentation
   • Don't average away disagreement — it usually means different user segments
5. FREQUENCY RANKING: count how many independent sources mention each insight
   • "N out of M sources mention this" (inspired by frequency-based evidence ranking)
6. Distinguish surface request from underlying need (e.g., "faster export" may mean "I don't trust the data will be there when I need it")
7. Produce ranked insights by weighted frequency

Output Format

User Signal Synthesis — [Date / Period]

Sources included: [list with evidence type for each] Total signals processed: [n] Evidence quality: [% REAL / % SYNTHETIC / % INFERRED]

Insight 1: [Underlying need, not feature request]

• Frequency: [N/M sources] — [list which sources]
• Evidence type: [REAL/SYNTHETIC/INFERRED] combined confidence: [0.0-1.0]
• Evidence:
  • Analytics: [specific data point] (REAL, 0.8)
  • Survey: [specific finding] (REAL, 0.85)
  • Interviews: [quote or pattern] (REAL, 0.9)
  • Synthetic: [what AI predicted] (SYNTHETIC, 0.3)
• Conflicting signals: [Any contradicting evidence and how to interpret it]
• REAL vs SYNTHETIC delta: [Where synthetic research got it wrong/right]
• Product implication: [Specific, not generic]

[Repeat for top 3-5 insights, ordered by combined confidence × frequency]

Divergent Signals (Possible Segmentation)

[Where user groups appear to have genuinely different needs]

• Segment A says X (evidence: ...) while Segment B says Y (evidence: ...)
• Implication: [consider separate solutions or prioritize one segment]

What the Data Does NOT Tell Us

[Gaps that require further research before acting]

• [Gap 1]: would need [research method] to resolve
• [Gap 2]: low confidence because only SYNTHETIC evidence exists

Confidence Summary

Source: lenar-amirov/product-pipeline-public — distributed by TomeVault.