DOMAIN:MARKETING — CONTENT IMPACT SCORING¶
OWNER: tjarda ALSO_USED_BY: rick (copy awareness), felice (visual awareness), valentijn (strategic input) UPDATED: 2026-04-03 SCOPE: all GE marketing content — score BEFORE producing, kill low-impact ideas early
PURPOSE¶
RULE: every content idea must score above threshold before entering content calendar RULE: score before drafting — don't waste creative cycles on weak ideas RULE: killed ideas are logged, not deleted — audience needs change RULE: impact scoring applies to ALL channels (LinkedIn, X, blog, YouTube, PR pitches)
IMPACT_FORMULA¶
DIMENSION_DEFINITIONS¶
| Dimension | Weight | 90-100 | 60-80 | 30-50 | 0-20 |
|---|---|---|---|---|---|
| Relevance | 0.30 | Exact match to stated audience pain point | Related to audience domain, not direct pain | Tangentially connected | No audience connection |
| Novelty | 0.25 | Never published — new data, new angle, new result | Known topic, genuinely fresh perspective | Known topic, minor twist | Rehash of existing content |
| Utility | 0.25 | Reader can act on this TODAY — steps, templates, tools | Directional guidance, needs adaptation | General principles, not actionable | Pure opinion or commentary |
| Shareability | 0.20 | "You need to read this" — forwarded to colleagues | "Interesting" — bookmarked but not shared | "Nice" — read and forgotten | No sharing trigger |
THRESHOLDS¶
| Score | Action | What happens |
|---|---|---|
| 85-100 | PRIORITY | Fast-track to production. Rick/Felice get the brief this sprint. |
| 70-84 | PROCEED | Schedule normally. Standard creative pipeline. |
| 50-69 | REWORK | Improve weakest dimension. ONE rework attempt. Still below 70 → KILL. |
| 0-49 | KILL | Do not produce. Log to killed-ideas backlog with reason. |
RULE: reworked content gets ONE chance to improve above 70 — no infinite loops RULE: killed ideas can be resurrected IF audience/market context changes (note the trigger) RULE: track KILL rate — if >50% of ideas are killed, review content strategy alignment
SCORING_PROTOCOL¶
STEP 1: state the content idea in one sentence (forces clarity) STEP 2: identify the primary target audience segment STEP 3: score each dimension 0-100 with 2-3 sentence justification STEP 4: compute weighted total STEP 5: apply threshold decision STEP 6: IF REWORK THEN specify which dimension(s) to improve and how STEP 7: log score to content calendar entry (even for KILL — track the decision)
TARGET_AUDIENCES (for Relevance scoring)¶
| Segment | Pain points | Content that scores 90+ on Relevance |
|---|---|---|
| SME business owners | Can't afford custom software, don't trust agencies, burned by failed projects | How GE delivers at 10% cost with proof. Real client results. |
| CTOs / Tech leads | AI hype fatigue, skeptical of agent quality, need to evaluate technically | Architecture deep-dives, security audits, real code examples |
| Tech journalists | Need a story, need a hook, need a quote, need to be first | Unprecedented scale (59 agents), EU angle, contrarian position |
| AI/Dev community | Want to learn, want to build, want to compare approaches | Technical how-tos, open patterns, honest failure stories |
EXAMPLES¶
PRIORITY Example (Score: 89)¶
Idea: "How 59 AI agents pass ISO 27001 — the architecture behind enterprise compliance at agent scale" Audience: CTOs / Tech leads
| Dimension | Score | Justification |
|---|---|---|
| Relevance | 92 | Direct pain point — CTOs evaluating AI tools need to know about compliance. ISO 27001 is table stakes for enterprise. |
| Novelty | 95 | Nobody has published how a multi-agent AI system achieves ISO 27001. This is genuinely first. |
| Utility | 80 | Architectural patterns they can study and adapt. Not step-by-step for their stack, but deeply informative. |
| Shareability | 88 | "You need to see how they did this" — Slack-worthy for any engineering team evaluating AI tooling. |
IMPACT: (92×0.30) + (95×0.25) + (80×0.25) + (88×0.20) = 27.6 + 23.75 + 20.0 + 17.6 = 89.0 → PRIORITY
PROCEED Example (Score: 74)¶
Idea: "Our wiki brain: how GE agents learn from every project" Audience: AI/Dev community
| Dimension | Score | Justification |
|---|---|---|
| Relevance | 75 | Interesting to builders but not a direct pain point — more curiosity than need. |
| Novelty | 80 | Self-learning agent systems are discussed but rarely shown with real implementation detail. |
| Utility | 65 | Conceptual — readers learn the approach but can't directly replicate without GE's stack. |
| Shareability | 72 | "Cool" but not urgent. Developers bookmark it, maybe share in a thread. |
IMPACT: (75×0.30) + (80×0.25) + (65×0.25) + (72×0.20) = 22.5 + 20.0 + 16.25 + 14.4 = 73.2 → PROCEED
KILL Example (Score: 42)¶
Idea: "Why AI is the future of software development" Audience: General
| Dimension | Score | Justification |
|---|---|---|
| Relevance | 50 | Too generic — doesn't address any specific segment's pain. |
| Novelty | 10 | Published 10,000 times. Zero new information. |
| Utility | 40 | No actionable insight — "AI is great" is not a recommendation. |
| Shareability | 30 | Nobody shares generic AI takes. Infinite supply of this content already. |
IMPACT: (50×0.30) + (10×0.25) + (40×0.25) + (30×0.20) = 15.0 + 2.5 + 10.0 + 6.0 = 33.5 → KILL Logged reason: Zero novelty, zero utility. Resurrect ONLY if we have specific data to anchor it (e.g., "We measured: 59 agents ship 3x faster than 12 human devs on identical specs").
CONTENT_CALENDAR_INTEGRATION¶
RULE: every content calendar entry includes impact score in metadata RULE: calendar sorted by impact score within each week (PRIORITY first) RULE: if calendar is full and a PRIORITY idea arrives, it bumps the lowest-scoring PROCEED item RULE: monthly review — compare predicted impact scores against actual engagement metrics RULE: calibrate scoring quarterly — if 70-score content outperforms 85-score content consistently, dimensions need reweighting
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