Setting the file. One moment.
Subchapter 11.7
references/sector-riders.mdMarkdown11 KBView on GitHub
Six sector-specific overlays for the insurgent playbook. A rider does not replace the function-first routing in Stage 4 — it layers on top of it, adjusting archetype defaults, surfacing the sector’s common failure mode, and biasing channel weighting.
Stage 1 captures the user’s sector. Stage 4 opens this file and applies the matching rider after the function-first decision. Stage 5 uses the rider’s failure-mode note as a standing warning in the anti-vanity dashboard.
If the user’s sector does not fit one of the six below, pick the closest rider and flag the mismatch in the Assumptions table. Do not invent a rider on the fly — the riders exist because they encode real structural facts about how demand, distribution, and trust compound in each sector.
Structural advantage: the room is the product. Attendees meet each other, form the cohort bond, and come back next year (or send their colleagues). Referral flywheels compound through the attendee network, not through marketing spend. Recurring cadence — same time each quarter, same format — beats one-shot launches because it builds category reliability.
Primary channel fit: Tier 1 (founder presence + community nodes + word-of-mouth referrals) with a heavy weighting on community. An active Slack / Discord / WhatsApp for past and prospective attendees is the single highest-leverage channel in this sector.
Archetype defaults: founder-story arc, community-build, referral flywheel, coalition plays with adjacent teachers/operators. Earned-media works here when anchored to a specific session or a teaching artifact (curriculum, post-mortem, open notebook).
Common failure mode: selling information in a world where information is free. The cohort-based course competing on “learn X” loses to a free YouTube playlist on the same topic. What you sell is the room: the peers, the accountability, the direct feedback, the alumni network. Market the room, not the curriculum.
Metric emphasis: applications-to-admits ratio, cohort-to-alumni network growth, referral % of new applicants, repeat attendee rate, post-cohort NPS that actually translates to referrals (not vanity NPS).
Structural advantage: buyers are findable on LinkedIn, specific titles hold purchasing authority, and a single champion can drive multi-seat expansion. Founder LinkedIn presence is the highest-leverage distribution channel in B2B — parasocial trust with the founder compresses the sales cycle by weeks.
Primary channel fit: Tier 1 (founder LinkedIn + SEO demand capture on high-intent non-brand queries) with Tier 2 (podcast guesting on operator/eng shows, community nodes for the buyer persona). Paid social works only as retargeting of warm audiences.
Archetype defaults: founder-story arc (CEO or technical founder in public), earned-media through in-depth post-mortems and changelogs, category-insertion vs. category-creation decision (joining an existing category beats creating a new one 90% of the time for a challenger).
Common failure mode: MQL theatre. Buying top-of-funnel clicks that generate
form-fills the sales team calls “leads,” chasing platform-reported ROAS,
confusing activity for pipeline. The fix: track incremental revenue lift via
Template 1 or 4 (references/lift-test-templates.md), not MQL volume.
Metric emphasis: activation rate (signup → first value), expansion revenue from existing accounts, pipeline generated from founder content (UTM-tagged), demo-to-close rate, NOT: website sessions, MQL count, attributed-but-non- incremental ROAS.
Structural advantage: mission is inherently shareable. Supporters want to advocate publicly; the friction is giving them the asset, the ask, and the moment. A mission with a named, specific beneficiary outperforms a mission with an abstract cause — story > statistic.
Primary channel fit: Tier 1 (volunteer networks, chapter model, email list) and Tier 2 (earned media on specific beneficiary stories, coalition with aligned orgs). Paid reserved for donor retargeting only — never cold acquisition; the LTV math does not pencil out on cold paid for most causes.
Archetype defaults: coalition play (formal alliance with 3–5 aligned orgs), founder-story arc (executive director or named beneficiary), earned-media on accountability moments (government action, corporate abuse, crisis response), volunteer flywheel (every volunteer recruited = two more likely in their network).
Common failure mode: donor-speak instead of story. Annual reports written in foundation jargon, pitches anchored to organizational outputs (“we delivered 2,400 program hours”) instead of named human outcomes (“Fatima is in school because…”). The fix: lead with one named person, one specific moment, one concrete ask. No acronyms in the first paragraph.
Metric emphasis: donor retention rate, average gift size growth, volunteer-to-donor conversion, advocate-to-recurring-supporter rate, earned- media placements that mention a named beneficiary, NOT: impression counts, Facebook page likes, email list size without engagement segmentation.
Structural advantage: user-generated content (UGC) is a compounding asset class. Every customer post is a proof point for the next buyer. Repeat purchase economics dominate — a brand that gets retention right can spend 3–5× more on acquisition than a brand that doesn’t.
Primary channel fit: Tier 1 (organic creator partnerships with 10–50k follower accounts in the niche, UGC programs, email/SMS list) and Tier 2 (Reddit / subreddit presence, podcast sponsorships with proven hosts). Tier 3 paid social only after an organic winner exists (24–48h traction gate).
Archetype defaults: founder-story arc for category-creating or mission-driven brands, referral flywheel (every 3rd customer gets a referral link; measure incremental referral lift, not gross referrals), earned-media stunt for launches (one newsworthy action, not a press release).
Common failure mode: paying influencers for reach without organic proof. Buying a creator’s audience before you have any organic creator content gives you nothing durable; when the paid engagement ends, so does the visibility. The fix: seed product to 50 creators first (no ask), let the organic content accumulate, then buy usage rights + amplify the 3–5 pieces that worked organically.
Metric emphasis: 90-day repeat purchase rate, organic UGC volume and sentiment, contribution margin per acquisition channel (post-refunds, post-shipping), retention cohort curves, NOT: ROAS from platform attribution, reach numbers, vanity engagement rate.
Structural advantage: ground-game math. A volunteer door-knock is roughly 45× more persuasive than a paid impression in the same household. Turnout in the last 72 hours is largely a function of organized human contact, not ad spend. Unified message discipline beats fragmented messaging even when the unified version is less exciting.
Primary channel fit: Tier 1 (volunteer networks, chapter / precinct structure, rally attendance, founder/candidate presence on TikTok + Facebook Lives from unscripted locations) and Tier 2 (counter-media in information-dark zones — small-town newspapers, regional radio, podcast appearances where the opposition is absent). Paid heavily constrained by EU/US rules and by the diminishing-returns curve at high saturation.
Archetype defaults: founder-story arc (candidate’s own lived experience, Ganz Self/Us/Now), counter-narrative (name the incumbent’s saturation tactic, refuse to match it), coalition play (formal multi-party or multi-org alliance), earned-media stunt tied to a policy accountability moment.
Common failure mode: fragmented opposition. Three small parties running three small campaigns against one consolidated incumbent lose even if their combined vote share is larger. The fix: consolidation beats differentiation in a threshold-rewarding system; sometimes the right campaign is a coalition campaign, not your party’s campaign.
Metric emphasis: registered-voter contact rate, volunteer-to-active-volunteer conversion, turnout in targeted precincts vs. control precincts, unique sharers of organic campaign content in the target geography, NOT: national ad reach, national poll movement (too lagged, too noisy), social-media follower count.
Structural advantage: the person is the product. Consistency of voice and format over time builds a trust moat that no agency or competitor can manufacture — they cannot fake your specific point of view, your specific vocabulary, your specific takes. One niche, held for 2+ years, compounds.
Primary channel fit: Tier 1 (one primary platform where the audience actually is — not five half-tended accounts — plus an email list as the platform-change-proof asset). Tier 2 (podcast guesting, substack/newsletter cross-posts) once Tier 1 is compounding.
Archetype defaults: founder-story arc is the entire playbook. Every piece of content should answer “why does this person have a point of view on this” better than a generic content farm can. Earned-media via niche-specific publications; paid only to amplify a piece with proven organic lift.
Common failure mode: niching too late, or never. “I write about technology and leadership and design and productivity” has no audience. “I write about why SaaS pricing pages lie, every Thursday” has an audience. The fix: pick a niche so narrow it feels uncomfortable for 12 months; widen only after the niche audience is saturated.
Metric emphasis: email list size with open rate >35%, repeat readers / listeners / viewers (7-day and 30-day return rate), inbound DMs / invitations for paid work, revenue per newsletter subscriber, NOT: follower count, post likes, impressions.
Every rider above implies a default archetype ordering in
references/campaign-archetypes.md. When the rider and the user’s selected
concept conflict (e.g., a personal brand picking a coalition play), surface the
conflict and ask the user to confirm — the rider is the default, not the rule.
If the user’s sector is genuinely outside the six — industrial B2B manufacturing, deep research, regulated healthcare, heavy industry — pick the closest rider and flag the mismatch in the Assumptions table. Note which structural assumptions from the chosen rider do not apply so the user can push back on those specific points. Do not attempt to synthesize a new rider in-session; the rider encodes structural truths that need real evidence, not improvisation.