Janady13/AI_Brain / daemon/datasets/performance_memory.json

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Field	Recorded value
Source type	GitHub source
Original record	Janady13/AI_Brain / daemon/datasets/performance_memory.json
Date	Not specified in source
Repository	Janady13/AI_Brain
Revision	11aa50ef5d7692e8abd1b8d74ae31c5c29201b14
Branch	codex/build-online-github-ani-irqa7y
Path or archive member	daemon/datasets/performance_memory.json
Original SHA-256	d3dad8654bccbbfc29107e54f381715160617ffd6279dc834cf8638d127d0483
Git blob SHA	c0677aba7f6487ab5e1bc7a772292e48dab57c08
Reading-copy SHA-256	d3dad8654bccbbfc29107e54f381715160617ffd6279dc834cf8638d127d0483

Original source ↗

Every recorded occurrence

These 21 records contain identical original source bytes. Their paths and revisions remain part of the archive.

Original record	Branch or archive	Revision / identifier	Original location
Janady13/AI_Brain / daemon/datasets/performance_memory.json	codex/build-online-github-ani-irqa7y	11aa50ef5d7692e8abd1b8d74ae31c5c29201b14	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	codex/build-online-github-ani-hr4y0z	366ab848896ae49159cfb3f6c201b8c5a731e9c8	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	codex/build-online-github-ani-i3cigl	3a6e002311c48c27ffd41ef4a2a31c63947128cd	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	codex/build-online-github-ani-hrtsoc	50285aff347685f043e7a72e4d72891eebaa6cd6	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	codex/build-online-github-ani-7v4kug	5cd0bbf744b0aee9445913337e852889d9a8098e	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	copilot/convert-python-files-to-csharp	6d0767aad08781eb5c4e1eb14a42ec92c437be78	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	copilot/vscode1760283895982	9e1c1a5b171addb5bb4a83b2ea7dede50c1cc81e	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	codex/build-online-github-ani-jf5olo	c6653627c453ab428d665226167fa5f4aa05e9a5	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	feature/seeder-engine-tests	cdb497ece2d1a770ca8c83eb1de8923cfaa9eceb	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	codex/build-online-github-ani-7nmb21	d0f50071c85cddaaebbf9c4dd1313bd7676986da	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	codex/build-online-github-ani-0etkns	e250b19d446bdffd542c9eea31cead12e30800d6	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	codex/build-online-github-ani-hr4y0z	366ab848896ae49159cfb3f6c201b8c5a731e9c8	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	codex/build-online-github-ani-irqa7y	11aa50ef5d7692e8abd1b8d74ae31c5c29201b14	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	codex/build-online-github-ani-i3cigl	3a6e002311c48c27ffd41ef4a2a31c63947128cd	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	copilot/convert-python-files-to-csharp	6d0767aad08781eb5c4e1eb14a42ec92c437be78	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	codex/build-online-github-ani-7v4kug	5cd0bbf744b0aee9445913337e852889d9a8098e	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	copilot/vscode1760283895982	9e1c1a5b171addb5bb4a83b2ea7dede50c1cc81e	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	codex/build-online-github-ani-7nmb21	d0f50071c85cddaaebbf9c4dd1313bd7676986da	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	feature/seeder-engine-tests	cdb497ece2d1a770ca8c83eb1de8923cfaa9eceb	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	codex/build-online-github-ani-jf5olo	c6653627c453ab428d665226167fa5f4aa05e9a5	Open original
Janady13/AI_Brain / daemon/datasets/performance_memory.json	codex/build-online-github-ani-hrtsoc	50285aff347685f043e7a72e4d72891eebaa6cd6	Open original

Recovered source transcript

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[
  {
    "play_name": "Enterprise AI Pipeline Recovery",
    "stage": "conversion",
    "audience_focus": "enterprise ai buyers",
    "channel": "account based ads",
    "tags": ["enterprise", "ai", "pipeline", "conversion", "abm", "retargeting"],
    "summary": "Scaled an ABM retargeting loop that merged first-party usage signals with dynamic creative to unstick stalled enterprise evaluations.",
    "wins": [
      "Recovered 31 stalled enterprise evaluations within 2 weeks",
      "Lifted sales-qualified opportunity volume by 22%"
    ],
    "losses": [
      "Creative variants fatigued without weekly refresh cadence"
    ],
    "next_actions": [
      "Automate creative refresh sequencing with the automation sprint loops",
      "Align SDR outreach cadences to marketing engagement spikes"
    ],
    "kpi_focus": ["sales qualified opportunities", "opportunity velocity"],
    "confidence": 0.86,
    "source": "performance_memory",
    "notes": "Validated with 6 enterprise cohorts across AI infrastructure buyers."
  },
  {
    "play_name": "Founder-Led Launch Momentum",
    "stage": "awareness",
    "audience_focus": "startup founders",
    "channel": "social amplification",
    "tags": ["founder", "awareness", "launch", "social", "community", "plg"],
    "summary": "Activated a social-first launch sprint that paired founder narratives with community seeding and curated influencer syndication.",
    "wins": [
      "Generated 18k signups in the first month",
      "Earned placements in 12 tiered newsletters without paid spend"
    ],
    "losses": [
      "Community moderators flagged promotional tone until value hooks were front-loaded"
    ],
    "next_actions": [
      "Blend social proof snippets into top-of-funnel creative",
      "Automate warm outreach to partner communities with curated toolkits"
    ],
    "kpi_focus": ["top-of-funnel signups", "share of voice"],
    "confidence": 0.8,
    "source": "performance_memory",
    "notes": "Repeatable for PLG launches where founders participate in amplification."
  },
  {
    "play_name": "Mid-Market Demo Accelerators",
    "stage": "consideration",
    "audience_focus": "mid-market operations leaders",
    "channel": "webinar nurture",
    "tags": ["midmarket", "operations", "webinar", "nurture", "demo", "consideration"],
    "summary": "Stacked intent webinars, on-demand workshops, and triggered case study flows to compress the mid-market evaluation window.",
    "wins": [
      "Increased marketing qualified leads to demo conversion from 19% to 34%",
      "Cut average sales cycle length by 11 days"
    ],
    "losses": [
      "Attendance decay after third webinar required diversified formats"
    ],
    "next_actions": [
      "Rotate workshop formats every two weeks to sustain attendance",
      "Push interactive product tours immediately after webinar hand-raise"
    ],
    "kpi_focus": ["demo conversion", "sales cycle velocity"],
    "confidence": 0.83,
    "source": "performance_memory",
    "notes": "Works best when supported by strong sales enablement cadences."
  },
  {
    "play_name": "Customer Expansion Control Tower",
    "stage": "retention",
    "audience_focus": "customer success leaders",
    "channel": "lifecycle automation",
    "tags": ["customer", "retention", "expansion", "automation", "lifecycle", "advocacy"],
    "summary": "Implemented lifecycle automations that fused product telemetry, advocacy prompts, and success manager alerts to increase expansion rates.",
    "wins": [
      "Net revenue retention climbed from 108% to 124% in one quarter",
      "Generated 47 peer reviews powering downstream acquisition campaigns"
    ],
    "losses": [
      "Over-automation risked impersonal outreach until human checkpoints were inserted"
    ],
    "next_actions": [
      "Insert success manager video touches for top value milestones",
      "Expand advocacy prompts into partner marketplaces"
    ],
    "kpi_focus": ["net revenue retention", "expansion bookings"],
    "confidence": 0.9,
    "source": "performance_memory",
    "notes": "Requires coordinated product usage data access to avoid stale triggers."
  },
  {
    "play_name": "AI Content Flywheel",
    "stage": "awareness",
    "audience_focus": "ai practitioners",
    "channel": "content syndication",
    "tags": ["ai", "content", "awareness", "syndication", "thought leadership"],
    "summary": "Stitched together an AI-focused content flywheel mixing technical deep dives, prompt libraries, and partner distribution.",
    "wins": [
      "Organic traffic compounded 3.1x over 90 days",
      "Captured 4 enterprise inbound opportunities from syndicated webinars"
    ],
    "losses": [
      "Required weekly expert contributions to maintain credibility"
    ],
    "next_actions": [
      "Automate prompt library refreshes with contributor sourcing",
      "Bundle downloadable assets with Stripe monetization experiments"
    ],
    "kpi_focus": ["organic traffic", "content engagement"],
    "confidence": 0.78,
    "source": "performance_memory",
    "notes": "Pair with RAG recommendations focused on technical storytelling."
  },
  {
    "play_name": "Revenue Portfolio Balancing",
    "stage": "conversion",
    "audience_focus": "revops leadership",
    "channel": "multi-channel orchestration",
    "tags": ["revops", "conversion", "budget", "portfolio", "multi-channel"],
    "summary": "Deployed a budget balancing program that reallocated spend weekly based on blended ROI variance across paid social, search, and partner channels.",
    "wins": [
      "Improved blended ROI from 2.4 to 3.1 within a month",
      "Unlocked 14% incremental pipeline without increasing total spend"
    ],
    "losses": [
      "Search CPC spikes required tighter guardrails on bid automation"
    ],
    "next_actions": [
      "Feed budget allocator recommendations into finance cadences",
      "Set alerting for ROI variance beyond 15% week-over-week"
    ],
    "kpi_focus": ["blended roi", "pipeline contribution"],
    "confidence": 0.88,
    "source": "performance_memory",
    "notes": "Pairs with budget optimizer outputs for closed-loop governance."
  }
]