# Enterprise Architecture for Social Growth: Data, Models, and Governance

High-performing social programs aren’t accidental. They combine a clear content ontology, streaming data pipelines, pragmatic scoring models, and operations governance that turns signals into repeatable output. This blueprint is built for teams that need reliability, scale, and revenue accountability.

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## 1) Content Ontology and Topic Graph
Design an ontology that maps business value to content primitives so creation, labeling, and measurement stay consistent.

- Business pillars → Topics → Subtopics → Proof assets (case/data/demo)
- Personas × Journey stages matrix (Problem → Solution → Proof → Purchase)
- Content structures: Thread, Long-form, Visual explainer, Checklist, Case mini
- Metadata: Intent (educate/convert/support), CTA type (soft/hard), Evidence (stat/case/visual)

Outcome: every artifact carries consistent tags for routing, measurement, and reuse.

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## 2) Data Pipeline (Minimal Viable Warehouse)
- Ingest: platform APIs/webhooks (X, LinkedIn), site analytics, UTM resolver
- Stream: queue → durable storage (e.g., Kafka → S3/GCS) → warehouse (Snowflake/BigQuery)
- Transform: dbt models to standardize posts, authors, sessions, and conversions
- Metrics layer: semantic definitions (Looker/Mode/Transform) for non-duplicated KPIs
- Reverse-ETL: push scores and lists back to engagement tools (Soshi/SuperX/CRM)

Key tables (normalized):
- dim_author, dim_topic, dim_asset, fct_post, fct_interaction (like/comment/reply/share), fct_session, fct_conversion

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## 3) Scoring Models (Operational, Not Academic)
Keep models simple, explainable, and controllable by operators.

- Author Priority Score (APS):
  - Inputs: role seniority, ICP match, historical responsiveness, network centrality
  - Use: rank listening queues and human-in-the-loop follow-ups
- Thread Opportunity Score (TOS):
  - Inputs: early velocity (likes/replies in first 60–120m), topical proximity, author priority, time-to-next-peak window
  - Use: trigger clarifying/expansion comments and asset drops
- Content Structure Fitness (CSF):
  - Inputs: structure type, media type, opening hook profile, CTA type
  - Use: pick format for next post under each topic

Implementation note: write models as dbt macros or SQL UDFs tied to the metrics layer; version changes and change logs.

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## 4) Operating Model and Cadence
- Daily (30–45m):
  - Listen → Segment: Priority authors, Topic matches, Maintenance threads
  - Act: 6–10 replies using snippet+evidence; schedule 1–2 expansions on rising posts
  - Capture: promote 1–2 replies to reusable snippets with tags
- Weekly (60–90m):
  - Review structure × time-of-day charts; retire bottom quartile, promote winners
  - Update ontology: new subtopics, new proof assets, revised CTAs
  - Sync with Sales/CSM: feedback on lead quality and thread notes
- Monthly:
  - Attribution review (model stability, lag effects)
  - Governance check: risk controls, rate limits, disclosure policies

RACI: Marketing owns ontology and cadence; RevOps owns data/attribution; Social Ops owns queues/snippets; Leadership reviews KPIs.

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## 5) Attribution and Measurement (Pragmatic)
- First-touch vs. last-touch vs. position-based (40/20/40) for social-influenced deals
- Assisted conversion flags when social session occurs within 7–14 days pre-opportunity
- Content contribution: allocate credit to structure and topic, not only to single posts
- Guardrails: do not optimize solely to vanity metrics; include lead quality and sales cycle velocity

Recommended KPIs (definitions in the metrics layer):
- Reply Like→Reply ratio ≥ 0.35
- Profile Click Rate from replies ≥ 2.0%
- Topic-level assisted conversions MoM +15% with stable CAC
- Lead Acceptance Rate from social ≥ baseline other channels

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## 6) Risk and Compliance Controls
- Human-in-the-loop confirmation for outbound replies from automation
- Rate limiting and phrasing randomization to lower platform risk
- Privacy: no sensitive data; handle screenshots with redaction policy
- Disclosure: mark automated/scheduled where required

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## 7) Implementation Checklist
- [ ] Define ontology and persona × journey matrix
- [ ] Stand up warehouse tables + dbt transforms
- [ ] Ship APS/TOS/CSF as versioned UDFs
- [ ] Configure listening queues and thresholds
- [ ] Create 20–30 tagged snippets with evidence placeholders
- [ ] Schedule weekly review and governance rituals

Bottom line: treat social as a product surface. With a documented ontology, explainable scores, and disciplined operations, growth becomes a controllable system—not an art project.

A practitioner’s blueprint for building an enterprise-grade social growth system: content ontology, data pipelines, engagement scoring, operations governance, and measurable KPIs tied to revenue outcomes.

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