Tracking Outcomes

After publishing content, feed the learning loop so the system can measure what worked and calibrate scoring for future recommendations. The outcome infrastructure is live — this guide walks through the three layers: real-time tracking reads, snapshot-based diffs over time, and outcome event reporting for the learning loop.

The three layers

All three work independently — you can mix and match. The recommended pattern after publishing: record an outcome event, capture a baseline snapshot, then diff against it at 7/30/90 days.

Step 1 — record the outcome event

When content is published (or rejected, or observed), log it via the outcome event endpoint. This is the input to the learning loop calibration.

curl -s -X POST \
  -H "X-API-Key: $BM_API_KEY" \
  -H "Content-Type: application/json" \
  "https://api.boringmarketing.com/track/outcomes/report" \
  -d '{
    "brand_id": "'$BRAND_ID'",
    "recommendation_id": "opp-uuid",
    "brief_id": "brief-uuid",
    "event_type": "completed",
    "notes": "Published at https://yourdomain.com/article"
  }'

Supported event_type values:

Step 2 — capture a baseline snapshot

Snapshots are stateful point-in-time captures of a brand's rankings and citations. They are used to compute deltas later.

curl -s -X POST \
  -H "X-API-Key: $BM_API_KEY" \
  "https://api.boringmarketing.com/track/brands/$BRAND_ID/snapshot"

Response:

{
  "brand_id": "brand-uuid",
  "snapshot_id": "snap-uuid",
  "created_at": "2026-04-07T10:00:00Z",
  "rankings_count": 127,
  "citations": {
    "chat_gpt": 24,
    "google": 10
  }
}

Each snapshot captures up to 200 ranking keywords + per-platform citation counts. Snapshots are cheap — capture one before publishing any new content.

Step 3 — compute changes against an earlier snapshot

At 7 days (or any interval), compute the delta between the latest snapshot and an earlier one:

# Capture another snapshot first
curl -s -X POST \
  -H "X-API-Key: $BM_API_KEY" \
  "https://api.boringmarketing.com/track/brands/$BRAND_ID/snapshot"

# Then compute changes vs the original baseline
curl -s \
  -H "X-API-Key: $BM_API_KEY" \
  "https://api.boringmarketing.com/track/brands/$BRAND_ID/changes?since=2026-04-07T10:00:00Z"

The since parameter takes any ISO 8601 datetime. The endpoint finds the most recent snapshot at or before that time and diffs it against the latest snapshot. Returns structured deltas: ranking improvements, ranking declines, citation gains, and citation losses.

Milestone tracking pattern

The canonical pattern for 7/30/90-day observations:

1. When you publish, call POST /track/outcomes/report with event_type: "completed" and POST /track/brands/{id}/snapshot to capture a baseline.
2. 7 days later: capture a new snapshot, call GET /track/brands/{id}/changes?since=<publish_timestamp>, then call POST /track/outcomes/report with event_type: "observed_7d" and the observed deltas in notes.
3. Repeat at 30 and 90 days.

You can schedule the observations via schedule, a cron, or your agent's scheduler — the API does not auto-fire them.

Live reads (no history)

If you just want a current snapshot without saving it:

Google rankings

curl -s \
  -H "X-API-Key: $BM_API_KEY" \
  "https://api.boringmarketing.com/track/rankings?brand_id=$BRAND_ID&limit=100"

LLM citations

curl -s \
  -H "X-API-Key: $BM_API_KEY" \
  "https://api.boringmarketing.com/track/citations?brand_id=$BRAND_ID"

AI Overview presence (per topic family hub keyword)

curl -s \
  -H "X-API-Key: $BM_API_KEY" \
  "https://api.boringmarketing.com/track/aio?brand_id=$BRAND_ID"

Full 30-day report

curl -s \
  -H "X-API-Key: $BM_API_KEY" \
  "https://api.boringmarketing.com/track/report?brand_id=$BRAND_ID"

The learning loop

Outcome events feed the leverage scoring calibration. The multiplier table starts as heuristics (trust_gap: 1.20x, eeat_gap: 1.25x, content_gap: 1.10x, technical_gap: 1.30x) and gets tuned from real results as outcome observations accumulate.

After enough brands have tracked outcomes:

• The system learns which signal types produce the highest ROI
• Format recommendations improve (hub-and-spokes vs single article vs comparison)
• Priority scoring becomes more accurate for your industry

This is the compounding advantage of reporting every outcome event consistently.