Experiment Loop for MD Home Care

Tracks content changes, measures their impact on traffic and rankings, and decides whether to keep, iterate, or revert. Runs weekly.

CRITICAL: Lag Times for YMYL Content

YMYL content (aged care, disability services) has longer lag times than SaaS content. Do not evaluate changes too early.

| Change Type | SEO Lag | AEO Lag | Evaluation Window | |-------------|---------|---------|-------------------| | Service page optimization | 10-21 days | 3-7 days | 3 weeks minimum | | Location page creation | 14-21 days | 7-14 days | 3 weeks minimum | | Blog post publishing | 7-14 days | 3-7 days | 2 weeks minimum | | Provider comparison addition | 7-14 days | 3-7 days | 2 weeks minimum | | Trust signal enhancement | 10-21 days | 7-14 days | 3 weeks minimum | | FAQ addition | 7-14 days | 3-7 days | 2 weeks minimum |

Step 1: Weekly Git Scan

Identify all content changes from the past week:

cd ~/Projects/mdhomecarebuild

# All content changes in last 7 days
git log --since="7 days ago" --name-only --pretty=format:"%h %s" -- "src/content/**/*.md" "src/content/**/*.mdx"

# Summarize by type
git log --since="7 days ago" --name-only --pretty=format:"" -- "src/content/blog/*.md" | sort -u | head -20
git log --since="7 days ago" --name-only --pretty=format:"" -- "src/content/services/*.md" | sort -u | head -20
git log --since="7 days ago" --name-only --pretty=format:"" -- "src/content/providers/*.md" | sort -u | head -20

Categorize each change:

New page: Completely new content file
Major edit: Structural changes (new sections, comparison tables, rewritten H1/H2)
Minor edit: Small fixes (typos, link updates, frontmatter changes)

Step 2: Baseline Measurement

For each changed page, capture the pre-change baseline. If baseline was not captured before the change, use the previous period as proxy.

GSC Baseline

cd ~/Projects/mdhomecarebuild

# For each changed page, get keyword data
python3 src/scripts/advanced_gsc_analyzer.py --page "/services/[slug]"
python3 src/scripts/advanced_gsc_analyzer.py --page "/blog/[slug]"

Record:

Top 10 keywords by clicks
Average position for primary keyword
Total impressions and clicks (last 7 days)

PostHog Baseline

# Page traffic
python3 src/scripts/posthog_analytics.py --page "/services/[slug]" --days 7

# AI referral traffic
python3 src/scripts/posthog_analytics.py --ai-referrals --days 7

Record:

Total pageviews (last 7 days)
AI referral visits to that page
Traffic sources breakdown

Step 3: Post-Change Measurement

After the evaluation window has passed (see lag times table), measure again.

# GSC: same page analysis
python3 src/scripts/advanced_gsc_analyzer.py --page "/services/[slug]"

# PostHog: same page traffic
python3 src/scripts/posthog_analytics.py --page "/services/[slug]" --days 7
python3 src/scripts/posthog_analytics.py --ai-referrals --days 7

Step 4: Attribution and Decision

Compare Metrics

For each experiment, calculate:

| Metric | Before | After | Change | |--------|--------|-------|--------| | Organic clicks (7d) | X | Y | +/- % | | Impressions (7d) | X | Y | +/- % | | Avg position (primary KW) | X | Y | +/- positions | | AI referral visits (7d) | X | Y | +/- % | | Total pageviews (7d) | X | Y | +/- % |

Decision Framework

KEEP if:

Organic clicks increased >10%
OR average position improved by 2+ positions
OR AI referral visits increased >20%
OR impressions increased >15% (leading indicator)
AND no negative impact on other pages (cannibalization check)

ITERATE if:

Mixed signals (some metrics up, some flat)
OR small positive movement (<10% clicks) that suggests potential
OR evaluation window has not fully elapsed
Action: Make targeted refinements and re-evaluate after another cycle

REVERT if:

Organic clicks decreased >15%
AND average position dropped by 3+ positions
AND no compensating AI referral increase
Action: Restore previous version via git, document what went wrong

WAIT if:

Change is too recent (within lag window)
Action: Re-evaluate next week

Step 5: Log to Playbook

Record every experiment result in PLAYBOOK.md:

## [Date] - [Experiment Name]

**Category:** [Service page optimization / Location page / Blog post / Comparison / Trust signal / FAQ]
**Page:** [URL path]
**Change:** [Brief description of what was changed]
**Hypothesis:** [What we expected to happen]

**Baseline (pre-change):**
- Organic clicks (7d): X
- Avg position (primary KW): X
- AI referrals (7d): X

**Result (post-change, measured [date]):**
- Organic clicks (7d): Y (+/- %)
- Avg position (primary KW): Y (+/- positions)
- AI referrals (7d): Y (+/- %)

**Decision:** KEEP / ITERATE / REVERT / WAIT
**Lesson:** [What we learned]

Experiment Categories

Service Page Optimizations

Adding comparison tables
Rewriting H1/byline
Adding trust signal sections
Expanding FAQ sections
Adding AI differentiation paragraphs

Location Page Creation

New suburb-specific service pages
Measure: local keyword rankings, location-specific traffic

Blog Post Publishing

New informational content
Template/download posts
Provider comparison posts
Measure: organic clicks, keyword coverage expansion

Provider Comparison Additions

New comparison tables on existing pages
New "vs" blog posts
Measure: comparison keyword rankings, AI referral traffic

Trust Signal Enhancements

Adding registration numbers
Adding testimonials
Adding clinical governance sections
Measure: overall page authority signals, position changes

FAQ Additions

New FAQ sections
Expanding existing FAQs with PAA questions
Measure: featured snippet captures, PAA appearances

Weekly Routine

Every week:

Run git scan (Step 1)
For changes past their evaluation window, measure results (Step 3)
Make keep/iterate/revert decisions (Step 4)
Log results to PLAYBOOK.md (Step 5)
Capture baselines for new changes (Step 2)

Usage

/experiment-loop

Runs the full weekly cycle: scan, measure, decide, log.

/experiment-loop --check "/services/sil-services"

Check status of a specific page experiment.