Ivx Ams Revenue Intelligence

Cursor install notes

• Skill root: this folder (scripts + references live here).
• Vendor source: ~/.cursor/skills/_vendor/ai-marketing-skills/ (git pull to update).
• Telemetry is optional; skip telemetry/*.py unless you opt in.
• Run Python from this skill directory so relative paths resolve.
• Prefer python on Windows if python3 is missing.

AI Revenue Intelligence

Working directory

cd ~/.cursor/skills/ams-revenue-intelligence
# Windows: cd $env:USERPROFILE\.cursor\skills\ams-revenue-intelligence

AI-powered revenue intelligence: sales call insight extraction, content-to-revenue attribution, and multi-source client reporting.

When to Use

• User wants to extract insights from Gong sales call transcripts
• User needs to identify objections, buying signals, or competitive mentions in calls
• User wants to prove content ROI by mapping content to closed deals
• User needs revenue attribution across first-touch and multi-touch models
• User wants to generate a unified client report from GA4 + HubSpot + Ahrefs + Gong
• User asks about content gaps in the buyer journey
• User needs anomaly detection across marketing metrics

Tools

Gong-to-Insight Pipeline (gong_insight_pipeline.py)

Extracts structured intelligence from sales call transcripts. Works with Gong API or plain transcript files.

# Analyze a single transcript file
python gong_insight_pipeline.py --file transcript.txt

# Analyze multiple transcript files
python gong_insight_pipeline.py --dir ./transcripts/

# Pull recent calls from Gong API (last 7 days)
python gong_insight_pipeline.py --gong --days 7

# Pull specific call by ID
python gong_insight_pipeline.py --gong --call-id abc123

# Output as JSON file
python gong_insight_pipeline.py --file transcript.txt --output insights.json

# Generate content topics from recurring objections
python gong_insight_pipeline.py --dir ./transcripts/ --content-topics

# Generate follow-up suggestions for outbound sequences
python gong_insight_pipeline.py --file transcript.txt --follow-ups

What it extracts:

• Objections (categorized: pricing, timing, competition, authority, need)
• Buying signals (budget confirmed, timeline mentioned, decision maker engaged, champion identified)
• Competitive mentions (who was mentioned, context: positive/negative/neutral)
• Pricing discussions (anchors, pushback, willingness indicators)
• Content topic suggestions from recurring objection patterns
• Personalized follow-up drafts based on call context

Output: Structured JSON to stdout or file. Each call produces an insights object with objections, buying_signals, competitive_mentions, pricing_discussions, content_topics, and follow_ups arrays.

Revenue Attribution Mapper (revenue_attribution.py)

Maps content pieces to pipeline and closed revenue. Proves content ROI with first-touch and multi-touch attribution.

# Run full attribution report (GA4 + HubSpot)
python revenue_attribution.py --report

# First-touch attribution only
python revenue_attribution.py --report --model first-touch

# Multi-touch (linear) attribution
python revenue_attribution.py --report --model linear

# Time-decay attribution
python revenue_attribution.py --report --model time-decay

# Filter by date range
python revenue_attribution.py --report --start 2025-01-01 --end 2025-03-31

# Calculate cost-per-acquisition by content type
python revenue_attribution.py --cpa --costs content_costs.json

# Identify content gaps in the buyer journey
python revenue_attribution.py --gaps

# Output as JSON
python revenue_attribution.py --report --json --output attribution.json

What it produces:

• Content-to-revenue mapping (which blog posts, videos, podcasts drove deals)
• First-touch, linear, and time-decay attribution models
• Cost-per-acquisition by content type (blog, video, podcast, webinar)
• Content ROI report with revenue per piece
• Content gap analysis (funnel stages with no attribution)
• Top-performing content ranked by attributed revenue

Data sources: GA4 (page paths, sessions, conversions) + HubSpot (deals, touchpoints, close dates)

Multi-Source Client Report Generator (client_report_generator.py)

Generates unified client-ready BI reports from GA4, HubSpot, Ahrefs, and Gong.

# Generate full client report
python client_report_generator.py --client "Acme Corp"

# Specify date range
python client_report_generator.py --client "Acme Corp" --start 2025-03-01 --end 2025-03-31

# Output as markdown
python client_report_generator.py --client "Acme Corp" --format markdown --output report.md

# Output as JSON (for rendering in slides/dashboards)
python client_report_generator.py --client "Acme Corp" --format json --output report.json

# Skip specific data sources
python client_report_generator.py --client "Acme Corp" --skip gong
python client_report_generator.py --client "Acme Corp" --skip ahrefs,gong

# Enable anomaly detection
python client_report_generator.py --client "Acme Corp" --anomalies

# Compare to previous period
python client_report_generator.py --client "Acme Corp" --compare previous-month

What it produces:

• Executive summary with key metrics and period-over-period changes
• Traffic section: sessions, users, top pages, channel breakdown (GA4)
• Pipeline section: deals created, moved, closed, revenue (HubSpot)
• SEO section: keyword rankings, backlinks, domain rating changes (Ahrefs)
• Call quality section: talk ratios, objection frequency, win rates (Gong)
• Anomaly flags: unusual spikes/drops with severity and context
• Output as structured markdown or JSON

Configuration

All scripts read from environment variables. Copy .env.example to .env and fill in your values.

Required Environment Variables

Optional Environment Variables

Data Flow

Gong Transcripts → Insight Pipeline → Objections, Signals, Competitors → Content Topics + Follow-ups
GA4 + HubSpot   → Attribution Mapper → Content ROI, CPA, Gap Analysis → Revenue Proof
GA4 + HubSpot + Ahrefs + Gong → Client Report → Executive Summary + Anomalies → Client Deliverable

Recommended Workflow

1. Weekly: Run gong_insight_pipeline.py --gong --days 7 to extract call intelligence
2. Monthly: Run revenue_attribution.py --report to prove content ROI
3. Monthly: Run client_report_generator.py for each client deliverable
4. Quarterly: Run revenue_attribution.py --gaps to find content gaps
5. Ongoing: Feed Gong insight follow-ups into outbound sequences

Revenue Analytics Feedback Loop

Any recommendation that changes outbound, sales language, routing, content investment, or client reporting should get a readback.

Before recommending:

• Define the baseline window and candidate window.
• Pull source data from HubSpot, Gong, GA4, Ahrefs, and any outbound platform available.
• Identify the primary metric before looking at the result, otherwise the analysis becomes KPI karaoke.

After the change:

1. Pull analytics after the readback date.
2. Compare baseline vs candidate.
3. Separate owner/participant effects, list quality, campaign changes, seasonality, and attribution gaps.
4. Promote, keep testing, rollback, or mark unproven.

Common primary metrics:

• positive reply rate
• booked meeting rate
• qualified opportunity movement
• pipeline created
• speed-to-lead
• content-assisted revenue
• conversion rate
• objection frequency reduction

Every promoted playbook patch should include the change made, source systems, baseline window, candidate window, metric winner, caveats, and rollback rule.

Dependencies

pip install -r requirements.txt