AI Revenue Intelligence skill

AI Revenue Intelligence

Preamble (runs on skill start)

# Version check (silent if up to date)
python3 telemetry/version_check.py 2>/dev/null || true

# Telemetry opt-in (first run only, then remembers your choice)
python3 telemetry/telemetry_init.py 2>/dev/null || true

Privacy: This skill logs usage locally to ~/.ai-marketing-skills/analytics/. Remote telemetry is opt-in only. No code, file paths, or repo content is ever collected. See telemetry/README.md.

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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

Variable	Used By	Description
GONG_API_KEY	Gong Pipeline, Client Report	Gong API access key
GONG_API_BASE_URL	Gong Pipeline, Client Report	Gong API base URL
HUBSPOT_API_KEY	Attribution, Client Report	HubSpot private app token
GA4_PROPERTY_ID	Attribution, Client Report	GA4 property ID
GA4_CREDENTIALS_JSON	Attribution, Client Report	Path to GA4 service account JSON

Optional Environment Variables

Variable	Used By	Description
AHREFS_TOKEN	Client Report	Ahrefs API token
OUTPUT_DIR	All	Directory for output files (default: ./output)

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