sales-playbook

name	sales-playbook
description	"Build value-based pricing frameworks, generate pre-call briefings, analyze sales calls for patterns, and create tiered pricing packages. Use when asked to "create pricing tiers", "pre-call briefing", "analyze this sales call", "value-based pricing", "upsell strategy", or "deal packaging"."

AI Sales Playbook — Value-Based Pricing & Deal Upselling

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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Framework for value-based pricing that moves deals from $10K/mo → $40-100K/mo. Pre-call briefings, tiered package generation, post-call analysis, and a pattern library for training sales teams on proven pricing techniques.

When to Use

Use this skill when:

• Preparing for a sales call and need competitive data to anchor on value
• Building tiered pricing proposals for prospects at different deal sizes
• Analyzing sales call transcripts to score against the value-based pricing framework
• Training sales reps on proven pricing patterns and objection handling
• Upselling existing deals by identifying missed value levers

Tools

Pre-Call Preparation

Script	Purpose	Key Command
value_pricing_briefing.py	Generate pre-call briefing with competitive data, value calcs, and conversation hooks	python3 value_pricing_briefing.py --domain acme.com --competitors "comp1.com,comp2.com"
value_pricing_packager.py	Generate tiered S/M/L + performance pricing packages	python3 value_pricing_packager.py --target-monthly 80000 --services "seo,cro,content,paid"

Post-Call Analysis

Script	Purpose	Key Command
call_analyzer.py	Score a call transcript against the value-based pricing framework	python3 call_analyzer.py --transcript call.txt
pricing_pattern_library.py	Reference library of 10 proven pricing patterns + training mode	python3 pricing_pattern_library.py --list

Configuration

All scripts use environment variables for API keys:

Optional Environment Variables

• AHREFS_API_KEY — Ahrefs API key (Briefing Generator, optional — uses stubs without it)
• SEMRUSH_API_KEY — SEMrush API key (Briefing Generator, optional — uses stubs without it)
• ANTHROPIC_API_KEY — Anthropic API key (Call Analyzer, Pattern Library scenario mode)
• OPENAI_API_KEY — OpenAI API key (alternative to Anthropic for LLM features)

Scripts work without API keys using built-in stubs and sample data for testing.

Key Concepts

The Value-Based Pricing Framework

1. Lead with data, not your pitch — Show the prospect their competitive gaps before discussing services
2. Anchor high — Present the premium tier first so the target tier feels reasonable
3. Tie price to value — Every dollar of investment maps to projected ROI
4. Use competitive triggers — Competitor rankings activate urgency without being pushy
5. Present tiered options — 3-4 tiers with clear tradeoffs, always including a performance option

Pricing Framework Score (0-100)

The call analyzer scores calls against these criteria:

• Showed data before pitching (20 pts)
• Presented tiered options (20 pts)
• Anchored high first (15 pts)
• Tied price to value/ROI (15 pts)
• Used competitive triggers (15 pts)
• Got prospect to state their own pain (15 pts)

Dependencies

• Python 3.9+
• requests (for API integrations)
• No other external dependencies