ai-marketing-skills/sales-playbook/README.md

💰 AI Sales Playbook — Value-Based Pricing & Deal Upselling

Turn $10K/mo deals into $40-100K/mo using value-based pricing, not discounting.

A complete framework for value-based pricing in B2B services sales: pre-call competitive briefings, tiered package generation, post-call analysis, and a proven pattern library for training sales teams.

These tools were built from real sales call patterns at Single Grain, where deals routinely moved from $10K/mo to $40-100K/mo using the techniques in this playbook. Now open-sourced for any sales team to use.

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The Framework

The value-based pricing framework is built on 5 principles:

1. Lead with data, not your pitch — Show competitive gaps before discussing services
2. Anchor high — Present premium tier first so the target feels reasonable
3. Tie price to value — Every dollar maps to projected ROI
4. Use competitive triggers — Competitor data activates urgency
5. Present tiered options — 3-4 tiers with clear tradeoffs

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Tools

1. 📊 Pre-Call Briefing Generator (value_pricing_briefing.py)

Walk into every call armed with competitive data that makes the prospect sell themselves on the value.

What it generates:

• Anchor data points (keyword gaps, traffic gaps vs. competitors)
• Competitive triggers ("CompetitorA is #1 for [keyword], you're #14")
• Value calculations (position improvement → traffic → paid equivalent value)
• Conversation hooks (opening questions to surface pain)
• Objection pre-empts (responses for 6 common objections at this deal size)

# Basic briefing
python3 value_pricing_briefing.py --domain acme.com --competitors "comp1.com,comp2.com"

# With industry and deal target
python3 value_pricing_briefing.py --domain acme.com --competitors "comp1.com" --industry saas --deal-target 80000

# JSON output
python3 value_pricing_briefing.py --domain acme.com --competitors "comp1.com,comp2.com" --format json

2. 📦 Tiered Package Builder (value_pricing_packager.py)

Auto-generates S/M/L + performance-based pricing tiers with pricing psychology built in.

Tiers generated:

Tier	Purpose	Price vs. Target
Powerhouse	The anchor (makes everything else look reasonable)	130-150%
Value ⭐	Where you want them to land	100%
Baseline	Floor (proves the model before scaling)	40-50%
Performance	Skin in the game (lower base + bonus triggers)	30-40% base

Each tier includes: specific deliverables, monthly price, ROI projection, included vs. excluded features.

# Full package generation
python3 value_pricing_packager.py --target-monthly 80000 --services "seo,cro,content,paid"

# With current spend context
python3 value_pricing_packager.py --target-monthly 50000 --services "seo,content" --current-spend 10000

# JSON output for integration
python3 value_pricing_packager.py --target-monthly 80000 --services "seo,cro,content,paid" --format json

3. 🎯 Post-Call Deal Analyzer (call_analyzer.py)

Score any sales call transcript against the value-based pricing framework.

Scoring criteria (0-100):

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

Also extracts: buying signals, objections (categorized), deal probability, upsell opportunities, and recommended next steps.

# Analyze a transcript file
python3 call_analyzer.py --transcript call.txt

# Pipe from stdin
cat call.txt | python3 call_analyzer.py

# JSON output
python3 call_analyzer.py --transcript call.txt --format json

4. 📚 Pattern Library & Training (pricing_pattern_library.py)

10 proven value-based pricing patterns with detailed breakdowns, example dialogue, and interactive training.

Patterns included:

1. Anchor With Data
2. Tiered Packaging (S/M/L + Performance)
3. Competitive Ego Trigger
4. Strategic Involvement Upsell
5. Bridge Offer
6. Performance Skin-in-Game
7. Value Math on Screen
8. Compound Effect Close
9. Reference Customer Drop
10. In-House Team Framing

# List all patterns
python3 pricing_pattern_library.py --list

# Deep dive on a pattern
python3 pricing_pattern_library.py --pattern "anchor-with-data"

# Get recommendations for a scenario
python3 pricing_pattern_library.py --scenario "prospect is a $50M SaaS company spending $15K/mo on marketing"

# Interactive training quiz
python3 pricing_pattern_library.py --quiz

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Quick Start

1. Clone and install

git clone https://github.com/nichochar/ai-marketing-skills.git
cd ai-marketing-skills/sales-playbook
pip install -r requirements.txt

2. Run a pre-call briefing

python3 value_pricing_briefing.py --domain acme.com --competitors "techstart.com,novapay.com" --deal-target 80000

3. Generate pricing tiers

python3 value_pricing_packager.py --target-monthly 80000 --services "seo,cro,content,paid" --current-spend 15000

4. Analyze a call

python3 call_analyzer.py --transcript sample_call.txt

5. Study the patterns

python3 pricing_pattern_library.py --list
python3 pricing_pattern_library.py --quiz

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Optional API Integration

All scripts work without API keys using built-in stubs. For live data:

API	Used By	Env Variable
Ahrefs	Briefing Generator	AHREFS_API_KEY
SEMrush	Briefing Generator	SEMRUSH_API_KEY
Anthropic	Call Analyzer, Pattern Library	ANTHROPIC_API_KEY
OpenAI	Call Analyzer, Pattern Library	OPENAI_API_KEY

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File Structure

sales-playbook/
├── README.md                        # This file
├── SKILL.md                         # Claude Code skill definition
├── requirements.txt                 # Python dependencies
├── value_pricing_briefing.py        # Pre-call briefing generator
├── value_pricing_packager.py        # Tiered package builder
├── call_analyzer.py                 # Post-call deal analyzer
└── pricing_pattern_library.py       # Pattern library & training

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How It Works Together

1. Before the call: Run the briefing generator to get competitive data, value calculations, and conversation hooks
2. Preparing the proposal: Use the package builder to generate tiered pricing with ROI projections
3. After the call: Run the call analyzer to score the conversation and identify next steps
4. Ongoing training: Use the pattern library to study and practice the 10 core patterns
5. Continuous improvement: Analyze call scores over time to identify which patterns your team needs to practice

The result: a systematic approach to value-based pricing that turns data into leverage and conversations into larger deals.

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