Alfred Claw 3a0e0931d9 Add sales-playbook: Value-Based Pricing framework (4 skills)

New category: sales-playbook/
- Value-Based Pricing: Pre-Call Briefing Generator
- Value-Based Pricing: Tiered Package Builder
- Value-Based Pricing: Post-Call Deal Analyzer
- Value-Based Pricing: Pattern Library & Training (10 proven patterns)

All data fully anonymized. Telemetry preamble included.

2026-03-31 10:28:12 -07:00

3.5 KiB

Raw Blame History

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.

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

Lead with data, not your pitch — Show the prospect their competitive gaps before discussing services
Anchor high — Present the premium tier first so the target tier feels reasonable
Tie price to value — Every dollar of investment maps to projected ROI
Use competitive triggers — Competitor rankings activate urgency without being pushy
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

3.5 KiB Raw Blame History