---
name: ai-sales-playbook-value-based-pricing-deal-upselling
description: "Open-source AI marketing skills for Claude Code \u2014 growth experiments, sales pipeline, content ops, outbound, SEO, and finance automation"
---

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

## Preamble (runs on skill start)

```bash
# 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

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


---
Source attribution: ericosiu
Original project: https://github.com/ericosiu/ai-marketing-skills/blob/8088e1a167804d5615f6e982f62e5b4b6ff8f2f1/sales-playbook/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Single Grain

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: ericosiu
Original project: https://github.com/ericosiu/ai-marketing-skills/blob/8088e1a167804d5615f6e982f62e5b4b6ff8f2f1/sales-playbook/SKILL.md
Attribution basis: Source repository record

Fields of work: Marketing, Sales
Roles: Growth Marketer, Marketing Analyst, Product Marketer, Sales Engineer, Sales Development Rep (SDR)
Specialties: API
Tools: Ahrefs, Anthropic API, OpenAI API, SEMrush
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 1c01a14dcb26a7f50f4f567cd4a8765d32e98b3d56edada5f976329e42170396