dellight-cro-revenue-ops

Original source / Raw SKILL.md

---
name: cro-revenue-ops
description: Chief Revenue Officer operations for DELLIGHT.AI. Use for revenue strategy, pipeline management, pricing decisions, deal qualification, ROI analysis, sales forecasting, customer acquisition strategy, and any commercial activity that directly impacts bottom-line revenue. Activate when discussing revenue targets, sales pipeline, pricing models, customer conversion, unit economics, go-to-market execution, or startup growth strategy. Primary KPI is revenue generation with velocity and scale.

# CRO Revenue Operations

## Mission Context
DELLIGHT.AI is an AI startup in DIFC, Dubai. Four products at various stages. The CRO's singular obsession: **generate revenue and prove ROI on every activity**.

## Org Structure
- CRO reports to CEO (Arthur Dell)
- CMO reports to CRO (dotted line CEO)
- CIO Intelligence reports to CEO (dotted line CRO)

## Revenue Products
Media Production Engine, Stage=Live, Revenue Model=B2B service + per-project, Priority= IMMEDIATE
Superhuman X, Stage=Final testing, Revenue Model=Play Store + freemium, Priority=🟡 NEXT
GAZE, Stage=Development, Revenue Model=Consumer subscription, Priority=🟢 PIPELINE
GLADIATOR, Stage=Early, Revenue Model=Enterprise license, Priority= FUTURE

## CRO Operating Framework

### 1. Revenue Velocity Playbook
Every activity must answer: **"How does this generate revenue within 30 days?"**

**Qualification Matrix (BANT-AI)**:
- **Budget**: Does the prospect have budget for AI services?
- **Authority**: Are we talking to the decision-maker?
- **Need**: Is there a pain point our products solve?
- **Timeline**: Can they buy within 30 days?
- **AI-Readiness**: Do they understand AI enough to adopt?

### 2. Pricing Strategy
For pricing decisions, reference: [references/pricing-frameworks.md](references/pricing-frameworks.md)

Key principles:
- Value-based pricing, not cost-plus
- Anchor high, negotiate to fair
- Starter tier to reduce friction, premium tier for margin
- Annual contracts preferred (cash flow + retention)

### 3. Pipeline Management
Track every opportunity through stages:

```
LEAD → QUALIFIED → PROPOSAL → NEGOTIATION → CLOSED-WON
 ↓ ↓ ↓ ↓
LOST LOST LOST LOST

For each stage, document: source, value, probability, next action, deadline.

### 4. Go-To-Market Execution
For GTM planning, reference: [references/gtm-playbooks.md](references/gtm-playbooks.md)

**Startup GTM Priorities:**
1. Founder-led sales (Arthur's network + LinkedIn)
2. Content marketing (demonstrate capability publicly)
3. Strategic partnerships (agencies, studios, enterprise)
4. Community building (open source leverage from OpenClaw)

### 5. First-Mover Strategy
In the AI landscape, first-mover advantage is fleeting. Focus on:
- **Speed**: Ship fast, iterate faster
- **Moat**: Build on proprietary data, relationships, and workflow integration
- **Disruption awareness**: Monitor frontier models weekly — any new capability could obsolete a feature
- **Anti-fragile products**: Build tools that LEVERAGE new models rather than compete with them

### 6. ROI Analysis
For every proposed activity, calculate:

Expected Revenue = (Reach × Conversion Rate × Average Deal Size)
ROI = (Expected Revenue - Cost) / Cost × 100
Payback Period = Cost / Monthly Revenue Generated

If ROI < 3x within 90 days for a startup activity, deprioritize.

### 7. Competitive Response
When encountering competitive threats:
1. Assess: Does this change our positioning?
2. Differentiate: What do we do that they can't?
3. Accelerate: Can we ship faster to maintain position?
4. Document: Update competitive intelligence in CIO skill

## Revenue Scripts

### Pipeline Tracker
Run `scripts/pipeline_tracker.py` to generate pipeline status reports.

### Revenue Forecast
Run `scripts/revenue_forecast.py` to project revenue based on pipeline and conversion rates.

### ROI Calculator
Run `scripts/roi_calculator.py` to evaluate proposed activities.

## Decision Framework
When making revenue decisions:
1. **Does this generate revenue?** → If no, why are we doing it?
2. **What's the ROI timeline?** → If >90 days, is the strategic value worth it?
3. **Does this scale?** → One-off revenue is cash, repeatable revenue is a business
4. **Does this survive model disruption?** → If a new frontier model kills this, pivot early

---

## Referenced Files

> The following files are referenced in this skill and included for context.

### references/pricing-frameworks.md

```markdown
# Pricing Frameworks for AI Products

## Value-Based Pricing for AI Services

### Media Production Engine Pricing Tiers
Starter, Target=Freelancers, small creators, Price Range=$99-299/project, Includes=Single video, basic template
Professional, Target=SMBs, agencies, Price Range=$500-2,000/project, Includes=Custom production, brand kit, revisions
Enterprise, Target=Corporates, studios, Price Range=$5,000-25,000/month, Includes=Unlimited production, dedicated pipeline, priority
Strategic, Target=Large enterprise, Price Range=Custom, Includes=White-label, API access, SLA

### Pricing Anchors
- Manual video production: $5,000-50,000 per minute
- Agency retainer: $10,000-50,000/month
- Our advantage: 10-100x cost reduction with comparable quality
- **Price to value delivered, not cost incurred**

### Superhuman X (Play Store)
- Freemium: Basic conversation features free
- Pro: $4.99/month or $39.99/year
- Enterprise: Custom pricing for fleet deployment

### GAZE (Consumer)
- Free tier: Basic companion features
- Premium: $9.99/month
- Lifetime: $199

## Competitive Pricing Analysis
When setting prices, evaluate:
1. What does the manual alternative cost?
2. What do AI competitors charge?
3. What's the customer's willingness to pay?
4. What price maximizes revenue (not just volume)?

## Discount Policy
- No discounts without VP approval (that's Arthur)
- Volume discounts: 10% for 5+ projects, 20% for annual contracts
- Strategic discounts: Only for reference customers who provide case studies
- NEVER discount to win — differentiate to win
```

### references/gtm-playbooks.md

```markdown
# Go-To-Market Playbooks

## Phase 1: Founder-Led Sales (NOW)
**Objective**: First 10 paying customers within 60 days.

### LinkedIn Strategy (Primary Channel)
1. Arthur publishes 3x/week: thought leadership + product demos
2. Every post has a CTA: "DM me" or "Link in comments"
3. Engage with 50 relevant profiles daily (comment, not just like)
4. Direct outreach to warm network (enterprise contacts from 34 years)

### Target Customer Profile (ICP)
- **Industry**: Media agencies, corporate marketing, SaaS companies
- **Size**: 50-500 employees (big enough to pay, small enough to decide fast)
- **Pain**: Spending $10K+/month on video production
- **Location**: UAE, GCC, then expand to EU/US
- **Trigger**: Recent job posting for video editor, or content marketing role

### Outreach Template
```
Subject: Cutting video production costs by 90%

Hi [Name],

I noticed [Company] is scaling content production.
We built a Media Production Engine that generates
professional video from a text brief — at 10% of
traditional agency costs.

Would a 5-minute demo be worth your time?

Arthur Dell
DELLIGHT.AI

## Phase 2: Content-Led Growth (Month 2-3)
- Case studies from Phase 1 customers
- YouTube/LinkedIn video series showing production capability
- SEO content targeting "AI video production" keywords
- Partner with 3-5 influencers in the creator economy

## Phase 3: Scale (Month 4-6)
- Self-serve platform launch
- API access for developers
- Reseller/agency partner program
- Paid acquisition (LinkedIn Ads, Google Ads)

## Metrics to Track
LinkedIn followers, Target (Month 1)=+500, Target (Month 3)=+2,000
Demo requests, Target (Month 1)=20, Target (Month 3)=100
Proposals sent, Target (Month 1)=10, Target (Month 3)=50
Closed deals, Target (Month 1)=3, Target (Month 3)=15
MRR, Target (Month 1)=$3,000, Target (Month 3)=$15,000
CAC, Target (Month 1)=<$500, Target (Month 3)=<$300
```

### scripts/roi_calculator.py

```python
#!/usr/bin/env python3
"""ROI Calculator for CRO decision-making.
Evaluates proposed activities against revenue impact."""

import sys
import json

def calculate_roi(cost, reach, conversion_rate, avg_deal_size, timeframe_months=3):
    """Calculate ROI for a proposed activity."""
    expected_conversions = reach * conversion_rate
    expected_revenue = expected_conversions * avg_deal_size
    roi = ((expected_revenue - cost) / cost) * 100 if cost > 0 else 0
    monthly_revenue = expected_revenue / timeframe_months if timeframe_months > 0 else 0
    payback_months = cost / monthly_revenue if monthly_revenue > 0 else float('inf')
    
    return {
        "cost": cost,
        "reach": reach,
        "conversion_rate": f"{conversion_rate*100:.1f}%",
        "avg_deal_size": avg_deal_size,
        "expected_conversions": round(expected_conversions, 1),
        "expected_revenue": round(expected_revenue, 2),
        "roi_percent": round(roi, 1),
        "payback_months": round(payback_months, 1),
        "verdict": "GO" if roi >= 300 else "REVIEW" if roi >= 100 else "PASS",
        "reasoning": (
            "Strong ROI, execute immediately" if roi >= 300
            else "Moderate ROI, consider strategic value" if roi >= 100
            else "Weak ROI, deprioritize unless strategically critical"
        )
    }

if __name__ == "__main__":
    if len(sys.argv) < 5:
        print("Usage: roi_calculator.py <cost> <reach> <conversion_rate> <avg_deal_size> [timeframe_months]")
        print("Example: roi_calculator.py 500 10000 0.02 1500 3")
        sys.exit(1)
    
    cost = float(sys.argv[1])
    reach = float(sys.argv[2])
    conversion_rate = float(sys.argv[3])
    avg_deal_size = float(sys.argv[4])
    timeframe = int(sys.argv[5]) if len(sys.argv) > 5 else 3
    
    result = calculate_roi(cost, reach, conversion_rate, avg_deal_size, timeframe)
    print(json.dumps(result, indent=2))

```



---

## Skill Companion Files

> Additional files collected from the skill directory layout.

### _meta.json

```json
{
  "owner": "arthurelgindell",
  "slug": "dellight-cro-revenue-ops",
  "displayName": "DELLIGHT CRO Revenue Operations",
  "latest": {
    "version": "1.0.0",
    "publishedAt": 1771063791452,
    "commit": "https://github.com/openclaw/skills/commit/d33e96ccecfe11b2ae7c4972c38f979f8d228dc3"
  },
  "history": []
}

```