sales-engineer

# Sales Engineer Skill

A production-ready skill package for pre-sales engineering that bridges technical expertise and sales execution. Provides automated analysis for RFP/RFI responses, competitive positioning, and proof-of-concept planning.

## Overview

**Role:** Sales Engineer / Solutions Architect
**Domain:** Pre-Sales Engineering, Solution Design, Technical Demos, Proof of Concepts
**Business Type:** SaaS / Pre-Sales Engineering

### What This Skill Does

- **RFP/RFI Response Analysis** - Score requirement coverage, identify gaps, generate bid/no-bid recommendations
- **Competitive Technical Positioning** - Build feature comparison matrices, identify differentiators and vulnerabilities
- **POC Planning** - Generate timelines, resource plans, success criteria, and evaluation scorecards
- **Demo Preparation** - Structure demo scripts with talking points and objection handling
- **Technical Proposal Creation** - Framework for solution architecture and implementation planning
- **Win/Loss Analysis** - Data-driven competitive assessment for deal strategy

### Key Metrics

| Metric | Description | Target |
|--------|-------------|--------|
| Win Rate | Deals won / total opportunities | >30% |
| Sales Cycle Length | Average days from discovery to close | <90 days |
| POC Conversion Rate | POCs resulting in closed deals | >60% |
| Customer Engagement Score | Stakeholder participation in evaluation | >75% |
| RFP Coverage Score | Requirements fully addressed | >80% |

## 5-Phase Workflow

### Phase 1: Discovery & Research

**Objective:** Understand customer requirements, technical environment, and business drivers.

**Activities:**
1. Conduct technical discovery calls with stakeholders
2. Map customer's current architecture and pain points
3. Identify integration requirements and constraints
4. Document security and compliance requirements
5. Assess competitive landscape for this opportunity

**Tools:** Use `rfp_response_analyzer.py` to score initial requirement alignment.

**Output:** Technical discovery document, requirement map, initial coverage assessment.

### Phase 2: Solution Design

**Objective:** Design a solution architecture that addresses customer requirements.

**Activities:**
1. Map product capabilities to customer requirements
2. Design integration architecture
3. Identify customization needs and development effort
4. Build competitive differentiation strategy
5. Create solution architecture diagrams

**Tools:** Use `competitive_matrix_builder.py` to identify differentiators and vulnerabilities.

**Output:** Solution architecture, competitive positioning, technical differentiation strategy.

### Phase 3: Demo Preparation & Delivery

**Objective:** Deliver compelling technical demonstrations tailored to stakeholder priorities.

**Activities:**
1. Build demo environment matching customer's use case
2. Create demo script with talking points per stakeholder role
3. Prepare objection handling responses
4. Rehearse failure scenarios and recovery paths
5. Collect feedback and adjust approach

**Templates:** Use `demo_script_template.md` for structured demo preparation.

**Output:** Customized demo, stakeholder-specific talking points, feedback capture.

### Phase 4: POC & Evaluation

**Objective:** Execute a structured proof-of-concept that validates the solution.

**Activities:**
1. Define POC scope, success criteria, and timeline
2. Allocate resources and set up environment
3. Execute phased testing (core, advanced, edge cases)
4. Track progress against success criteria
5. Generate evaluation scorecard

**Tools:** Use `poc_planner.py` to generate the complete POC plan.

**Templates:** Use `poc_scorecard_template.md` for evaluation tracking.

**Output:** POC plan, evaluation scorecard, go/no-go recommendation.

### Phase 5: Proposal & Closing

**Objective:** Deliver a technical proposal that supports the commercial close.

**Activities:**
1. Compile POC results and success metrics
2. Create technical proposal with implementation plan
3. Address outstanding objections with evidence
4. Support pricing and packaging discussions
5. Conduct win/loss analysis post-decision

**Templates:** Use `technical_proposal_template.md` for the proposal document.

**Output:** Technical proposal, implementation timeline, risk mitigation plan.

## Python Automation Tools

### 1. RFP Response Analyzer

**Script:** `scripts/rfp_response_analyzer.py`

**Purpose:** Parse RFP/RFI requirements, score coverage, identify gaps, and generate bid/no-bid recommendations.

**Coverage Categories:**
- **Full (100%)** - Requirement fully met by current product
- **Partial (50%)** - Requirement partially met, workaround or configuration needed
- **Planned (25%)** - On product roadmap, not yet available
- **Gap (0%)** - Not supported, no current plan

**Priority Weighting:**
- Must-Have: 3x weight
- Should-Have: 2x weight
- Nice-to-Have: 1x weight

**Bid/No-Bid Logic:**
- **Bid:** Coverage score >70% AND must-have gaps <=3
- **Conditional Bid:** Coverage score 50-70% OR must-have gaps 2-3
- **No-Bid:** Coverage score <50% OR must-have gaps >3

**Usage:**
```bash
# Human-readable output
python scripts/rfp_response_analyzer.py assets/sample_rfp_data.json

# JSON output
python scripts/rfp_response_analyzer.py assets/sample_rfp_data.json --format json

# Help
python scripts/rfp_response_analyzer.py --help
```

**Input Format:** See `assets/sample_rfp_data.json` for the complete schema.

### 2. Competitive Matrix Builder

**Script:** `scripts/competitive_matrix_builder.py`

**Purpose:** Generate feature comparison matrices, calculate competitive scores, identify differentiators and vulnerabilities.

**Feature Scoring:**
- **Full (3)** - Complete feature support
- **Partial (2)** - Partial or limited feature support
- **Limited (1)** - Minimal or basic feature support
- **None (0)** - Feature not available

**Usage:**
```bash
# Human-readable output
python scripts/competitive_matrix_builder.py competitive_data.json

# JSON