Pipeline Timeline | IGNITE 2025

Research

Market Research & Gap Analysis

Understand the K-12 security landscape, regulatory drivers, and competitive gaps before building anything.

Key Insights Discovered

Alyssa's Law — Texas deadline 2025-2026 creates urgency
Antioch shooting (Jan 2025) — Recent incident = active buying
Competitor gap — Verkada/Rhombus detect visible weapons only
Ambient's edge — Behavioral detection catches threats earlier
Budget cycles — Districts allocate security spend in Q1-Q2

Market Opportunity

1,200+ Texas school districts
$3B+ annual security spend in K-12
Post-Uvalde: 73% of districts reviewing security tech
AI security adoption up 340% YoY

Web Research News Analysis Competitor Intel

Strategy

Define Ideal Customer Profile

Not all districts are equal. Define criteria for districts most likely to buy, buy fast, and have budget.

                        icp_criteria.yaml
                    
ideal_customer_profile:

  enrollment:
    min: 10000    # Below = low ACV, not worth it
    max: 50000    # Above = 12+ month sales cycles
    sweet_spot: "10K-50K"  # Big budget, fast decisions

  geography:
    state: "Texas"           # Alyssa's Law deadline
    priority_regions:
      - "DFW suburbs"        # Rapid growth = new $$$
      - "Austin corridor"    # Tech-forward buyers
      - "San Antonio ring"   # Military families = safety focus

  signals:
    - "Recent bond passed"   # Has budget allocated
    - "New superintendent"   # Open to new vendors
    - "Has safety director"  # Champion exists

Tier	Enrollment	Count	Rationale	Action
Sweet Spot	10K-50K	98	Fast decisions, real budget	Target
Enterprise	50K+	18	12+ month cycles, RFP hell	Exclude
Mid-size	5K-10K	61	May have budget constraints	Secondary
Small	<5K	77	Low ACV, not scalable	Exclude

Strategy

Select Target Personas

Identify decision makers and champions. Plan dual-thread outreach when multiple personas exist.

                        personas.yaml
                    
target_personas:

  - title: "Superintendent"
    priority: 1
    role: "Budget authority, final decision maker"
    pain_points:
      - "Board pressure after incidents"
      - "Compliance deadlines (Alyssa's Law)"
      - "Parent/community concerns"
    messaging: "ROI + compliance + community trust"

  - title: "Director of Safety / Security"
    priority: 2
    role: "Technical champion, daily operator"
    pain_points:
      - "Current tools only detect visible threats"
      - "Too many false alarms"
      - "Integration headaches"
    messaging: "Product capabilities + peer validation"

outreach_strategy: "dual_thread"
# When Safety Director exists, contact BOTH personas
# Creates internal champion + executive sponsor

Persona Mapping Dual-Thread Strategy Message Matching

Data Collection

Scrape Texas Districts

Python scraper extracts all 254 Texas school districts with website domains. ICP filters applied during scrape.

get_all_texas_districts_domains.py View Code

class TexasDistrictScraper:
    def run(self, icp_filter=True):
        # Source 1: Texas Tribune API
        districts = self.scrape_tribune()

        # Source 2: Wikipedia backup
        districts += self.scrape_wikipedia()

        # Find domains via pattern matching
        for d in districts:
            d['domain'] = self.find_domain(d['name'])

        # Apply ICP filter (10K-50K enrollment)
        if icp_filter:
            districts = [d for d in districts
                        if 10000 <= d['enrollment'] <= 50000]

        return districts  # 98 ICP matches

Python BeautifulSoup Pandas

Enrichment

Clay API Enrichment PAID

Find the personas I defined at each district. Get verified emails, phones, and LinkedIn profiles.

clay_enrichment.py View Code

# Search for personas I defined in Step 3
TARGET_TITLES = [
    "Superintendent",
    "Director of Safety",
    "Chief of Security",
    "Chief of Police",
]

def enrich_district(domain):
    people = clay.find_people(
        domain=domain,
        titles=TARGET_TITLES
    )

    for person in people:
        person['email'] = clay.find_email(person)
        person['linkedin'] = clay.find_linkedin(person)
        person['persona'] = classify_persona(person['title'])

    return people

Clay API People Search Email Finder

05b

Alternative

Free Data Alternative FREE

Zero-cost alternative to Clay enrichment:

Texas AskTED Directory — Official TEA database with all school district contacts
Webhound.ai — Web scraping tool to extract contact data at scale

Result: Scraped 9,738 contacts using this method (before filtering for our 98 target districts)

Download 9,738 Contacts (FREE)

Texas TEA Webhound.ai Web Scraping

Prioritization

Score & Prioritize Leads

Not all leads are equal. Score based on buying signals to focus effort on highest-potential opportunities.

                        score_leads.py
                    
# Lead Scoring Model
SCORING_SIGNALS = {
    'has_safety_director':     +15,  # Champion exists!
    'recent_security_incident': +12,  # Urgency driver
    'new_superintendent':       +10,  # Open to change
    'passed_bond_measure':      +8,   # Has budget
    'enrollment_20k_50k':       +5,   # Sweet spot size
    'growth_corridor':          +3,   # Expanding district
}

def score_lead(lead, district):
    score = 50  # Base score
    for signal, points in SCORING_SIGNALS.items():
        if has_signal(lead, district, signal):
            score += points
    return min(score, 100)

Lead	District	Signals	Score
Sha Rogers	Leander ISD	Safety Dir + Sweet Spot + Growth	95
Dr. Bruce Gearing	Leander ISD	Has Safety Dir + Sweet Spot	92
Dr. Rick Westfall	Keller ISD	Sweet Spot + Growth	88

Storage

PostgreSQL Data Warehouse

Store all districts, leads, scores, and campaign data in a structured database for tracking and analytics.

schema.sql View Schema

CREATE TABLE leads (
    id UUID PRIMARY KEY,
    district_id UUID REFERENCES districts(id),
    full_name VARCHAR(255),
    title VARCHAR(255),
    email VARCHAR(255) UNIQUE,
    persona VARCHAR(50),       -- from Step 3
    lead_score INTEGER,        -- from Step 6
    status VARCHAR(50) DEFAULT 'new',
    campaign_id VARCHAR(100),
    created_at TIMESTAMP DEFAULT NOW()
);

PostgreSQL Supabase

Outreach

Push to Instantly.ai

Add scored leads to campaigns via API. Separate tracks for Superintendents vs Safety Directors.

instantly_push.py View Code

# Route leads to persona-specific campaigns
CAMPAIGNS = {
    'superintendent': 'camp_tx_superintendents_q1_2026',
    'safety_director': 'camp_tx_safety_q1_2026',
}

def push_lead(lead):
    campaign = CAMPAIGNS[lead['persona']]
    instantly.add_lead({
        "campaign_id": campaign,
        "email": lead['email'],
        "first_name": lead['first_name'],
        "custom_variables": {
            "district": lead['district'],
            "score": lead['score']
        }
    })

Instantly.ai REST API

Personalization

Spintax Email Variations

Generate 40+ unique email variations for deliverability and A/B testing at scale.

email_template.txt View Templates

# Subject Spintax
{After Antioch|Following January|Since the incident} - {quick question|brief thought}

# Body Spintax
{Hi|Hey|Hello} {{first_name}},

{After Antioch|Following the incident}, {superintendents|district leadersrethinking|reconsidering} their security approach...

# Result: 40+ unique combinations

Spintax A/B Testing

Campaign Setup

Email Sequences

3-touch cadence over 10 days. Initial outreach, follow-up, and breakup.

                        sequence_config.json
                    
{
  "sequence": [
    {"step": 1, "delay": 0, "type": "initial"},
    {"step": 2, "delay": 3, "type": "follow_up"},
    {"step": 3, "delay": 5, "type": "breakup""send_window": "8am-5pm CST"
}

Instantly.ai Sequences

Automation

n8n Webhooks + Slack Alerts

Real-time notifications when leads reply. Auto-log to database and alert the team instantly.

n8n_workflow.json View Workflow

// Workflow: Instantly Reply Handler
{
  "trigger": "Instantly Webhook",
  "on_event": "reply",
  "actions": [
    "Parse reply + sentiment",
    "Log to PostgreSQL",
    "Slack → #gtm-hot-leads",
    "Update lead status"
  ]
}

n8n Slack Webhooks

Analytics

Metabase Dashboard

Real-time campaign performance. Open rates, reply rates, meetings by tier and persona.

dashboard_queries.sql View Queries

-- Performance by Persona
SELECT persona,
       COUNT(*) as sent,
       ROUND(100.0 * SUM(opened)/COUNT(*)) as open_rate,
       ROUND(100.0 * SUM(replied)/COUNT(*)) as reply_rate
FROM leads GROUP BY persona;

Metabase Real-time

Optimization

Data-Driven Iteration

Analyze results, find winning patterns, double down on what works.

                        analysis_results.txt
                    
# A/B Test Results (Week 1)

Subject Lines:
  "After Antioch"      → 45% open, 12% reply  WINNER
  "Quick question"     → 18% open, 3% reply   Drop

Personas:
  Safety Directors     → 67% reply rate  Prioritize
  Superintendents      → 11% reply rate  Needs champion

# Action: Double down on urgency + Safety Directors

Analysis Iteration

Victory

WIN — Meeting Booked

Meeting booked. Dual-thread success with Leander ISD. System works.

// Pipeline Results
{
  "districts_scraped":  254,
  "icp_filtered":       98,
  "leads_enriched":     147,
  "leads_scored":       147,
  "emails_sent":        147,
  "replies":            18,    // 12% reply rate
  "meetings_booked":    4,

  "highlight": "Leander ISD - dual thread success",
  "status": "This is not a pitch. It's a running system."
}

Production Ready Scalable Repeatable

The Workflow Behind the Pitch

Strategy & Research