Blueprint Playbook for Aeries

Who the Hell is Jordan Crawford?

Founder of Blueprint. I help companies stop sending emails nobody wants to read.

The problem with outbound isn't the message. It's the list. When you know WHO to target and WHY they need you right now, the message writes itself.

I built this system using government databases, public records, and 25 million job posts to find pain signals most companies miss. Predictable Revenue is dead. Data-driven intelligence is what works now.

Company Overview: Aeries

Website: https://aeries.com

Core Problem: K-12 school districts struggle to manage fragmented student data, reporting compliance, and operational inefficiencies across multiple disconnected systems, forcing administrators to manually track enrollment, grades, attendance, state reporting, and health records—wasting time that should be spent on improving student outcomes.

Product Type: B2B SaaS - Student Information System (SIS)

Target ICP

Industries: Public K-12 Education

Company Size: 50-5000+ employees (varies by district); represents 45%+ of California student population

Operational Context: Public school districts with state reporting requirements (CALPADS, PEIMS), multiple schools requiring centralized student data management, compliance with education data security standards (iKeepSafe, NIST 800-53/171/218), parent communication needs, attendance/enrollment/grades/health record management across campuses

Primary Buyer Persona

Title: Technology Director / IT Leader

Key Responsibilities: Student information system selection and implementation, data security and compliance oversight, system integration across district technology stack, IT staff training and technical support, cloud hosting and infrastructure management, state reporting configuration and maintenance

KPIs: Data security compliance (NIST, iKeepSafe, FERPA), system uptime and reliability, integration efficiency across tools, implementation timeline and budget adherence, user adoption rates across schools, state reporting accuracy

The Old Way (What Everyone Does)

Your GTM team is buying lists from ZoomInfo, adding "personalization" like mentioning a LinkedIn post, then blasting generic messages about features. Here's what it actually looks like:

The Typical Aeries SDR Email:

Subject: Streamline your student data management Hi [First Name], I noticed your district is growing and wanted to reach out about Aeries SIS. We help K-12 districts like yours streamline student data management, improve state reporting compliance, and enhance parent communication. Our platform features: • Centralized student records • Automated state reporting • Parent portal integration • Cloud-hosted security We work with 600+ California districts. Would you have 15 minutes this week to discuss how we can help [District Name]? Best, [SDR Name]

Why this fails: The prospect is an expert. They've seen this template 1,000 times. There's zero indication you understand their specific situation. Delete.

The New Way: Intelligence-Driven GTM

Blueprint flips the approach. Instead of interrupting prospects with pitches, you deliver insights so valuable they'd pay consulting fees to receive them.

1. Hard Data Over Soft Signals

Stop: "I see you're hiring for IT roles" (job postings - everyone sees this)

Start: "Your district missed the December 15th CALPADS certification deadline by 18 days" (state reporting database with exact dates)

2. Mirror Situations, Don't Pitch Solutions

PQS (Pain-Qualified Segment): Reflect their exact situation with such specificity they think "how did you know?" Use government data with dates, record numbers, facility addresses.

PVP (Permissionless Value Proposition): Deliver immediate value they can use today - analysis already done, deadlines already pulled, patterns already identified - whether they buy or not.

Aeries PVP Plays: Delivering Immediate Value

These messages provide actionable intelligence before asking for anything. The prospect can use this value today whether they respond or not.

PVP Public Data Strong (8.8/10)

3 of your schools have 250+ IEPs each

What's the play?

Identify districts with uneven special education distribution across schools. Show them you've mapped which specific schools carry disproportionate IEP loads and understand their coordination challenges.

Why this works

Naming specific schools with exact IEP counts proves you've done detailed research on their district. The coordination workflow insight shows you understand their operational reality, not just surface-level demographics. This level of specificity makes it feel like you're already a consultant working for them.

Data Sources
  1. California DataQuest - special_education_count by school
  2. NCES Common Core of Data - school_names, enrollment_total

The message:

Subject: 3 of your schools have 250+ IEPs each Roosevelt, Jefferson, and Madison schools each manage 250+ IEPs - that's 775 IEPs concentrated in 3 of your 12 schools. I mapped how those 3 schools coordinate with your district-level special education office differently than your other 9 sites. Want the coordination workflow map?
PVP Public Data Strong (8.7/10)

I mapped your 847-student growth by school

What's the play?

Target districts with rapid enrollment growth. Break down exactly where new students landed across their schools to show uneven distribution creates different operational loads per site.

Why this works

The district knows they grew, but likely hasn't analyzed the distribution across schools. Naming specific schools (Lincoln Elementary, Washington Middle) with exact student counts proves you've done the homework they haven't. The insight about uneven distribution creating different processing loads is genuinely useful and shows operational sophistication.

Data Sources
  1. NCES Common Core of Data - enrollment_total by school, year-over-year comparison
  2. California DataQuest - enrollment_by_grade, school-level data

The message:

Subject: I mapped your 847-student growth by school I broke down where your 847 new students landed across your 5 schools - Lincoln Elementary absorbed 203, Washington Middle took 156. That uneven distribution creates different registration processing loads per site. Want the school-by-school breakdown?
PVP Public Data Strong (8.6/10)

I found where your 1,047 absent students cluster

What's the play?

Target districts with chronic absence rates above state average. Show them you've identified which specific schools drive the attendance gap and offer grade-level/student group analysis.

Why this works

The 1,047 number is calculated from their actual data vs. state average - shows analytical work. Naming specific schools (Roosevelt Elementary, Lincoln Middle) proves you've drilled into school-level data. The offer to show grade-level and student group breakdowns provides immediate actionable value for intervention planning.

Data Sources
  1. California CALPADS - chronic_absenteeism by school
  2. California DataQuest - attendance_rates, enrollment_by_grade
  3. Ed-Data.org - demographic_breakdowns by school

The message:

Subject: I found where your 1,047 absent students cluster Your 1,047 chronically absent students (above state average) concentrate in 4 of your 9 schools - Roosevelt Elementary, Lincoln Middle, and 2 others. I can show you which grade levels and student groups drive the attendance gap at each site. Want the school-by-school breakdown?
PVP Public + Internal Strong (8.6/10)

I counted your 2,341 IEPs across 12 schools

What's the play?

Target multi-school districts with high special education populations. Show them you understand their above-average IEP load and offer implementation examples from similar districts.

Why this works

Specific numbers (2,341 students, 12 schools, 18.7%, 687 more IEPs) prove detailed research. The state comparison provides useful context without being generic. The offer to show how similar districts solved this creates immediate practical value. Notes about schools over 250 IEPs show you've identified their specific bottlenecks.

Data Sources
  1. California DataQuest - special_education_count, enrollment_total
  2. NCES Common Core of Data - district enrollment for comparison calculation

The message:

Subject: I counted your 2,341 IEPs across 12 schools At 2,341 special education students across 12 schools, you're averaging 195 IEPs per school - but your data shows 3 schools over 250. I pulled together how 2 districts with similar multi-school complexity centralized IEP compliance tracking. Want those setup examples?
DATA REQUIREMENT

This play assumes Aeries has implementation case studies from districts with high special education populations and multi-school complexity.

Combined with public enrollment and special ed data, this creates district-specific implementation guidance competitors cannot provide.
PVP Public Data Strong (8.5/10)

203 new students at Lincoln Elementary alone

What's the play?

Target districts where one school absorbed disproportionate enrollment growth. Offer workflow examples from similar districts that faced the same concentration challenge.

Why this works

Naming the specific school (Lincoln Elementary) with exact student count (203) and the 26% calculation shows real analytical work. The comparison to other 4 schools averaging 161 provides context. Offering workflow examples from similar districts creates immediate practical value - they can use this whether they buy or not.

Data Sources
  1. NCES Common Core of Data - enrollment_total by school, year-over-year
  2. California DataQuest - enrollment_by_grade by school

The message:

Subject: 203 new students at Lincoln Elementary alone Lincoln Elementary grew by 203 students while your other 4 schools averaged 161 each - Lincoln's carrying 26% more enrollment growth. I can show you how 3 similar-sized districts redistributed registration workflows when one school spiked like this. Want those workflow examples?
DATA REQUIREMENT

This play assumes Aeries has case studies or workflow documentation from other high-growth districts that faced similar enrollment concentration challenges.

Combined with public enrollment data, this synthesis provides implementation guidance unique to Aeries' customer base.
PVP Public Data Strong (8.5/10)

Your chronic absence jumped 7.1 points in one year

What's the play?

Target districts with significant year-over-year increases in chronic absence rates. Offer intervention examples from similar California districts that reversed major attendance declines.

Why this works

The specific rate change (24.1% to 31.2%, 7.1 points in a single school year) creates urgency. Real examples from similar districts that reversed 5+ point increases provide immediate practical value. The connection to early warning systems tied to SIS makes the product benefit clear without being pitch-heavy.

Data Sources
  1. California CALPADS - chronic_absenteeism, year-over-year trends
  2. California DataQuest - attendance_rates by year

The message:

Subject: Your chronic absence jumped 7.1 points in one year Your chronic absence rate grew from 24.1% to 31.2% - that's 7.1 percentage points in a single school year. I pulled how 3 similar California districts reversed 5+ point increases using early warning systems tied to their SIS. Want those intervention examples?
DATA REQUIREMENT

This play assumes Aeries has case studies from districts that improved chronic absence rates using early warning systems integrated with their SIS platform.

This synthesis of public attendance data with internal success stories provides actionable intervention guidance competitors cannot match.
PVP Public + Internal Strong (8.4/10)

Your Spring 1 CALPADS deadline is February 19th

What's the play?

Target districts that missed previous CALPADS deadlines. Provide a 90-day countdown checklist for their next submission deadline with specific data collection points mapped.

Why this works

References their actual late submission (October, 18 days late) to establish credibility. The specific upcoming deadline (February 19th) creates urgency. The 90-day countdown with 14 mapped data collection points provides immediate practical value - they can use this checklist whether they respond or not.

Data Sources
  1. California CALPADS - submission dates and certification status
  2. CDE CALPADS calendar - upcoming deadlines

The message:

Subject: Your Spring 1 CALPADS deadline is February 19th Based on your October submission being 18 days late, I pulled together a 90-day countdown checklist for your Spring 1 CALPADS deadline on February 19th. It maps the 14 data collection points you'll need ready by mid-January. Want the checklist?
DATA REQUIREMENT

This play assumes Aeries has CALPADS preparation templates based on submission requirements and common data collection bottlenecks from their customer base.

This helps recipients meet state deadlines and avoid compliance issues by providing checklist templates only an SIS provider would have.
PVP Public + Internal Strong (8.3/10)

January PEIMS deadline in 47 days

What's the play?

Target Texas districts that missed previous PEIMS deadlines. Provide a countdown checklist for their next submission with specific data checkpoints mapped.

Why this works

The specific upcoming deadline (January 24th, 47 days away) creates urgency. References their actual late Fall submission (11 days late) to establish credibility. The 45-day countdown with 9 mapped data checkpoints provides immediate practical value - a tool they can use today.

Data Sources
  1. Texas PEIMS Standard Reports - submission dates and status
  2. TEA PEIMS calendar - upcoming deadlines

The message:

Subject: January PEIMS deadline in 47 days Your Winter PEIMS submission is due January 24th - 47 days from now. Based on your Fall submission being 11 days late, I built a 45-day countdown with the 9 data checkpoints you'll need. Want the countdown checklist?
DATA REQUIREMENT

This play assumes Aeries has PEIMS preparation templates and countdown tools based on submission requirements and common data collection bottlenecks from their Texas customer base.

This helps recipients meet state deadlines and avoid funding delays by providing checklist templates only an SIS provider would have.

Aeries PQS Plays: Mirroring Exact Situations

These messages demonstrate such precise understanding of the prospect's current situation that they feel genuinely seen. Every claim traces to a specific government database with verifiable record numbers.

PQS Public Data Strong (8.6/10)

Your 18.7% special ed rate across 12 schools

What's the play?

Target multi-school districts with special education enrollment significantly above state average. Show them you understand they're managing far more IEPs than a typical district their size.

Why this works

Specific numbers (2,341 students, 12 schools, 18.7%) prove you've researched their district. The state comparison (13.2% vs 18.7%) provides context. The calculated 687 more IEPs than typical shows analytical work specific to their situation. The routing question is easy to answer and helps identify the right contact.

Data Sources
  1. California DataQuest - special_education_count, enrollment_total
  2. NCES Common Core of Data - district enrollment, school count

The message:

Subject: Your 18.7% special ed rate across 12 schools Your district serves 2,341 special education students across 12 schools - that's 18.7% of total enrollment. State average is 13.2%, meaning you're managing 687 more IEPs than a typical district your size. Who coordinates IEP documentation across your schools?
PQS Public Data Strong (8.5/10)

Your PEIMS Fall submission was 11 days late

What's the play?

Target Texas districts that missed PEIMS submission deadlines. Mirror back the exact late submission with specific dates and consequences to show you understand their compliance stress.

Why this works

Specific dates (October 8th submission vs September 27th deadline, 11 days late) prove you've accessed their actual compliance records. The consequences (funding delay, TEA compliance review) are real and urgent. The routing question about Winter submission creates immediate relevance - that deadline is coming soon.

Data Sources
  1. Texas PEIMS Standard Reports - submission dates
  2. Texas Education Agency - compliance status

The message:

Subject: Your PEIMS Fall submission was 11 days late Your district submitted PEIMS Fall data on October 8th - 11 days after the September 27th deadline. Late PEIMS submissions delay state funding allocations and trigger TEA compliance reviews. Who's responsible for the Winter submission in January?
PQS Public Data Strong (8.4/10)

Your enrollment jumped 847 students in 12 months

What's the play?

Target districts with rapid enrollment growth. Show them you understand the specific administrative burden created by processing hundreds of new registrations.

Why this works

Specific enrollment numbers (6,203 to 7,050, exactly 847 new students) prove detailed research. The growth is accurate but the 212 admin hours calculation feels somewhat generic - could be stronger with district-specific evidence. Easy routing question allows quick response.

Data Sources
  1. NCES Common Core of Data - enrollment_total, year-over-year comparison
  2. California DataQuest - enrollment_by_grade trends

The message:

Subject: Your enrollment jumped 847 students in 12 months Your district enrollment grew from 6,203 to 7,050 students between Fall 2023 and Fall 2024 - that's 847 new registrations. At that growth rate, manual registration processing adds 212+ admin hours per enrollment period. Who's managing the registration workflow now?
PQS Public Data Strong (8.3/10)

847 new students - your 5-school capacity stretched

What's the play?

Target districts where rapid growth is distributed unevenly across schools. Show them you've identified which specific school absorbed the most growth.

Why this works

Specific growth numbers verified from public data (847 total, 169 average per school). The Lincoln Elementary callout (203 students) shows detailed school-level research. The question allows yes/no response and is genuinely useful for understanding their operational capacity. Strong specificity throughout.

Data Sources
  1. NCES Common Core of Data - enrollment_total by school
  2. California DataQuest - enrollment_by_grade by school

The message:

Subject: 847 new students - your 5-school capacity stretched Your district added 847 students across 5 schools in 12 months. That's 169 new students per school on average - your Lincoln Elementary alone grew by 203. Is enrollment processing keeping pace with growth?
PQS Public Data Strong (8.3/10)

Your chronic absence rate jumped to 31.2%

What's the play?

Target districts with significant chronic absence rate increases. Show them the exact year-over-year change and connect it to state compliance requirements (LCAP intervention).

Why this works

Specific rates with exact years (24.1% to 31.2% between 2022-23 and 2023-24) prove detailed research. The LCAP intervention requirement is a real compliance consequence that creates urgency. The "one-third of students" translation helps contextualize the severity. Good routing question about LCAP reporting.

Data Sources
  1. California CALPADS - chronic_absenteeism by year
  2. California DataQuest - attendance_rates

The message:

Subject: Your chronic absence rate jumped to 31.2% Your district's chronic absence rate increased from 24.1% to 31.2% between 2022-23 and 2023-24 school years. At 31.2%, nearly one-third of students are missing 10%+ of school days - triggering LCAP intervention requirements. Who's tracking attendance data for LCAP reporting?
PQS Public Data Strong (8.2/10)

Attendance gap: 31.2% vs 22.8% state average

What's the play?

Target districts with chronic absence rates significantly above state average. Show them exactly how many more students are chronically absent compared to what's expected for their size.

Why this works

Specific comparison (31.2% vs 22.8%) with calculated impact (1,047 more chronically absent students than expected) shows real analytical work. State comparison adds useful context without being generic. Question helps understand their organizational structure for attendance intervention.

Data Sources
  1. California CALPADS - chronic_absenteeism
  2. California DataQuest - state attendance averages

The message:

Subject: Attendance gap: 31.2% vs 22.8% state average Your chronic absence rate is 31.2% compared to California's 22.8% average. That's 1,047 more chronically absent students than expected for your enrollment size. Is attendance intervention centralized or site-based?
PQS Public Data Strong (8.1/10)

3 CALPADS audit findings flagged for your district

What's the play?

Target districts with recent CALPADS data quality issues. Show them the specific audit categories flagged and connect to state intervention consequences.

Why this works

Specific audit categories (enrollment counts, special education service records, English learner classifications) show real research. State intervention threat in 4 other districts creates urgency by showing consequences. Yes/no question about remediation plan is easy to answer. Could be more specific about which October submission.

Data Sources
  1. California CALPADS - data quality audit findings
  2. CDE audit reports - compliance findings by category

The message:

Subject: 3 CALPADS audit findings flagged for your district CDE flagged 3 data quality issues in your October CALPADS submission - enrollment counts, special education service records, and English learner classifications. These same categories triggered state intervention letters in 4 other districts last quarter. Is someone already working the remediation plan?
PQS Public Data Okay (7.9/10)

Your M&O tax rate at $1.04 - financial stress signal

What's the play?

Target Texas districts with high maintenance & operations tax rates combined with late PEIMS submissions. Connect the two data points to suggest strained administrative capacity.

Why this works

Specific tax rate ($1.04) with context (top 15% statewide) shows research. Connecting tax rate to late PEIMS submission provides insight about administrative capacity strain. Somewhat assumes correlation without direct proof, but the pattern is defensible. Good routing question about coordination.

Data Sources
  1. Texas Education Agency - M&O tax rates by district
  2. Texas PEIMS Standard Reports - submission dates

The message:

Subject: Your M&O tax rate at $1.04 - financial stress signal Your district's M&O tax rate is $1.04 per $100 valuation - that's in the top 15% statewide. High tax rates combined with your October 8th late PEIMS submission suggest strained administrative capacity. Is someone coordinating PEIMS and budget reporting?
PQS Public Data Okay (7.8/10)

687 more IEPs than typical district your size

What's the play?

Target districts with special education enrollment above state average. Show them the calculated difference in IEP volume and connect to compliance touchpoint burden.

Why this works

Specific calculation based on their actual data (687 more IEPs). The 12-15 touchpoints feels generic - could be researched better with specific compliance requirements. Question is useful for understanding their organizational setup. Strong opening with concrete number.

Data Sources
  1. California DataQuest - special_education_count, enrollment_total
  2. NCES Common Core of Data - state averages for comparison

The message:

Subject: 687 more IEPs than typical district your size At 18.7% special education enrollment, you're managing 687 more IEPs than the state average district of 12,523 students. Each IEP requires 12-15 compliance touchpoints annually across multiple staff members. Is IEP tracking centralized or school-by-school?
PQS Public Data Okay (7.7/10)

Your October 8th PEIMS miss - I mapped why

What's the play?

Target Texas districts with late PEIMS submissions. Identify the specific data collection bottlenecks that likely caused their delay based on common patterns.

Why this works

References their specific late submission date (October 8th). Three specific bottleneck categories (attendance reconciliation, special program codes, staff FTE reporting) provide actionable insight. The 68% stat feels generic without source attribution. Could be genuinely useful for their next submission.

Data Sources
  1. Texas PEIMS Standard Reports - submission dates
  2. TEA compliance reports - common data quality issues

The message:

Subject: Your October 8th PEIMS miss - I mapped why I traced your October 8th late PEIMS submission back to 3 data collection bottlenecks - attendance reconciliation, special program codes, and staff FTE reporting. Those same 3 categories delay 68% of late PEIMS districts. Want the bottleneck breakdown?
DATA REQUIREMENT

This play assumes Aeries has analyzed common PEIMS submission delays across their Texas customer base and can identify typical bottleneck patterns.

This synthesis of public submission data with internal bottleneck analysis helps districts prepare for future deadlines.

What Changes

Old way: Spray generic messages at job titles. Hope someone replies.

New way: Use public data to find districts in specific painful situations. Then mirror that situation back to them with evidence.

Why this works: When you lead with "Your district missed the December 15th CALPADS deadline by 18 days" instead of "I see you're hiring for IT roles," you're not another sales email. You're the person who did the homework.

The messages above aren't templates. They're examples of what happens when you combine real data sources with specific situations. Your team can replicate this using the data recipes in each play.

Data Sources Reference

Every play traces back to verifiable public data. Here are the sources used in this playbook:

Source Key Fields Used For
California DataQuest enrollment_by_grade, student_demographics, special_education_count, attendance_rates, free_reduced_meals, english_learners California district enrollment trends, special education populations, attendance rates, demographic analysis
California CALPADS student_enrollment, demographics, program_participation, course_completion, chronic_absenteeism, student_incidents, discipline_records, calpads_certification_date State reporting compliance tracking, longitudinal student records, incident/discipline data, certification deadline monitoring
Texas PEIMS Standard Reports district_enrollment, student_demographics, grade_distribution, program_participation, staff_counts, financial_data, test_results Texas district enrollment analysis, PEIMS compliance tracking, staffing and financial data
NCES Common Core of Data (CCD) school_district_name, nces_id, enrollment_total, grade_distribution, teacher_count, student_teacher_ratio, district_locale, funding_revenue National baseline for district characteristics, multi-district systems identification, year-over-year comparisons
Ed-Data.org enrollment_trends, financial_metrics, staffing_data, student_performance, demographic_breakdowns, test_scores California district financial health, enrollment change analysis, staffing turnover patterns
Urban Institute Education Data Portal district_enrollment, demographics, ccd_data, civil_rights_data, school_characteristics Programmatic access to CCD and federal datasets, diversity analysis, school-level disaggregation
California State Controller's Office - LEA Audit Reports district_name, audit_exceptions, compliance_findings, financial_recommendations, audit_status Districts with audit failures, internal control weaknesses, financial compliance issues
Texas Education Agency - Financial & Compliance Reports district_name, financial_status, compliance_status, audit_findings, accountability_ratings Texas district compliance challenges, financial distress signals, accountability status
SchoolDigger API school_names, district_boundaries, enrollment_totals, student_teacher_ratios, demographic_makeup, free_reduced_lunch_percentage Multi-school district identification, school-level enrollment analysis, operational complexity mapping