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.
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 Hipp Health SDR Email:
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.
Blueprint flips the approach. Instead of interrupting prospects with pitches, you deliver insights so valuable they'd pay consulting fees to receive them.
Stop: "I see you're hiring compliance people" (job postings - everyone sees this)
Start: "Your facility at 1234 Industrial Pkwy received EPA violation #2024-XYZ on March 15th" (government database with record number)
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.
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.
PE-backed platforms typically close add-on acquisitions and inherit fragmented EHR landscapes where legacy organizations maintain separate NPIs and systems for 60–180 days post-close. We identify the exact sites still billing under legacy NPIs (via NPPES) and the distinct EHRs they run (via The ABA Index software_tools_used field) to expose integration friction that never appeared in deal financials. This is a proving signal of multi-system overhead, not a generic timing observation.
A COO managing post-acquisition integration has already felt the BCBA supervision pain (toggling between two EHRs) and the payer enrollment complexity (dual NPIs, dual clearinghouses). Naming the exact NPI still live and the specific EHRs by site makes them feel seen—we are describing *their* actual footprint, not a category. The ask is tiny (one-word yes for a map) and the deliverable (24-site NPI + EHR inventory) is immediately actionable for integration planning.
OIG Wisconsin audit found $18.5M in improper payments driven by sessions billed without valid authorization on file. ProviderSpark and NPPES jointly confirm which multi-location ABA organizations hold Medicaid enrollment in Wisconsin. Hipp's aggregated claims data (10+ Wisconsin-billed practices) shows that CO-197 (no authorization on file) represents 34% of first-pass Medicaid denials in that state—directly matching the OIG's documented gap. This bridges public audit findings to a specific recipient's exposure.
A billing team in Wisconsin can instantly verify this finding against their own remit data. Offering the state-level benchmark (34% CO-197) as a curious question (Does that match what your billing team sees?) opens dialogue without pressure. The email names the recipient's exact in-state location count and Medicaid enrollment, making it highly specific. A one-word reply moves the conversation forward; silence means they self-checked internally.
Hipp aggregated first-pass Medicaid denial rates and top CARC denial codes by state from $194M in processed claims, with 10+ Hipp-billed practices per state (or clearly labeled regional fallback).
Assumes 10+ Hipp-billed Medicaid ABA practices in Wisconsin; if fewer, use a clearly labeled regional fallback aggregate. The 34% CO-197 share is Hipp proprietary data; competitors (CentralReach, Rethink, Motivity) do not publish state-level denial-code distributions to their customers.Multi-site ABA organizations operating CentralReach with 4+ bolt-on tools (form builders, CRM, e-sign, LMS, reminders) require new front-office hires to master 6 distinct systems on day one. We identify the exact tool list by combining The ABA Index software listings with Indeed job descriptions (which name required systems). Hipp's migration records show that 13 of 14 organizations arriving with this 6-category stack signature retired all 5 bolt-ons within 90 days—proprietary data on consolidation outcomes that no competitor holds.
An Operations Manager hiring a new intake coordinator faces the hard truth that onboarding now requires six logins and six separate training tracks. Pulling the exact tool list from their own dated job postings makes the observation undeniable. The statistic (13 of 14 retiring everything within 90 days) is specific and remarkable—they will want to verify it in writing. The ask (replacement list) is small and useful for a vendor audit, earning a one-word yes without commitment.
Hipp onboarding records grouped by incoming tool-category signature (EHR, form builder, CRM, e-sign, LMS, reminders, clearinghouse) with 10+ migrations per signature, showing tools retired and consolidation timelines.
The 13-of-14 statistic and 90-day consolidation timeline are proprietary outcomes from Hipp's 50+ onboarded practices. No other vendor publishes disaggregated migration timelines by tool-category signature. This proprietary lens enables the claim to withstand competitor scrutiny.Post-acquisition clinical leadership struggles with the operational reality of supervising across two distinct EHRs (one on legacy acquired platform, one on parent EHR). NPPES and The ABA Index together show exactly which sites remain on the legacy system and how many clinicians must toggle between two interfaces daily. This specific friction is not obvious to deal teams but becomes immediately real to clinical directors.
A BCBA covering both the acquired and parent locations experiences this pain every day—checking treatment plans and authorization units across two systems is cognitively expensive and error-prone. Framing it as a curious question (Does that match what your clinical leads are seeing?) invites validation rather than defensiveness, making a one-word reply easy. The email acknowledges a real problem without offering a solution, which qualifies it as PQS.
Large multi-site ABA organizations relying on CentralReach plus four or more bolt-on tools create operational complexity across 20+ locations. The ABA Index and recent Indeed job postings for operations/billing/intake roles name the exact vendor stack. Hipp's migration records (grouped by incoming stack signature at 10+ migrations per signature) reveal that organizations with this same tool-category combination cut a median of 5 vendor contracts, with the form builder and reminder tool eliminated within 30 days—proprietary consolidation data.
A multi-site operator with 22 locations running on 6 vendors understands the contract and integration overhead. Asking if the vendor count matches (6) invites a curious reply and opens dialogue. The median-5-contracts-cut figure is a number they can use in budget conversations with finance. The assertion that all 14 reference organizations eliminated the form builder and reminder tool within 30 days is specific enough to verify; they will ask for names (aggregate only) or references.
Hipp onboarding records grouped by incoming tool-category signature (EHR, form builder, CRM, e-sign, LMS, reminders, clearinghouse) with 10+ migrations per signature, showing median vendor count eliminated and tool retirement timelines.
The median-5 contract count and all-14 form-builder/reminder-tool elimination within 30 days are proprietary outcomes from Hipp's customer migrations. Competitors cannot replicate this without access to equivalent aggregate migration data across 50+ practices. Messaging avoids industry averages or analyst citations; all figures anchor to Hipp's own customer base.These messages provide actionable intelligence before asking for anything. The prospect can use this value today whether they respond or not.
OIG Medicaid ABA audit series in Wisconsin ($18.5M improper), Colorado ($77.8M improper), and Indiana (completed 2024) identified three precise documentation gaps: expired treatment plans, billed units mismatched to session notes, and missing RBT supervision logs. By joining ProviderSpark (Medicaid enrollment status) and NPPES (practice location count) with Hipp's aggregated claims data (denial codes and frequencies by state across 10+ billed practices), we convert public audit findings into a specific, actionable self-check mapping denial codes to OIG gaps. The proprietary signal is Hipp's own first-pass denial rate and CARC code distribution by state—data no other vendor publishes.
A billing or compliance officer in an audited state holds real fear of recoupment and service disruption. Naming the exact audit findings ($77.8M, three specific gaps) proves we understand the regulatory environment. Revealing that CO-197 (no auth on file) and CO-16 (missing documentation) drive 61% of Medicaid denials across Hipp's Colorado customer base gives them a benchmark they can pull from their own 835s *today* without Hipp. The one-page self-check is immediately valuable and requires only a one-word reply; the email is useful whether or not they respond.
Hipp aggregated first-pass Medicaid denial rates and top CARC denial codes by state from $194M in processed claims, with 10+ Hipp-billed practices per state (or clearly labeled regional fallback).
Proprietary denial-code distributions by state are competitive advantage: CentralReach and other EHRs do not expose denial pattern aggregates to their customers. Hipp's visibility into root-cause denial codes across multi-state Medicaid ABA practices, paired with the recipient's own location count, creates a unique pre-audit diagnostic that competitors cannot replicate.Old way: Spray generic messages at job titles. Hope someone replies.
New way: Use public data to find companies in specific painful situations. Then mirror that situation back to them with evidence.
Why this works: When you lead with "Your Dallas facility has 3 open OSHA violations from March" instead of "I see you're hiring for safety 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.
Every play traces back to verifiable public data. Here are the sources used in this playbook:
| Source | Key Fields | Used For |
|---|---|---|
| NPPES NPI Registry API | organization_name, npi_number, taxonomy_code, practice_locations, address, state, city, postal_code | Identifying multi-location ABA organizations, confirming practice locations, and detecting legacy NPI registrations post-acquisition |
| The ABA Index | organization_name, number_of_locations, states_served, npi_link, company_page, software_tools_used | Mapping multi-location ABA platforms, extracting current EHR and tool stack, and identifying vendor sprawl patterns |
| ProviderSpark | provider_name, npi, address, medicaid_enrollment_status, therapy_types, state, city | Confirming Medicaid enrollment status and filtering Medicaid-dependent ABA providers in audited states |
| NPPES Bulk Downloadable Files | provider_npi, organization_name, practice_location_addresses, primary_taxonomy, practice_location_reference_file | Bulk analysis of multi-location provider registrations and identification of new or unretired NPIs post-acquisition |
| OIG Medicaid ABA Audit Series | audit_status, state_audited, improper_payment_amount, billing_compliance_finding, billing_documentation_gaps | Extracting specific audit findings (state, dollar amount, documentation gaps) to map to recipient exposure |
| Indeed.com - ABA Operations Jobs | job_title, company_name, location, salary_range, posting_date, job_description | Extracting tool requirements from job postings to reconstruct vendor stack and identify administrative scaling signals |
| ABA Resource Center Job Board | job_title, employer_name, location, role_category | Tracking hiring velocity and operations/administrative staffing patterns in ABA organizations |
| Private Equity Deal Flow - ABA Platforms | platform_name, pe_backer, acquisition_targets, transaction_date, multi_state_footprint | Identifying PE-backed ABA platforms in 0–180 day post-close integration windows and acquisition targets |
| LinkedIn Company Data - ABA Organizations | employee_count, recent_hiring, job_openings_by_role, employee_growth_trend, leadership_changes | Confirming multi-site organization scale (500+ employees) and detecting organizational changes |
| Crunchbase - ABA Platform Funding | company_name, funding_round, funding_amount, funding_date, lead_investors | Tracking PE recapitalization and funding announcements as budget-availability and urgency signals |
| ABA Industry Billing Benchmark Data | average_denial_rate, average_ar_days, clean_claim_rate, top_denial_causes | Industry baseline context for billing ROI messaging (not used as primary targeting signal per number integrity rules) |