--- title: AviaryAI Research Intelligence Documents — 90 Tier 1 Accounts type: article created: '2026-04-05' updated: '2026-04-05' source_docs: - raw/2026-02-11-aviaryai-weekly-call-121746286.md tags: - abm - outbound-sales - research - credit-unions - hubspot - client:aviaryai layer: 2 client_source: null industry_context: null transferable: true --- # AviaryAI Research Intelligence Documents — 90 Tier 1 Accounts ## Overview As part of the [[wiki/clients/aviaryai/index|AviaryAI]] ABM buildout, a **Research Intelligence Document** was created for each of the 90 Tier 1 credit union accounts. These documents serve as the foundational layer of the high-touch outreach strategy — giving the sales rep (Aaron) everything needed to personalize outreach before sending a single email or LinkedIn message. Each document is attached directly to the corresponding company record in HubSpot within the [[wiki/knowledge/outbound-sales/abm-pipeline-hubspot-setup|ABM Pipeline]]. --- ## Document Structure Each Research Intelligence Document covers the following sections: ### 1. Company Overview Basic firmographic data: name, location, asset size, member count, employee count, and primary use case match (e.g., new member onboarding, M&A conversion, collections outreach). ### 2. NCUA Financial Snapshot Regulatory financial data pulled from NCUA filings. Provides context on the credit union's scale, growth trajectory, and financial health — useful for framing ROI conversations. ### 3. Technology Stack Assessment Current vendors and platforms in use. Identifies potential integration angles, incumbent displacement opportunities, and technical readiness signals. ### 4. Recent News & Growth Signals Real-time intelligence on the account: leadership changes, branch expansions, merger activity, awards, press releases. Used to identify timely hooks for outreach. ### 5. Signal Assessment Synthesized interpretation of the news and growth signals. Example output: > *"The CEO's industry recognition and emphasis on innovation signals openness to cutting-edge solutions."* This section translates raw data into actionable sales insight. ### 6. Pain Point Hypotheses Structured hypotheses about the account's likely operational challenges, mapped to AviaryAI's capabilities. Example: > *"With 177,000 members across 118 counties and only 618 employees, they cannot scale proactive outreach for collections."* ### 7. Coverage Gaps Identifies areas where the credit union appears underserved or where AviaryAI has a clear wedge — used to sharpen the value proposition. ### 8. Personalization Hooks Specific, human details about key contacts that can be used to open conversations naturally. Designed to make outreach feel researched, not templated. ### 9. Peer Credit Union Context Comparable institutions in the same asset band or geography. Useful for social proof framing ("your peers are doing this") and competitive positioning. ### 10. Proof Point Matching AviaryAI's existing case studies and proof points mapped to this account's specific situation. Surfaces the most relevant evidence to lead with. ### 11. Competitive Strategy Notes Guidance on how to position against likely incumbent vendors or objections specific to this account. ### 12. Source Links Citations for all research used in the document, enabling Aaron to verify or go deeper on any claim. --- ## How These Documents Are Used Research Intelligence Documents are paired with a **Strategy Document** for each account. The strategy doc contains the actual 30-day outreach sequence (emails, LinkedIn steps, cadence notes) and references the research doc for personalization inputs. See: [[wiki/knowledge/outbound-sales/aviary-abm-strategy-docs|AviaryAI ABM Strategy Documents — 30-Day Outreach Sequences]] The workflow is: 1. Aaron reviews the Research Intelligence Doc before engaging an account 2. He uses the personalization hooks, pain point hypotheses, and proof point matches to tailor his approach 3. Pre-written email copy in the Strategy Doc is already informed by this research — but the doc gives Aaron the context to go off-script when needed --- ## HubSpot Integration All 90 Research Intelligence Documents are being uploaded to their respective **company records** in HubSpot. Once attached, Aaron can access the full document without leaving the CRM. Custom HubSpot properties added to each company record surface key excerpts directly in the list view: - `Primary Use Case` - `Campaign Status` - `ABM Tier` - `Score` (0–100 fit score) This means Aaron gets a quick-read summary in the list view and can drill into the full document when preparing for outreach. > **Status (as of meeting date):** Upload of all 90 documents to HubSpot was in progress. Mark Hope was completing this as a follow-up action item. --- ## Scoring & Tiering Accounts were assigned to Tier 1 based on a custom 0–100 fit score. The scoring matrix weights multiple factors (not the HubSpot default lead scoring tool, which was deemed unsuitable for ABM). The full scoring matrix was flagged as a deliverable — Mark to share with Aaron and Justin. Tier breakdown across the 500-account universe: | Tier | Accounts | Treatment | |------|----------|-----------| | Tier 1 | 90 | High-touch ABM with Research + Strategy docs | | Tier 2 | 40 | Future ABM focus | | Tier 3 | 36 | Future ABM focus | | Remaining | ~334 | [[wiki/knowledge/outbound-sales/aviary-tier2-drip-campaign|10-email cold drip via SendGrid]] | --- ## Related - [[wiki/clients/aviaryai/index|AviaryAI Client Overview]] - [[wiki/knowledge/outbound-sales/abm-pipeline-hubspot-setup|ABM Pipeline HubSpot Setup]] - [[wiki/knowledge/outbound-sales/aviary-abm-strategy-docs|AviaryAI ABM Strategy Documents — 30-Day Outreach Sequences]] - [[wiki/knowledge/outbound-sales/aviary-tier2-drip-campaign|AviaryAI Tier 2/3 Cold Drip Campaign — SendGrid]] - [[wiki/knowledge/outbound-sales/linkedin-automation-research|LinkedIn Automation Research]]