Change Detection Methodology

Change detection methodology is the systematic process for identifying, validating, and propagating entity updates—monitoring role changes, mandate shifts, contact updates, and firm events—so intelligence stays current and teams avoid outreach errors from stale data.

Without change detection, data rots. The CIO you researched 8 months ago left the firm. The mandate you documented last year shifted to direct investing. The phone number in your database disconnected 6 months ago. With change detection, you're running monitoring workflows: quarterly re-verification for high-value contacts, automated alerts for role changes (LinkedIn signals, news mentions, regulatory filings), and systematic recency flagging for data >12 months old.

This is a targeting accuracy issue. Stale data produces failed outreach (bounced emails, wrong contacts) and credibility damage (LPs notice when you don't know they changed roles). Strong change detection preserves data value over time.

How allocators define change detection risk drivers

Teams structure change detection through:

• Automated monitoring (ongoing): Set alerts for entity names, key people, firm domains in: news (Google Alerts), regulatory filings (EDGAR, Form ADV updates), LinkedIn (job changes), event rosters
• Quarterly re-verification (high-value entities): Re-check role, firm, contact accuracy for Tier 1 targets (top 100 LPs) every 90 days via multi-source triangulation
• Recency flagging (systematic): Auto-flag records >12 months old for re-verification; prioritize contacts >18 months old for immediate review
• Change validation (triggered): When alert fires (LinkedIn update, news mention), validate via Tier 1-2 source before updating record (don't trust single unverified signal)
• Update propagation (controlled): When change confirmed, update master record + add change history note + trigger outreach team notification if affects active target
• Detection cadence: Tier 1 (top 50 targets) = monthly monitoring + quarterly re-verification; Tier 2 (next 100) = quarterly monitoring + semi-annual verification; Tier 3 (active pipeline) = semi-annual monitoring + annual verification; Tier 4 (inactive) = annual monitoring only
• Evidence phrases: "change detected," "role update," "last verified," "change history," "monitoring alert," "re-verification trigger"

Allocator framing:
"How do we ensure our intelligence stays current—or are we relying on aging data that's no longer accurate?"

Where it matters most

• high-velocity roles (investment team members change frequently)
• mandate evolution (family offices adding/removing strategies)
• contact accuracy (email domains change with firm rebrands)
• relationship continuity (tracking people across firm changes)

How it changes outcomes

Strong change detection discipline:

• prevents outreach to exited employees (preserves credibility)
• identifies mandate shifts that create new opportunities
• updates contact information before bounce rates spike
• tracks key people across firm changes (preserves relationships)
• maintains data accuracy over multi-year fundraising cycles

Weak change detection discipline:

• embarrassing wrong-person contact ("that person left 18 months ago")
• missed mandate shifts (pitching old strategy to LP who moved on)
• high email bounce rates from outdated addresses
• lost relationships when key people change firms
• data decay eroding targeting accuracy over time

How allocators evaluate change detection discipline

Confidence increases when teams:

• show automated monitoring infrastructure (alerts, triggers)
• demonstrate tiered re-verification cadence (quarterly for top targets)
• flag data staleness explicitly (last verified date on every record)
• validate changes via Tier 1-2 sources before propagating
• maintain change history (tracking entity evolution over time)

What slows decision-making

• no automated monitoring (manual checking only)
• treating verification as one-time (set and forget)
• accepting single-source change signals without validation
• no systematic recency flagging (don't know what's stale)
• missing change history (can't track entity evolution)

Common misconceptions

"Once verified, always accurate." → Roles, mandates, contacts change constantly; detection is ongoing.
"LinkedIn updates are auto-trust." → Validate role changes via regulatory filing or news before updating record.
"Only monitor during active fundraise." → Continuous monitoring prevents data decay between fundraises.

Key allocator questions during diligence

• What automated monitoring infrastructure do you use?
• What is your re-verification cadence for different entity tiers?
• How do you validate changes before propagating updates?
• What recency threshold triggers re-verification?
• How do you maintain change history to track entity evolution?