How to Connect Affinity CRM Data to Your Quarterly LP Report

It's two weeks before your quarterly LP report deadline. The data is somewhere in Affinity — deal stages, portfolio company updates, LP meeting logs, commitment amounts. It's all there. It has been there all quarter. And yet someone on your team is about to spend a long weekend copy-pasting it into a Google Slides deck, manually reformatting numbers that were already formatted, and reconciling columns that should have aligned automatically.

This is not a data problem. Affinity auto-enriches portfolio company records from 40+ sources including PitchBook, Crunchbase, and Dealroom — continuously, without analyst intervention. It logs every LP email and meeting automatically. It structures pipeline stages, deal values, and relationship strength scoring in a queryable format. Per Affinity's own positioning, the platform auto-generates 80% of the data most VC firms need for an investor update.

The problem is process, not data. This guide walks you through the exact Affinity workflow — data hygiene, Analytics templates, custom dashboards, export, and field mapping — and names the four failure points where firms lose time every quarter. Then it addresses what remains after a clean export: the last mile that's still manual unless you connect Affinity to a reporting layer.

Affinity gives you the data. It does not give you the report.

What Affinity Already Captures (That Most Firms Don't Use)

Before walking through the export workflow, it's worth establishing what data is already in Affinity that belongs in a quarterly LP report. Most firms use Affinity primarily as a deal CRM and underestimate how much LP-relevant data is already structured and queryable.

• Auto-captured relationship history. Every LP email and meeting is logged without manual entry. Your relationship history with LPs — frequency of contact, last interaction date, communication volume — is already structured data. It belongs in the narrative section of any quarterly report as relationship health context.
• Continuously enriched portfolio company profiles. Funding rounds, headcount signals, valuation updates, and competitive intelligence are pulled from 40+ external sources automatically. The raw data quality is high enough to trust in an LP-facing document — if the export workflow is clean.
• Structured pipeline and deal data. Stage assignments, deal values, owner assignments, and close dates are queryable fields. Pipeline movement this quarter — deals advanced, new investments, follow-ons flagged — is a standard LP report section that Affinity can produce directly.
• Relationship strength scoring. Affinity surfaces LPs whose engagement has dropped — useful context when writing the qualitative narrative for a quarterly report, and a natural prompt to schedule a call before the report goes out.

Step 1 — Audit Your Data Before You Export

This is the step most workflow guides skip — and the one that determines whether the rest of the process works. A clean export from dirty data produces a dirty report. IR teams at PE platforms and family offices report losing 5–10 hours per week to CRM hygiene and manual data aggregation. Most of that time is recoverable by front-loading a quarterly data audit instead of discovering gaps mid-export.

Run through this checklist before opening the Analytics tab:

Step 2 — Use the LP Reporting Template (Fastest Path)

Affinity ships an “Export data for an LP update” feature — validation that the use case is confirmed and in-product, not a workaround. For firms whose fund metrics fit a standard template structure, this is the fastest legitimate path to a structured export.

1. 1Navigate to Analytics in the left sidebar.
2. 2On the Analytics landing page, browse the available templates.
3. 3Select a quarterly LP-relevant template — "Quarterly LP Review" or "Pipeline Summary" depending on your fund stage.
4. 4Click "Use Template" — the report auto-populates with your firm's data.
5. 5Preview the output. Verify the data source is pointed at your LP list, not all contacts.
6. 6Click "Export Data" → download as CSV.

Step 3 — Build a Custom Dashboard for Fund-Specific Metrics

Most emerging managers have fund metrics that don't fit a generic template — different investment theses, LP structures, or stage mixes than the standard templates assume. If that's your situation, build a custom report instead of retrofitting a template.

1. 1Click "New Report" → name it specifically (e.g., "Q3 2025 LP Report — Fund II").
2. 2Select data source: your LP list or portfolio company list depending on which section you're building.
3. 3Click "Add Tile" → configure: Metric type (count, sum, or average), Field (e.g. "Commitment Amount," "Invested Capital," "Portfolio Company Stage"), Grouping by Status, Owner, or Time Period → set to "This Quarter."
4. 4Add a second tile for team activity: emails sent, meetings held, notes created this quarter.
5. 5Add a third tile for pipeline movement: deals advanced, new investments, follow-ons flagged.
6. 6Save each tile → save the report.
7. 7Export: "Export Data" → CSV.

Step 4 — Map the Export to Your LP Report Sections

This is where most firms lose the most time — and where the gap between “clean export” and “LP-ready report” becomes visible. The CSV column names from Affinity rarely match the section headers in your LP report template. Every quarter, someone does this translation manually.

Here is the canonical mapping between standard LP report sections and the Affinity fields and export columns that feed them:

The Compliance Angle: Unreconciled Affinity Data Is a Disclosure Risk

There is a subtler problem with manual Affinity exports that most LP reporting guides do not address: data staleness at the time of report generation creates a disclosure risk, not just an accuracy problem.

When you manually export Affinity data for an LP report, you capture a snapshot of that moment. If a portfolio company changed stage two days after the export — a follow-on round closed, a write-down flagged — the report reflects the prior state. If that change is material and an LP makes a commitment decision based on the report's data, the disclosure gap is real.

This matters more as LP sophistication increases. Institutional LPs doing diligence on a Fund II or Fund III raise are reviewing quarterly reports as historical records. A portfolio company status that contradicts public data — because the report was generated before the status updated in Affinity — raises questions about the firm's data discipline.

The fix at the manual level is tight timing: export as close to report generation as possible, and build a reconciliation step into the process. The structural fix is a live data connector that pulls current Affinity state at the time of report generation — not a CSV from last week.

Manual LP reporting costs $74K+/year in adjacent tooling and 2–3 weeks per quarter in analyst time. The data is already in Affinity. The gap is the last mile.

The Last-Mile Problem — And How Ledgerly Solves It

This guide has covered the clean-export workflow. Following every step gets you a structured CSV with accurate, current Affinity data mapped to the right LP report sections. That is genuinely useful. It is also still not an LP report.

Here is what remains after a clean export:

Ledgerly connects directly to Affinity via API — the same data this guide walks you through manually — and closes the last mile. The Portfolio Data Sync connector pulls current Affinity state at report generation time, not a stale CSV. The AI report generation layer produces a branded, ILPA-compliant draft with narrative commentary scaffolded from portfolio company and pipeline data. The compliance audit runs before delivery, not after.

For a sub-$500M emerging manager with a lean team and no dedicated IR staff, the shift is from 2–3 weeks per quarter of manual reconciliation to a review-and-approve workflow measured in hours. The Affinity data you already have — cleaned and structured per this guide — becomes the input. The LP-ready report is the output.

Carta does not connect to Affinity. Allvue connects to Affinity but requires a 6–12 month implementation at pricing built for $500M+ funds. Visible.vc serves startups reporting to VCs — not GPs reporting to LPs. The Affinity-to-LP-report gap is exactly what Carta leaves unresolved.