You hit export in Affinity and get a CSV. On the surface, it looks complete: names, emails, phone numbers, company names. Then you open it in a spreadsheet and realize the entire relationship graph—the connectors, the history, the interaction depth—is gone. A venture capitalist who knows three board members at five portfolio companies? They're now three separate contacts with no visible connection. A founder who met your CEO at a conference, then emailed, then called? That interaction sequence has collapsed into a single contact record. This is not a bug. It's a structural limit of how Affinity was designed. The platform was built around relationship visualization—a network graph that renders on screen. That graph does not serialize to CSV. When you export, you get the nodes. The edges (the relationships and their metadata) either strip out entirely or flatten into unstructured text fields that are nearly impossible to parse and rebuild. If you're migrating off Affinity—whether to consolidate tools, reduce seats, or move to a platform that bundles sales, support, and finance—losing that relationship depth costs real revenue. You lose deal context. Your reps lose sight of who knows whom. Warm introductions become cold outreach. This playbook shows you exactly what gets orphaned, why it matters, and how to rebuild it in 60 days using a platform designed to preserve relationship depth during migration. The nine fields that don't export from Affinity Affinity's export includes contact basics: name, email, phone, company, title, and custom fields you've manually filled. But the relationship graph lives in metadata Affinity does not make available in CSV format: Relationship type and direction. "Knows," "reports to," "introduced," "referred by." Affinity stores these as edge labels in its graph database. CSV export gives you no way to identify them. Relationship strength and recency. Affinity infers strength from interaction frequency and time decay. Two contacts who emailed five times in the past month have a different relationship than two who last touched three years ago. This signal is invisible on export. Interaction history. You can export the existence of interactions (Affinity logs them), but not the sequence, sentiment, or outcome. A call followed by an email followed by a Slack message tells a different story than three unrelated touchpoints, but the CSV doesn't capture order or context. Interaction source and metadata. Affinity tracks interactions from Gmail, Slack, LinkedIn, and web activity. The source matters—a LinkedIn view is a soft signal; a replied email is a strong one. Export drops the source. Common connections. If Contact A knows Contact B through Contact C, Affinity's graph renders that chain. The export lists all three as separate records with no visible bridge. Entity metadata. Affinity enriches companies with funding stage, headcount, industry, and website. On export, you get only the company name (and sometimes industry). The rest is locked in Affinity's interface. Opportunity relationships. An Affinity opportunity may be connected to five people at the target company. The export gives you the opportunity, but not which contacts are attached or in what role (champion, economic buyer, technical evaluator). List and segment membership. If you've organized contacts into lists ("VCs in Southeast Asia," "Portfolio founders," "2024 warm leads"), that membership doesn't export. You get a CSV of 5,000 contacts with no visible filters. Notes and relationship narratives. Custom fields export; notes do not. If you've written "Met at TechCrunch Disrupt 2023—interested in Series B follow-up," that context stays in Affinity. The core problem: Affinity's strength is visualization of a graph. A CSV is a table. Tables have columns and rows. Graphs have nodes and edges. One does not map to the other without structural loss. Why relationship depth matters—and what you'll lose on migration Losing relationship metadata hits revenue in three ways: 1. Forecast collapse. A ₹50M pipeline built on your venture team's network looks different depending on whether reps can see who they actually know. If Contact A at the target company knows three board members at existing portfolio companies, that's a warm signal worth 15–20% probability bump. Lose the connections, and you're forecasting blind. Your pipeline inflates because you can't separate "we have a meeting" from "we have a real champion." 2. Slowdown in early-stage discovery. In venture, wealth management, and partnership sales, the first weeks of a deal are all about mapping the ecosystem. Who knows whom? Who can make an introduction? If you're rebuilding that map from scratch after migration, you add 2–3 weeks of manual work that your old system did visually. Your deal timelines extend. Your reps spend time on spadework instead of selling. 3. Orphaned warm paths. You have a contact at a target company. You also have a contact at a partner, customer, or inve