> ## Documentation Index > Fetch the complete documentation index at: https://docs.sourcemedium.com/docs/llms.txt > Use this file to discover all available pages before exploring further. # Customer Record Enrichment (CDP Foundations) > How SourceMedium can be your customer source of truth for enrichment and audience workflows. SourceMedium’s transformed dataset is designed to be a **central source of truth for your customers**—with stable join keys and normalized tables that make it easier to: * Build a customer 360 (orders, products, geo, acquisition context) * Attach enrichment fields (zero-party, first-party, or third-party) * Create audience-ready customer lists (while respecting privacy requirements) ## What “customer record enrichment” means in practice Most enrichment work comes down to two steps: 1. **Pick a customer-level join key** (usually `sm_customer_key`) 2. **Attach attributes** you want to analyze (or activate) at the customer level In SourceMedium, `your_project.sm_transformed_v2.dim_customers` is the base customer table, and most downstream analysis joins back through `sm_customer_key`. ## Common enrichment sources ### Zero-party (self-reported) Collected directly from customers (post-purchase surveys, quizzes, account creation). This is typically the highest-quality demographic data you can get—because it’s explicit. ### First-party (internal systems you control) Attributes you collect operationally (loyalty tier, VIP status, subscription preferences, customer tags) that can flow into the warehouse via customer/order tags or platform fields. ### Third-party enrichment Purchased demographic/household attributes joined to your customer records via privacy-safe identifiers (often hashed email) or address/phone, depending on the vendor. ### Inference (use cautiously) Heuristic enrichment derived from PII (for example, gender inference from first name). Useful for directional analysis, not ground truth. ## Audience building workflows (warehouse-first) Once attributes are on a customer-level table, you can build segments like: * High LTV customers in a specific region * New customers acquired from a specific campaign strategy * Customers who match a survey persona Those segments can then be exported from your warehouse (commonly using hashed identifiers like `customer_email_hashed`) for downstream activation—if your policies and tools allow. ### Strategy-based “audience” segments via campaign/UTM conventions If you run demographic-targeted (or persona-targeted) campaigns, the most reliable way to measure **long-term LTV** is to encode the targeting strategy into a joinable attribute at purchase time—typically UTMs. For example: * Create distinct ad sets per segment * Add a stable segment label to `utm_campaign` / `utm_content` / `utm_term` * Analyze LTV by that label in the warehouse This produces “LTV by targeting strategy” (joinable to customers/orders), which is usually more actionable than ad-platform demographic breakdowns. Be careful with PII and inferred demographic attributes. Ensure your privacy policy, consent practices, and downstream usage are compliant with applicable laws and platform policies. ## Start here: demographics and joinability The most common question is demographics (age/gender) and whether ad platforms can provide it in a way that supports LTV. What you can (and can’t) do for LTV by demographic Practical tagging patterns for enrichment Customer join keys and identity fields How enrichment fits into SourceMedium’s transformation layer