SaaS Lead List Export Formats: CSV, JSON & CRM-Ready Structures | Dievio

You’ve built a tight ICP, applied the right filters, and previewed a solid count of SaaS leads. Now you need to get that list into your outbound stack. The export format you choose determines whether your CRM import succeeds on the first try, whether your enrichment tool can parse the data, and whether your sales team can act on the list without manual cleanup.

Exporting SaaS lead lists sounds simple until your CSV breaks CRM imports, your JSON doesn’t match your enrichment tool’s schema, or your sales team can’t use the data without hours of reformatting. This article walks through the three main export formats—CSV, JSON, and XML—and shows how to structure each for specific outbound tools like HubSpot, Salesforce, Apollo, Clay, and custom API workflows. You’ll get field-by-field guidance, a format comparison table, a pre-export checklist, and a workflow for building CRM-ready lead lists from scratch.

Before diving into field mappings, it helps to understand the strengths and tradeoffs of each format. The table below compares CSV, JSON, and XML across the dimensions that matter most for outbound operations.

For most SaaS outbound teams, CSV is the workhorse format for CRM imports and email sequencing, while JSON is the go-to for API-driven enrichment and automation workflows. XML remains relevant for specific enterprise integrations but is rarely the first choice for new lead list exports.

CSV (comma-separated values) remains the most compatible format for CRM imports and email sequencing tools. Its simplicity is its strength: a plain-text table with headers and rows. But that simplicity also creates pitfalls if you don’t structure the file correctly.

A well-formed SaaS lead list CSV should include at minimum these fields:

• First Name – Required for most CRM imports
• Last Name – Required for most CRM imports
• Email – Primary identifier; must be valid and unique per record
• Company – Company name; helps with deduplication and account mapping
• Job Title – Critical for segmentation and personalization

Optional but highly recommended fields include:

• Phone – Include with country code; format consistently
• LinkedIn URL – Useful for enrichment and manual research
• Company Size – Employee count range
• Industry – Standardized (e.g., “Software”, “FinTech”)
• Company Revenue – Annual revenue range
• Location – City, state, country
• Company Domain – Separate from email domain for account-level analysis

Use commas as delimiters unless your data contains commas (e.g., “New York, NY”). In that case, wrap the field in double quotes. Always export with UTF-8 encoding to avoid character corruption with special characters (accents, em dashes, etc.). Most modern CRMs expect UTF-8; if you export as ANSI or Latin-1, you’ll see garbled text on import.

• Extra columns – Remove any fields you don’t plan to use. Extra columns confuse mapping wizards.
• Inconsistent headers – Use the exact field names your target CRM expects (e.g., “First Name” vs “FirstName”).
• Missing required fields – Every row must have at least email or a name+company combination for CRM deduplication.
• Trailing commas – These create empty columns that can shift data in imports.

For a deeper dive on building ICP-aligned lead lists before you export, see our guide on how to build B2B lead lists for SaaS companies.

JSON (JavaScript Object Notation) is the preferred format for programmatic workflows. It supports nested structures, arrays, and key-value pairs, making it ideal for representing complex relationships like multiple contacts at one company or custom enrichment fields.

For most outbound use cases, a flat JSON structure (one object per lead) is easiest to work with. Example:

<code>{
  "first_name": "Jane",
  "last_name": "Doe",
  "email": "jane@example.com",
  "company": "Acme SaaS",
  "job_title": "VP of Sales",
  "company_size": "51-200",
  "industry": "Software"
}</code>

If you need to export multiple contacts per company, a nested structure with an array of contacts under a company object may be appropriate. However, most enrichment tools (Clay, Zapier) expect flat arrays for processing.

Consistency matters. Use either snake_case (e.g., first_name) or camelCase (e.g., firstName) throughout the file. Many APIs prefer snake_case for readability, but check your destination tool’s documentation. Avoid mixing conventions—it breaks automated mapping.

If a field is missing, use null rather than an empty string. This allows enrichment tools to detect gaps and fill them. For fields that can have multiple values (e.g., phone numbers), use an array: "phones": ["+14155551234", "+442071234567"].

JSON is the natural choice when you’re feeding leads into a platform like Clay for enrichment, or when you’re building a custom API integration. For a comparison of how Dievio fits into enrichment workflows alongside tools like Clay, see Apollo vs Clay vs Dievio: Choosing the Right Tool for Lead Search vs Enrichment Workflows.

Each CRM has specific field requirements for lead imports. Mapping your export fields to the CRM’s expected schema is the difference between a clean import and a messy one that requires hours of cleanup.

For additional context, see LinkedIn Sales Solutions on lead scoring.

HubSpot’s standard lead import expects these fields:

• First Name – Maps to firstname
• Last Name – Maps to lastname
• Email – Maps to email (used for deduplication)
• Company Name – Maps to company
• Job Title – Maps to jobtitle
• Phone – Maps to phone
• Website – Maps to website

You can also map custom properties if you’ve created them in HubSpot (e.g., “Company Size”, “Lead Source”). During import, HubSpot will ask you to match your CSV columns to its properties. Use the exact property internal names to avoid mismatches.

HubSpot’s guide to sales prospecting emphasizes the importance of clean data at the point of import—duplicate handling and field validation happen before the lead enters the CRM.

Salesforce’s Lead object has standard fields that must be populated for successful conversion:

• FirstName – Required
• LastName – Required
• Email – Strongly recommended for deduplication
• Company – Required (even if it’s the same as the account name)
• Title – Maps to job title
• Phone – Optional but useful
• LeadSource – Use to track where the lead came from

Salesforce also requires field type matching. For example, a date field must be in YYYY-MM-DD format. If your CSV has “MM/DD/YYYY”, the import will fail. Use the Salesforce guide to B2B lead generation for best practices on structuring lead data.

• ☐ Verify field names match CRM property names exactly
• ☐ Ensure all required fields are populated in every row
• ☐ Remove duplicate emails before import
• ☐ Format dates as YYYY-MM-DD
• ☐ Use consistent phone number format (e.g., +1XXXXXXXXXX)
• ☐ Test import with a small sample (10-20 rows) first

Before you hit export, run through this validation checklist. It prevents downstream enrichment failures and CRM sync errors that waste time and credits.

• Email format validation – Check for missing “@”, invalid domains, or spaces. Use a regex or validation tool.
• Company name normalization – Standardize abbreviations (e.g., “Inc.” vs “Incorporated”) to avoid duplicate accounts.
• Job title standardization – Map variations like “VP Sales”, “Vice President of Sales”, “Sales VP” to a consistent format if your CRM uses picklists.
• Phone number formatting – Ensure country codes are present and consistent. Remove dashes and parentheses if your CRM expects a plain string.
• Duplicate detection – Remove rows with identical email addresses. Keep the most complete record.
• Required field completeness – Every row must have at least email or name+company. Flag rows missing critical fields.

For a deeper look at data quality before purchase, read B2B Data Coverage, Accuracy, and Validation: What to Check Before You Buy.

Follow this six-step workflow to go from lead search to a clean, CRM-ready export file.

1. Define ICP and apply filters – Use a lead search tool with 20+ filters (industry, company size, job title, technology, funding stage) to narrow your list. Preview counts to ensure you’re targeting the right segment.
2. Preview and validate counts – Before spending credits, preview the estimated number of leads. Check that the sample matches your ICP. Adjust filters if needed.
3. Select export format based on destination tool – Choose CSV for CRM imports, JSON for API workflows, or XML for legacy systems.
4. Map fields to target schema – Align your export field names with the destination tool’s expected properties. Use the field mapping guidance above for HubSpot and Salesforce.
5. Run validation checks – Apply the pre-export checklist. Fix any issues before downloading the final file.
6. Import and verify – Import a small sample first. Check for errors, missing fields, and duplicates. Once clean, import the full list.

This workflow ensures you don’t waste credits on unusable exports and that your sales team gets data they can act on immediately.

Even with careful preparation, export errors happen. Here are the most common ones and quick fixes.

• Encoding errors – Characters like “é” or “—” appear as “é” or “—”. Fix: Export with UTF-8 encoding. Re-encode the file if necessary.
• Missing required fields – CRM import fails because a required column is empty. Fix: Fill in missing data using enrichment or remove those rows.
• Duplicate records – Same email appears multiple times. Fix: Deduplicate before import. Use email as the unique key.
• Date format mismatches – “01/15/2025” instead of “2025-01-15”. Fix: Convert all dates to ISO 8601 (YYYY-MM-DD).
• Special character handling – Commas in fields break CSV parsing. Fix: Wrap fields containing commas in double quotes.
• Oversized file imports – CRM rejects files over a certain row limit (e.g., 10,000 rows in HubSpot free tier). Fix: Split the file into smaller batches.

Different outbound tools have different format preferences. Here’s a quick guide.

• Apollo – CSV is preferred. Apollo’s import wizard expects standard fields like first name, last name, email, company, and title. You can also upload a CSV with custom fields and map them manually.
• Clay – JSON is ideal for enrichment tables. Clay’s API and spreadsheet-like interface handle nested JSON well. Use flat JSON with snake_case keys for best results.
• Outreach / Salesloft – CSV with sequence-specific fields. Include columns for “Sequence Name”, “Step Delay”, and custom variables like “{{first_name}}”.
• Custom API integrations – JSON with webhook delivery. Structure the payload as an array of lead objects. Include a unique ID per lead for idempotency.

For a broader comparison of lead search and enrichment tools, see Apollo vs Clay vs Dievio.

Choosing the right export format is about matching your destination tool’s expectations. Here are the key takeaways:

• Choose CSV for CRM imports – It’s the most compatible format for HubSpot, Salesforce, Outreach, and Salesloft. Keep headers consistent and validate before importing.
• Choose JSON for programmatic workflows – Use JSON when feeding leads into enrichment APIs, webhook-based automation, or tools like Clay. Stick to flat structures and consistent naming.
• Always validate before export – Run the pre-export checklist to catch errors early. A few minutes of validation saves hours of cleanup.
• Maintain consistent field naming across exports – Use the same field names every time you export. This makes it easy to build reusable import templates in your CRM.

When you’re ready to build and export a SaaS lead list that’s CRM-ready from the start, try Dievio’s SaaS lead list builder. You get 20+ filters, preview counts before spending credits, and exports in CSV or JSON with field mappings for HubSpot and Salesforce. No cleanup required.

Build Your First Outbound List to validate the segment before you commit to full outreach.