Apollo Enrichment: Clean and Enrich a Prospect CSV

Direct answer: Apollo CSV enrichment is useful when you already own or lawfully handle a B2B file and need to fill or refresh specific person and company fields. Do not upload the raw export immediately. First normalize domains and names, remove exact and probable duplicates, define the fields you actually need, and keep a stable row ID. In Apollo, map the strongest identifiers, review the credit estimate, start with Apollo-only enrichment, and confirm a small batch. Treat matched data as a research input. Verify the current employer, role, company fit, and contact route before any outreach.

This page has one narrow job: clean and enrich an external prospect CSV without losing control of identity, credits, or verification. It does not teach bulk scraping, CRM enrichment, Apollo's enrichment API, or how to build a market from scratch. Start with the B2B prospect-list method if your ICP and exclusions are still undefined.

Choose CSV enrichment only when the file is the system of work

Apollo separates CSV enrichment from CSV import. Enrichment adds Apollo data to an external file that you download. Import creates or updates records inside Apollo. The distinction matters because an operator who only needs a reviewed output file should not create a second record system by accident.

Apollo's official enrichment overview describes the same route boundary. Pick the place where the record already lives, then use the smallest enrichment workflow that keeps ownership clear.

CSV enrichment was blocked before the trial in the checked Free workspace

The FloxoLab Apollo workspace was checked on August 26, 2026 before the Professional trial was activated. It was on the Free plan, the account header showed 30 remaining credits, and the Free plan card listed 75 credits per seat per month granted upfront. Opening the Data enrichment route redirected to the upgrade screen.

On the live monthly plan screen, Basic was listed at US$65 per seat per month with 2,500 credits and CSV enrichment. Professional was listed at US$99 with 4,000 credits. The same screen described enrichment as a 1-to-8-credit action. Prices exclude applicable taxes and may change. Check the current account screen before upgrading because plan labels, allowances, and credit rules are volatile.

The later plan-comparison matrix showed CSV Enrichment as available across its plan columns, which did not match what I could access in the Free workspace. Because Apollo's permissions and plan presentation can vary by account, I would trust the capability you can actually open in your workspace over a comparison-table checkmark.

Prepare a 10-to-20-row owned test file

Apollo documents much larger technical limits, but those limits are not a sensible first test. Use 10 to 20 representative rows from a file your business is allowed to process. Include a few complete rows, incomplete rows, suspected duplicates, and intentionally difficult matches. That exposes the workflow without turning a setup mistake into a large credit event.

Keep only the fields required for matching and evaluation:

• input_record_id: your stable internal key, never a row number that changes after sorting;
• first_name and last_name: split into separate fields when available;
• company_name and company_domain: use the current business, not a free-form note;
• person_linkedin_url: include it only when it came from a permitted source and is current;
• existing_business_email: useful as a match key and comparison field when lawfully held;
• source and checked_at: keep these in your internal evidence file so you can explain where the row came from and when it was last reviewed.

Normalize and deduplicate before Apollo sees the file

Enrichment does not repair a weak identity model. A company may appear as a legal name, trading name, website URL, and domain. A person may have spacing, punctuation, middle-name, title, or employer variations. Normalize predictable formatting before matching so you can distinguish an Apollo miss from an input-quality problem.

1. Trim whitespace and remove invisible characters from every identifier.
2. Lowercase domains and email domains. Remove protocols, paths, tracking parameters, and a leading www. from company URLs.
3. Keep original values in separate columns. Never destroy the source while normalizing it.
4. Collapse exact duplicates by your strongest permitted key, such as an existing business email or person LinkedIn URL.
5. Flag probable duplicates when normalized name plus company domain match. Route conflicts to review instead of guessing.
6. Preserve one stable input ID so the downloaded result can be joined back to the correct source row.

If this stage needs automation, use the identity-key and conflict-routing pattern in How to Deduplicate Leads Before They Reach Your CRM. Do not expect the destination tool to be your first duplicate guard.

Map the strongest identifiers in Apollo

Apollo's current CSV enrichment guide accepts one of four matching routes for people: first name plus last name plus company URL, first name plus last name plus company name, person LinkedIn URL, or email. More relevant data can help matching, but more columns do not automatically mean a safer match.

1. Open Data enrichment, choose CSV, and start an import for enrichment.
2. Select contact or company enrichment. Do not mix the two jobs in one test conclusion.
3. Edit the output field selection. Request only the fields needed for your next decision.
4. Select the CSV and map each source column to the correct Apollo field.
5. Review mappings for domains, LinkedIn URLs, emails, and company names row by row when the file is small.
6. Choose whether email, mobile, or both are required. Phone options and multi-source availability depend on plan and access.

Apollo documents a maximum of 50 MB and 100,000 rows for this CSV enrichment route. It also notes that multi-source enrichment is disabled above 50,000 rows. Those are platform ceilings, not quality recommendations. Small teams should prove the match and verification process long before approaching either number.

Preview the credit decision before confirming

The confirmation screen is a budget boundary, not a routine click. Record the account plan, available enrichment credits, selected fields, email or phone options, Apollo-only or multi-source mode, and the displayed estimate. Apollo says multi-source enrichment can be more credit-intensive and may require admin approval. Start Apollo-only unless a failed, measured test gives you a reason to add other providers.

Waterfall and API costs are not one flat enrichment rate

The August 26 account explanation showed different rules for Apollo data, external providers, optional validation, phone data, and API endpoints. That is why your test record should keep the selected sources and requested fields beside the result.

Read matched, duplicate, and not-found as different outcomes

A high match count is not the same as a high usable-record count. Apollo's CSV report separates matched records, duplicate records, and not found. It also shows a before-and-after completeness view. Use those categories to diagnose the file instead of compressing everything into one percentage.

Calculate at least four rates: matched rows divided by submitted rows, usable rows divided by submitted rows, manually verified rows divided by checked rows, and credits consumed divided by usable rows. The final number is more useful than cost per match because a matched but wrong employer does not help the workflow.

Manually verify the rows that could reach outreach

Apollo's terms say its data may contain duplicates, errors, or omissions and place verification and lawful use on the user. For a small test, inspect every enriched row. For a later production batch, manually check a defined sample plus every high-value, conflicting, or uncertain row.

• Confirm the company still fits the intended market and exclusions.
• Confirm the person currently works at that company in the relevant role.
• Compare the enriched value with the original field instead of silently overwriting it.
• Keep email status and phone type distinct. Do not flatten every contact route into one field.
• Preserve opt-outs and suppression records in the system that controls outreach.
• Route uncertain matches to a human queue. Do not invent confidence from a populated cell.

Access to a business contact record does not create permission to contact someone. Apply the marketing, privacy, provider, and regional rules that govern your actual use case. Keep the first outreach step reviewed and narrow.

What not to do

• Do not upload a purchased or scraped consumer list because the file happens to be a CSV.
• Do not include sensitive personal information or internal notes that Apollo does not need for matching.
• Do not skip local deduplication and treat Apollo's duplicate category as the primary control.
• Do not enable every output field, phone option, and multi-source provider for the first test.
• Do not click Confirm before recording the credit estimate and selected settings.
• Do not overwrite the source file. Keep raw, normalized, submitted, and returned versions separate.
• Do not auto-enroll every matched row into a sequence.
• Do not report match rate as data accuracy, deliverability, reply rate, or revenue.

Use this pass-or-stop checklist

1. Input passed: the sample was lawfully handled, minimized, normalized, and deduplicated.
2. Mapping passed: identifiers and requested fields were reviewed before submission.
3. Credit passed: the displayed estimate was acceptable and recorded.
4. Output passed: matched, duplicate, and not-found rows can be joined back to stable source IDs.
5. Quality passed: the manual check found enough current, usable records for the actual business task.
6. Workflow passed: uncertain rows, opt-outs, failures, and later corrections have an owner.

Stop if the test cannot explain why records failed, what a usable result costs, or how corrections flow back upstream. Enrichment should reduce uncertainty. A larger file that hides the uncertainty is not progress.

Sources checked

Product behavior, credit boundaries, reporting, privacy, and legal-use limits were checked against official Apollo sources on August 26, 2026, and the plan and credit figures were re-confirmed on Apollo's pricing page on September 19, 2026. Plans, permissions, fields, credits, and multi-source behavior may change.

• Apollo Help: CSV enrichment fields, identifiers, mapping, limits, credits, and download workflow
• Apollo Help: enrichment workflow types and route selection
• Apollo Help: matched, duplicate, not-found, and completeness reporting
• Apollo Help: current credit-usage categories and review screen
• Apollo release notes: CSV upload with native and waterfall enrichment
• Apollo terms: data limitations, verification, and lawful-use responsibility
• Apollo privacy policy: business-contact data and customer-provided information