You've decided to enrich your CRM. You want missing phone numbers filled in, firmographics added to accounts, and verified emails for the contacts that came in with nothing but a name and a company.
Before you send the database to a provider, a few hours of prep work will change your results. Enriching a messy database is painting over rust. The coat looks acceptable for a month, then the underlying problems come back through, except now they're spread across more fields and the invoice already cleared.
This guide covers the prep itself. If what you're scoping is the cleanup as its own engagement, what to expect from a data cleaning project walks through those stages and timelines separately.
Step 1: Deduplicate First
The most important step, and the one most teams skip.
If you hold three records for John Smith at ABC Corp and enrich three, you now own three enriched duplicates. You paid three times for the same data, and merging got harder, because each copy now carries more populated fields to reconcile and more chances for the merge to pick the wrong value.
Duplicates also skew the project's economics quietly.
Action: Run deduplication first. Merge duplicates into golden records. Enrich the survivors.
Step 2: Remove Records That Shouldn't Be Enriched
If a quote is based on submitted-record count, excluding ineligible records before submission can reduce the total. Remove these first:
- Competitors you track for intelligence. Enrich them separately if you need to, on their own list.
- Do-not-contact records. Paying to find a working email for someone who opted out is money spent creating risk.
- Test records created by admins, integrations, or onboarding sessions. Each CRM has a few dozen.
- Out-of-market records from years-old imports. If a segment will not route to a rep, enriching it buys nothing.
- Unmatchable records with no name, no email, and no company. A provider has nothing to match on, so these come back as failures you still paid to process.
That's a meaningful discount on the project before a single field gets appended.
Step 3: Standardize Key Matching Fields
Providers match your records against their database on company name, email domain, and person name. Inconsistent inputs lower the matched share, and unmatched records return empty.
Before submission, standardize:
- Company names: Strip the Inc., LLC, and Corp. variations. Pick one format and apply it everywhere.
- Email addresses: Lowercase everything, trim whitespace, and fix the obvious typos (gmial.com, .con endings).
- Person names: Fix CAPS entries, names sitting in the wrong field, and bare initials where a full first name exists elsewhere in the record.
A provider can fail to match "ABC MANUFACTURING INC." and match "ABC Manufacturing" acceptable. Same company, different match outcome, and the difference was formatting you could have fixed in one pass.
Step 4: Define Which Fields You Need
Enriching each available field is the default providers hope you pick. Resist it. Scope the field list to what your revenue workflows read:
- For outbound sales: Email, direct dial, job title.
- For lead scoring: Company size, industry, revenue range.
- For territory assignment: HQ location, employee count.
- For ABM: Technology stack, funding stage, growth signals.
More fields expand the scope and review work, while unused values can become stale. Prioritize fields tied to a named near-term workflow, define who will use them, and leave optional fields for a separately approved pass.
Step 5: Set Quality Thresholds
Decide what "good enough" looks like before the project starts, in writing:
- Email matched share: Set the threshold from a representative sample of the market and the accepted validation statuses.
- Phone matched share: What direct-dial coverage do you expect?
- correctness standard: How will you spot-check? Define the sample size and what counts as a miss.
Thresholds agreed on before delivery prevent the argument after delivery. Both sides know what the file has to look like for the invoice to be fair.
Step 6: Run a Test Batch
Start with a representative, reversible test batch before releasing the full database:
- Submit the test batch.
- Review match rates per field against your thresholds.
- Review a representative set by hand: verify titles, check phone status, and confirm company data against the company's own site.
- Test email deliverability on a sample send.
- If the batch clears your thresholds, release the full dataset. If it doesn't, you've spent hundreds of dollars learning that instead of thousands.
Step 7: Plan the Import
The enriched file returns, and now it has to go back into the CRM without breaking anything. Decide in advance:
- Field mapping: Which enriched fields land in which CRM properties? Create the custom fields before the file arrives rather than during the import.
- Overwrite rules: Overwrite existing values, or fill empty fields only? Fill-only is safer for rep-maintained fields; overwrite fits vendor-sourced firmographics.
- Backup: Export a full backup before importing. Generally have a rollback point.
- Automation check: After import, test lead scoring, routing, and each workflow that reads the enriched fields. A suddenly-populated field can trigger automations that were dormant while it sat empty.
The Checklist
- Deduplicate the database
- Remove records that shouldn't be enriched
- Standardize company names, emails, and contact names
- Define which fields you need enriched
- Set matched share and correctness thresholds in writing
- Run a representative test batch
- Plan the import: field mapping, overwrite rules, backup, automation check
Two or three hours of prep, and in exchange: higher match rates, a smaller invoice, and an enriched file that imports clean instead of creating the next data project.
Define the return path
Before export, decide where proposed values, evidence, review status, and unresolved exceptions will land on return. Keeping proposals separate from approved production fields makes sampling, rollback, and sign-off much easier.
Frequently Asked Questions
Should I clean my CRM before enrichment?
Yes. Enriching before cleaning means paying to append data to duplicate records, filling fields on contacts that should have been deleted, and standardizing new data differently from old. Deduplicate and prune first, then enrich the records that survive.
What fields should I prioritize for enrichment?
The fields your revenue workflows read: email for outreach, direct phone for calling, job title for routing and scoring, company size for territory assignment, and industry for segmentation. Enrich what your team will use in the next quarter and skip the rest, since each added field raises the per-record spend.
How many records should I enrich at once?
Run a representative test batch first, including clean, sparse, ambiguous, and high-value records. Approve the matching, field, and exception rules before releasing the full database.
Should enriched data overwrite my existing CRM values?
Decide field by field before the import. Fill-empty-only is the safe default for fields your reps maintain by hand, like phone notes and titles they've confirmed on calls. Overwrite makes sense for fields the vendor sources better than your team does, like employee count and industry. Whatever you choose, back up the CRM first so you have a rollback point.
How is preparing for enrichment different from a data cleaning project?
Cleaning is its own project with its own stages: export, deduplication, standardization, verification, re-import. Pre-enrichment prep is a lighter pass with one goal, making your records matchable and worth paying to enrich. Some of the steps overlap, but prep can be done in hours where a full cleaning project runs about a week.