You raised a valid concern on today's call: Apollo data hasn't always been as fresh as you need, particularly for LinkedIn-sourced fields like current employer and email. This guide covers how Waterfall enrichment improves accuracy - and how to share feedback so Apollo's team can address specific gaps.
Data quality concern raised on the call
You mentioned two specific issues Apollo should know about:
Issue 1LinkedIn name matches at ~90% but the associated email is for a different person - identity mismatch between name and email record
Issue 2Job/company data not reflecting recent changes - contacts still showing previous employer after a move Apollo hasn't caught yet
Paul committed to sharing examples with the Apollo data team and will follow up with context on the recent dataset refresh and roadmap. Send specific examples to Paul - the more specific, the faster the team can debug.
What's in this guide
Part 1
What Waterfall is
How Apollo's multi-source enrichment works and why turning it on for your job change workflows improves accuracy.
Part 2
Enable & configure
Step-by-step: turn on Waterfall enrichment for your account and attach it to your job change workflows.
Part 3
Flagging data issues
How to report inaccurate records to Apollo so the team can investigate - and how to structure examples for the fastest response.
Part 4
Apollo's data roadmap
What Apollo recently refreshed, what's coming, and why the job change use case specifically benefits from the recent changes.
Part 1
What Waterfall enrichment does
Waterfall enrichment means Apollo queries multiple data providers in sequence - if the first source doesn't have a high-confidence match, it automatically falls through to the next source. You get the best data available across all of them, not just Apollo's own database.
How the cascade works
1
Apollo database
Primary source - 275M+ profiles
First attempt
↓ if no match or low confidence
2
Partner data source 1
Email verification + company data
Fallback
↓ if still no match
3
Partner data source 2
Title + seniority + LinkedIn signals
Fallback
↓ if still no match
4
Best available result returned
Highest confidence match from any source
Output
Why this matters for your job change use case
Without Waterfall, Apollo only checks its own database when a job change is detected. If Apollo's own record for that contact is stale, you get stale enriched data back.
With Waterfall enabled, Apollo cross-checks the enrichment result against multiple sources before writing it - dramatically reducing the chance of a LinkedIn mismatch or stale email landing in your Salesforce.
Part 2
Enable Waterfall enrichment
Waterfall enrichment can be turned on account-wide or on a per-workflow basis. For Testsigma, the recommended approach is to enable it specifically on your job change workflows so it fires on every enrichment triggered by a detection event.
Account-level activation
1
Go to Enrichment Settings
Navigate to Settings → Enrichment → Waterfall. You'll see a list of available data sources and the current on/off state.
2
Enable Waterfall for email and title fields
Toggle on Waterfall specifically for Email and Job Title / Company fields - these are the two fields most critical to your job change workflows and the areas where you've seen quality issues.
Enable waterfall for: ✓ Email (work) ✓ Current company / organization ✓ Job title // Leave off for fields you're not pushing to SFDC - saves credits
3
Attach to job change workflows
Open each of your job change workflows (Workflow A and Workflow B). In the enrichment step settings, confirm Waterfall: On is selected. If it shows "Apollo only," switch it to "All sources."
Credit note
Waterfall enrichment uses slightly more credits per contact when it falls through to a secondary source. Given you have 13-14k contacts needing enrichment, run a small batch first to baseline your credit consumption before opening it to the full database.
✓
Verify with a test contact
Pick a contact where you already know the current correct company and email. Run manual enrichment on them and check that the result matches. This is your ground truth test before bulk enabling.
Part 3
How to flag data quality issues
The fastest way to get bad records fixed is to submit specific, structured examples to Apollo. Here's how to do it - and what Paul's team needs to investigate the LinkedIn identity mismatch issue you raised.
Flagging a bad record in Apollo
1
Find the contact in Apollo
Navigate to the contact whose data is incorrect. Open their full profile page.
2
Use the "Flag" or "Report Issue" button
On the contact profile, look for the ⚑ Flag option (usually next to the email or data field). Select the issue type - "Wrong email", "Wrong company", or "Not this person."
3
Send examples directly to Paul
For the LinkedIn identity mismatch issue specifically, Paul asked for examples. Use this format when sending:
Contact name:[Name in Apollo] Apollo email shown:[what Apollo shows] Correct email:[what you know to be right] LinkedIn URL:[LinkedIn profile, if you have it] Apollo contact URL:[copy from your browser]
Why this matters
The identity mismatch issue (90% name match, wrong email) suggests a record linkage problem in Apollo's disambiguation model. Specific examples let the data team trace the exact merge/link that went wrong and fix it at the source - not just for you, but for all customers.
✓
Expectation: Paul to loop in data team
Paul committed on the call to share your examples with Apollo's product team and follow up with context on what was refreshed. Keep a running log of bad records as you find them - a batch of 10-15 examples is enough to identify a pattern.
Part 4
What Apollo recently refreshed
Paul shared on the call that Apollo's product team recently went through a major data quality initiative - refreshing core fields and certain high-priority categories. Here's a summary of what changed and what's still in progress.
Recently completed - dataset refresh
Done
Core contact data refresh
Significant re-verification pass across the core professional database - current employer, title, and email fields were prioritized.
Done
Technologies used - full revamp
The "technologies used" filter was completely overhauled. Coverage and freshness improved significantly - relevant if your team uses tech stack filters for prospecting.
In progress - Paul to share detail
Coming
LinkedIn identity disambiguation
The specific issue you raised - name-email mismatches from LinkedIn - is an active area of investment. Paul will share the roadmap context and timeline after this call.
Coming
Real-time job change signal coverage
Apollo is investing in faster detection of company moves - reducing the window between when someone changes jobs and when Apollo's database reflects it. This directly addresses your freshness concern.
What you can do right now while roadmap items land
NowEnable Waterfall - pulls from multiple sources, reducing dependence on Apollo's own database for any single record
NowSend Paul 10-15 specific identity mismatch examples - this is the fastest path to a fix
NowRun enrichment in batches - review output for accuracy before opening to the full 13-14k contact database
NowUse email verification step in workflow to confirm deliverability before updating Salesforce - catches bad emails before they land in SFDC