Combining Apollo, ZoomInfo, Cognism and Lusha exports

Every B2B data provider has gaps, and the gaps are not the same ones. Coverage varies by geography, by company size, and most of all by vertical. A provider that is excellent on North American software will be thin on European manufacturing, and the one that covers manufacturing will be missing the direct dials.

So teams that care about coverage buy more than one, pull the same target list from each, and take the best of what comes back. That is the right instinct. It is also the point at which the work starts, because you now have the same people described three different ways.

Every provider offers a native CRM integration, and on paper it removes the problem. Push straight from the provider into HubSpot or Salesforce, no files involved.

In practice it moves the mess rather than removing it. The integration writes records in the provider's shape, using the provider's field names and its idea of what a phone number looks like, and it writes them without anyone seeing them first. There is no step where you look at what is about to land.

With two providers connected it gets worse rather than twice as good. Both are writing into the same object, neither knows what the other just created, and neither is deduplicating against the other. You end up doing the consolidation work anyway, except now you are doing it inside the CRM on live records that already have owners and activities attached, which is the most expensive place to do it.

Do the consolidation before anything reaches the CRM, so the CRM only ever sees one reviewed list.

This is the part people underestimate. When you target broadly, providers return different companies and the overlap is modest. When you target a vertical, they are all drawing from the same finite pool, so they return substantially the same companies and often the same people at them.

The duplicates are also not tidy. The same person arrives as a work address from one provider and a generic company address from another. The same company appears with and without its legal suffix. Job titles are written three ways. Exact matching on email will not catch most of it, because the addresses genuinely differ.

So match in two passes. First on email, which removes the easy ones. Then on name plus company with similarity matching, which catches the pairs that used different addresses or spelled the company differently. That second pass is where most of the real duplicates in a vertical list live.

Once duplicates are collapsed you still have to choose between competing values, and doing it by hand across thousands of rows is not realistic.

The useful approach is a priority order per field rather than a preference per provider. Phone numbers are the clearest example: take the direct dial if there is one, then the mobile, then the office number, then the company switchboard, and stop at the first one that is actually present. That produces a better single phone column than any one provider gave you, which is the whole reason for buying more than one.

The same logic applies to job titles, company names and locations. Decide the order once, apply it to every row, and keep the result in the field your CRM actually has.

Provider data is a snapshot of when it was collected. People change jobs, companies fold, and mailboxes get retired, and none of that updates the file sitting on your disk.

The other reason is that not all addresses behave the same on send. A verified business mailbox, a role address like info@ or sales@, a free webmail account, a disposable domain and a catch-all domain that accepts everything and delivers nothing are all technically valid and all behave differently. Importing them as though they are equivalent is what produces a bounce rate that damages your sending reputation, and reputation is much harder to repair than a list.

So the useful output is not valid or invalid. It is knowing which addresses are safe to send to, which are risky, and which are catch-alls where nobody can tell you for certain.

Merge Files takes the exports and matches columns by name, up to 10 files on Pro, 15 on Team and 25 on Agency, with warnings wherever a file is missing a column the others have.

Deduplicate handles both passes: exact matching on email, then similarity matching on name plus company for the pairs that exact matching misses, with a threshold you set and the option to keep the first or last occurrence. On Pro you can export the removed rows as a separate file, which is worth doing the first time so you can check what it took out.

The CRM Formatter handles the priority order when mapping. A field can draw from several source columns in sequence and take the first one that is not empty, which is exactly the phone chain described above, and it standardizes the winning value on the way through.

The Email Verifier returns a status per address rather than a yes or no: valid, invalid, risky, catch-all, disposable, role or unknown, so you can decide what to import and what to hold back. It is on Pro and up, as is Merge Files.

What Operelio does not do is enrich. It will not find a missing phone number or fill a blank company for you. It works with what your providers gave you, which is the point: the providers do the finding, this does the deciding.

Every provider has a ready-made mapping into each supported CRM, so the column mapping is done for you, including which columns have no destination and which required CRM fields the export cannot fill. Apollo, ZoomInfo, Cognism and both Lusha exports all map into HubSpot, Salesforce, Pipedrive, Zoho, Monday, Freshsales, Close and Copper.