Honestly depends on your data stack and what you're trying to enrich. For local SEO, I'm usually pulling NAP data-names, addresses, phone numbers-for citation building or competitor analysis. Hunter.io is handy when I already know the domain, but it's limited once you need more than email. Lusha and Apollo give you the phone number side too, but the accuracy varies wildly by region. For UK businesses, Lusha's mobile numbers are decent; Apollo tends to flag a lot of landlines as "direct dial" when they're actually reception desks.
One thing that caught me early on: enrichment tools are only as good as your matching logic. If you're enriching a spreadsheet of company names, you need to standardise the domains first. Use a lookup script like this:
import re
def clean_domain(company_name):
# basic sanitise
name = re.sub(r'[^a-zA-Z0-9 ]', '', company_name).lower()
return f"{name.replace(' ', '')}.com"
Saves you from getting back garbage results because the tool can't parse 'Bob's Plumbing & Heating Ltd'.
What's your actual use case? B2B outbound for local services, or are you scraping competitor citation gaps? That changes which fields you need. Calling raw numbers without context tends to annoy people, so if you do have them, pair with a reason to call that's tied to the location, not just a generic pitch.
