We have studied how well 35 email finders perform. Among them, notorious solutions such as Apollo, Lusha, Hunter, etc.
To do so, we have created a test file of 300 prospects, with the following input entries for each prospect: full name and company name.
The file has been processed by all the email finders. Finally, we measured the email discovery rate of each competitor.
Disclaimer: this study has been conducted by Icypeas, a new Email finder. To establish our good faith and prove the solidity of this study, we have fully documented our work. Everything was recorded on video. To know more about this, see the "Open Data" section.
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We created a sample of 300 prospects, randomly, using Linkedin. To get more information on the sample, see this dedicated section. This gave us a list of 300 individuals, identified with their first name, last name and company name. We are well aware that some sales teams are able to collect the company’s domains but we have decided to perform this benchmark on the most difficult input data, i.e. company names instead of domains.
We have uploaded this 300-prospect list on the platforms we wanted to assess. Once the file was processed, we downloaded the enriched list. We calculated the “gross email discovery rate”, i.e. the number of found email addresses. We say it is a “gross” rate because at this stage it does not take into account the number of hard bounces. Some addresses given as valid will turn out to be invalid after the deliverability test in Step 3.
To get the number of hard bounces, we sent an actual message to each email address, and we waited up to 3 days for an error notification. In February 2023, we sent 1333 mails from 27 mailboxes. This gave us a "bounce rate" for each email finder. Good email finders must have a high gross email discovery rate with a small bounce rate. To merge these 2 rates into a final one, we consider the “net email discovery rate” as the ultimate performance indicator. The “net email discovery rate” is the number of email addresses given as valid by an Email Finder minus the number of hard bounces divided by the total number of prospects (in this experiment: 300).
To create our 300-prospect list, we choose a keyword, randomly. In 2023, it was: "sales". In 2022, it was "communication". We typed this keyword in the Linkedin Sales Navigator search bar. Then, we added both a "Geography" filter and a "Company headcount" filter, to make sure that our sample includes people from different areas (USA, France, Germany, UAE) and from companies of different sizes (1-10, 201-500, +10K employees). Finally, we scrapped these profiles
Here is how our prospects are distributed along these constraints:
Also we observe that 215 prospects have more than 10 years of experience.
85 have less than 10 years of experience.
This distribution is not controlled.
Here is the seniority levels of our sample: