Where does FullContact get its data?
We use our own Person API functionality for enriching information of your contacts.We combine information from hundreds of public websites, social networks, APIs, trusted partners and users of our service. You can learn about our Developer APIs here.
How do the FullContact algorithms connect the dots on social data?
FullContact provides identity resolution for all of the disparate pieces of public contact information out there on the web. We do this by aggregating billions of contact records, all with numerous attributes, including quality, freshness. and frequency. Our patent pending algorithms process all of this data and automatically produce clean, accurate full contact records. As a final step, we then check each data element to make sure that it’s publicly available before providing it to our customers.
For example, imagine that five people are sitting around a table and each pulled out a contact record for our CEO, Bart Lorang. One person might have his Twitter handle and email address; a second might have just an email address and an image; and the others might have various other pieces of data. If all of that information were mashed together, as humans, the group could put together a "FullContact" record for Bart with a high degree of accuracy. This is basically how FullContact works. We take a variety of data points and utilize algorithms to logically build clean, accurate FullContact records.
Where can I find a full list of the social network types that FullContact can return?
A full list can be found here.
Can I re-license FullContact’s data?
No. We do not allow relicensing or reselling of our data. For existing customers, please see our Terms of Service for more details on relicensing and reselling.
How accurate is FullContact's data?
Our accuracy rates are over 90% and match/fill rates are usually between 20-60%. Match rates refer to whether we can return at least one social URL for a given email or other query parameter. Our fill rates are often dependent on the nature of the queries we are given. For example, a list of old or out-of-date email addresses would typically result in a lower fill-rate than a list of current, accurate email addresses.
Geographically, we see matches that fall within the following percentages:
United States: 40-60%
United Kingdom: 20-40%
Australia / South Africa: 20-40%
Other International Countries: 10-30%
To note: When querying MD5 Email Addresses, we typically see match results of 10-40%