Any demographic research artist worth their salt knows and loves the awesome power of online directories and lists. Just the thought of categorized information and aggregated data gives me the biggest kid in a candy store, ear-to-ear grin. That’s just what Factual and its open data platform and community serves up.
Founded by Gil Elbaz, co-founder of Applied Semantics (which was sold to Google) Factual’s mission is “to maximize data accuracy, transparency, and availability.” I first came across Factual when digging away at a good ol’ fashioned down and dirty demographic research project. This business listing directory was unlike any other directory I had come in contact with. Why?
- It was one of many very large pre-aggregated data sets
- Each data set could be sorted by a number of different filters
- Much of the data could be output to CSV
Thus began my
obsession relationship with Factual.
What Data is Available?
The amount of data Factual houses is near overwhelming, and data wizard community members just keep piling it in. Factual contains data sets in quite a few verticals, from education, entertainment, health and nutrition to food, finance, government, and local business in 44 countries.
Data can be filtered and sorted by attributes the admin has assigned. Within the premium US POI (point of intersection) Business Listing dataset you can filter by:
- Factual ID
- PO Box
The refined datasets you create from Factual Premium datasets are not a simple CSV output. Unless you’re accessing the Factual API, you must request a download and describe how you’re leveraging that juicy data through application or service.
How to Find & Filter Data in Factual
Search is keyword or topic based. Test some queries to understand what datasets have previously been created around your desired topic. You might be surprised by the amount of data that already exists.
After having checking out pre-aggregated datasets, be sure to spend some time in the US POI and Local Business Listing Premium Datasets to glean what’s available in your desired vertical.
Search within the dataset. Here’s where things get real juicy. Business and company data not only paint an accurate market segment, but can help define highly pinpointed LinkedIn and Facebook ads by targeting users place of work.
Here the data is sorted for only rows with website URLs.
Use the “Filter by Column” dropdown menu to sort data by the following logic.
Exclude data that doesn’t contain data in select columns.
And yes, you can isolate and mine email addresses. Back spammers, back!
Use multiple filters for sorting.
How Factual Advises Contextual Targeting
Identifying user interests is a fundamental piece to the process of targeting ads to Facebook users. Below are a few interesting datasets that are unique for discovering peripheral and defining user interests.
Factual has some heavy implications for demographic and B2B market research. The ability to use this data to target businesses contextually in both Facebook and LinkedIn alone is worth having a look. Next time you need identifying lists upon lists of organizations, businesses, associations, schools, etc., have a look at Factual. You’ll be glad you did.
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