| Goal | How Bitly Profile Data Helps | |------|------------------------------| | Marketing campaign tracking | Identify which channel (email, social, ad) drives most clicks. | | Audience location targeting | See where users are clicking from → tailor content regionally. | | A/B test link variants | Compare two short links → pick higher-performing version. | | Bot/click fraud detection | Spot unnatural click spikes or identical timestamps. |
You run a webinar link. Profile data shows 40% of clicks come from a city you don’t serve. You either expand or refine your ad targeting. bitly profiledat
In the JSON response, each click event contains a profiledat object. Example snippet: | Goal | How Bitly Profile Data Helps
"clicks": [
"ts": "2025-04-01T14:32:11Z",
"profiledat":
"country": "DE",
"region": "BY",
"city": "Munich",
"device": "iPhone14,2",
"os": "iOS 17.4",
"browser": "Safari",
"referer": "https://twitter.com",
"campaign_id": "spring_25"
]
This data is what we call bitly profiledat. It is ingested from the HTTP request headers and IP geolocation when a user clicks the short link. You run a webinar link
Bitly internally uses shorthand prefixes to categorize data objects. Profiledat stands for Profile Data — a structured metadata set attached to a shortened link or a campaign. This data includes:
Unlike Bitly’s public “Overview” stats, profiledat is the raw, unaggregated payload designed for programmatic analysis. It powers the “Locations,” “Traffic Sources,” and “Devices” tabs in Bitly’s dashboard.
If you export profile data to a CSV or JSON, you can perform analysis using Python. Example:
import pandas as pd