DigitalFingerprint
Persistent visitor identity-fraud prevention & intelligence
17 followers
Persistent visitor identity-fraud prevention & intelligence
17 followers
Cookieless visitor identification and browser fingerprinting that identify returning visitors across VPNs, incognito, and cleared cookies. Drop-in JavaScript SDK with server-side smart signals.
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Launch Team
Viktor.comAn AI coworker that actually does the work
Promoted
A dashboard view that shows the accuracy rate across different scenarios (VPN, incognito, cleared cookies) would be really helpful. Right now it feels like a black box — I can see it works, but having per-environment confidence scores side by side would make it way easier to decide which signals to trust and which ones to weight differently in my own scoring logic.
@lapak93209 That’s fair feedback, and it’s exactly the kind of thing we want to hear!
Today you can see per-event confidence and the underlying signals (VPN, incognito, tampering, etc.) in the console on each identify event, but you’re right that there isn’t a single view that compares how those scores behave across scenarios like VPN vs incognito vs cleared cookies. We do publish production benchmark results publicly, but that’s not the same as seeing it broken down for your own traffic in one place.
A side-by-side scenario view would make it much easier to decide which signals to trust and how to weight them in your own scoring. We’re looking at how to surface that in the dashboard. If you’re open to it, I’d love to hear which scenarios matter most for your use case!
The SDK took maybe ten minutes to drop in and I was honestly surprised how many of my "private" test sessions still got matched the next day. Wish the dashboard had a bit more detail on which signals actually triggered, but the core accuracy feels solid.
@ezel656461 Thanks for this!
Glad the SDK was quick to drop in and that the matching held up across your private test sessions the next day. That’s what we care about most.
Totally fair on the dashboard. You can see smart-signal flags and risk on individual events, but it’s not always clear which signals drove a specific match or score. We’re looking at making that more obvious.
If you have an example where you expected a miss and got a match (or the other way around), I’d love to see it. That kind of feedback helps a lot.
Appreciate you trying it!
Honestly this looks really promising for handling the VPN and incognito cases since most tools struggle there. One thing that would honestly make it way more useful for my team is a built-in dashboard or at least some kind of visual reporting so we can actually see match rates and confidence scores over time without having to build our own analytics layer on top of the SDK.
@dnd1129690 Appreciate you saying that!
VPN and incognito are exactly where a lot of tools fall apart, so glad that stood out. On the reporting side, the dashboard already has some of this: event history, confidence, risk, and traffic breakdowns. What it doesn’t do as well yet is trend those match rates and confidence scores over time in a clear visual way. That’s a fair ask, and it’s something we’re thinking about so teams don’t have to build their own analytics layer on top of the SDK.
If you want, I can show you what’s already in the console today and take notes on what your team would want to see first.