Fingerprintty

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  our architecture

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  our team

Inspiration

We have been asked multiple times by different businesses to learn more about their website visitors. Even some of them have registered before, vast amount of users are "unknown" to the system and can't be personalized properly. We aim to fix it and deliver personal offerings even for incognito users!

See more about data, which is collected for your device at https://www.deviceinfo.me/ or https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers#client_hints

What it does

The system is based on MACH components and analyzes client request headers, followed by rule-based segmentation of visitor. We have created several sample segments (like potentially interested in iPhone13 or affordable Android device, can be easily managed by business user) and delivered basic personalization in Algolia

How we built it

We have used Algolia, commercetools and contentful as basis, and added some custom-baked microservices. You can see our high-level design on a diagram below

Challenges we ran into

Basically, it's all about time. All out team members had 100% project workload, so we usually met late evenings for brainstorming sessions or used weekends for coding.

What we liked the most

We have enjoyed the ease of connecting MACH systems together, clear documentation and. Also, it's now a pleasure to realize how ambitious results can be delivered in very limited timeframe.

What's next for fingerprinting personalization

The solution is not limited to particular tools or scenarios. We'd like to demo it to business and use ideas and knowledge in real project

Built With

• algolia
• contentful