xNomad Marketplace for Short term retail

Important results from the project

The purpose of this project is to build a recommendation engine based on a Machine Learning Model that uses data gathered from telecommunications and in store sensors to understand retail customer insights. This recommendation engine will help find your ideal short term space, through using an analysis of footfall and targeted demographics in order to aid decision making. A new website and model will be built to show, area footfall, customer demographics and age on each space listing. It also included the ability to help brands select move in ready pop up spaces with furniture was included

Expected long term effects

The project result was a new website which displays average footfall per area, age and gender, based on the AI/ML model data. The data from Telia was used in combination with in-store data thats helps brands choose their location based on the telecoms, in-store and demographic data. This feature was added to the website and each vacant space listing. A new UX/UI design and alternative space recommendation was added and allowed brands to find the best location faster and more accurately.

Approach and implementation

The project began with gathering telecommunication data from telia and in-store data from popup stores that took place across Sweden. Data scientists and developers then created a database from where the AI/ML use data to make a space recommendation engine based on the data. Graphic and UX/UI designers then designed a new UI/UX to represent the data in a graphical from and add it to the new website. The recommended space feature was added to each listing and the main website. The entire process of finding and booking a space on the website was redesigned to incorporate the data.

External links

Find a short term retail store through data on xNomad.co https://xnomad-staging.herokuapp.com/