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Analyze and predict physical customers’ patterns in Hypermarkets with high precision using the latest advancement in technologies

Analyze and predict physical customers’ patterns in Hypermarkets with high precision using the latest advancement in technologies

The time has come to announce that after more than a year and a half of research and tests we finally have a proof of concept to launch our new product – Sens.ai – with the main focus on  Retail. Sens.ai provides insights and analytics on the customers’ and employees’ behaviour in physical stores with high precision and reasonable pricing. The product is based on the latest technologies and innovation in IoT, Robotics and AI. It is a great example of how math and technology can be combined in order to obtain top class quality solutions with limited  data in the niche of hypermarkets. Background information  The project started in the summer of 2018. We were contacted by a leading retail consultant working with one of the largest retail chains in Bulgaria about a very specific task. The scope was to measure and analyse the customers’ behavior after layout change in one of their hypermarkets and to optimize store’s...
Beginners Guide how to Install Superset (Opensource BI platform) on EC2 AWS instance

Beginners Guide how to Install Superset (Opensource BI platform) on EC2 AWS instance

Why SuperSet? Superset is a data exploration platform designed to be visual, intuitive and interactive, the main objective is to slice, dice and visualize data easily. It is open-source BI platform which can be deployed on every virtual server with no usage costs. Some of the main advantages are: it is maintained by Apache foundation and supported by AirBnB Many visualization and ability to edit the code Support geolocations and uses mapbox Able to cache data for dashboards visualizations Admin panel available with very detailed settings Able to access many SQL and NoSQL databases Easy and friendly user interface According to GitHub repo, Superset is currently being used by Airbnb, Twitter, GfK Data Lab, Yahoo!, Udemy and many others. We at ShopUp decided to give a try of that great platform and noticed that as products driven by the community, sometimes there is missing documentation. We have met some difficulties in setting up the platform on EC2 instance, therefore...
Data preparation steps for Data Science or AI project in eCommerce or Retail

Data preparation steps for Data Science or AI project in eCommerce or Retail

Background Do you have tones of data and probably you want to take advantage of Data science and AI which are slowly but surely coming to the retail and ecommerce sector and these businesses are constantly generating huge amount of data. Before going into any modeling or data analysis each observer need to prepare the data in a way that machines can work with it. We decided to write an article about main approaches for data preparation of categorical variables called data encoding mainly observed in survey data. What data preparation means? Computers like variable as numbers therefore all textual values need to be presented in their numerical equivalent in order to be used for machine learning algorithms and deep learning neural networks. Simply they don’t like text. Here is an example. We have variables like: “Brand of car” with values “Mercedes”,”BMW” or others “Retailers Name” with values like “Kaufland”, “Metro” or “Mr.Bricolage” . “Sex” – “Male”, “Female” and many others...
Recommender systems in Retail 2019: Overview and Use case

Recommender systems in Retail 2019: Overview and Use case

 A Retail Case study about implementing recommender systems developed during the Summer School of Research Methods In summer 2019, some of the ShopUp team took part in the Summer School of Research Methods. The 7-day event was organized by several members of Data Science Society and academia representative from Sofia University, UNWE and Technical University Sofia. There were more than 10 lecturers and about 30 participants from different companies, organizations and universities. Among them were experts, researchers, PhD candidates and masters students. Each day the program started with presentations or workshops followed by allocated time for a Capstone project (a project which aims to capture what we’ve learnt). The workshops combined practice and theory in the area of maths, statistics, neural network, reinforcement learning and etc. The Capstone project was mandatory for each participant and there were three different cases to select from.    We at ShopUp decided to open-source this project work and share it with the community. The code we created is...
ShopUp Now Combines Data from Door Counters, Wi-Fi Routers, and Mobile Apps into an All-in-one Customer Analytics Platform for Malls and Shopping Centers

ShopUp Now Combines Data from Door Counters, Wi-Fi Routers, and Mobile Apps into an All-in-one Customer Analytics Platform for Malls and Shopping Centers

During the last 3 months ShopUp Customer Behavior Analytics platform has focused to malls and shopping centers, finalized the product, start on-boarding new customers  and generate constant revenue. Below you can read more about our progress. Business development: The ShopUp platform now offers an all-in-one solution for Malls and Shopping centers. We’ve created a complete tailored solution where we combined data from Door Counters, Wi-Fi Routers, Mobile Apps and other hubs into an all inclusive Customer Behaviour Analytics Platform. Now Mall owners, marketers, and analysts can see the data in one place and make decisions based on 360 degree view. 12 Malls have been on-boarded to the new platform and 20 more are in the pipeline. We started generating recurring revenue stream. We were part of the biggest Retail Conference: NRF in New York —  where we met great people and learned a lot about the newest trends in the industry. Product Updates Router Integration: ShopUp has been integrated and...
ShopUp is Now Scalable

ShopUp is Now Scalable

In the last 2 months we’ve done a lot of progress with our product and business development. We’d like to share with you where this journey took us: Product Updates Firmware Upgrade We improved the performance of the ShopUp sensors by upgrading the firmware. The amount of  data we’re now gathering have increased by more than 5 times. Also, we found a way to install the firmware directly on customers’ routers. We support more than 1,000 devices. That means we are now able to launch ShopUp remotely, without the need of physical installation on site within less than 24 hours. That is a huge advantage in terms of scalability. HeatMap We introduced a stable 2.0 version of the HeatMap where data visualization has been much improved compared to the previous version. The HeatMap is now based on an improved  mathematical model. Prediction Models Prediction models can now be generated based on ShopUp sensors and data integration with door counters,...