Every retailer knows that product position within their store has a direct influence on revenue. But how do you apply this knowledge? Would you win more by selling a batch of cheaper goods or a few high priced items? Will the product placed on the same position in two different outlets get equal attention from the consumers? DB Best leveraged neural networks and machine learning algorithms to develop a solution that allows for increasing sales and doubling the retailer’s revenue […]
Talks on Managing Data and Applications Anywhere
For retail businesses with distributed point of sales (POS) systems, you can often run into the problem where stores fail to synchronize their data with the centralized data warehouse. If you are not aware of this situation, you can end up taking faulty business decisions when making seasonal adjustments for your supply chain. The top-selling product for the last week at a given store ends up not getting resupplied. Even worse is when last week’s hot seller, no longer is.
In this blog post, we’ll discuss how machine learning anomaly detection for retail POS systems can identify sites that fail to consistently report data into a centralized data warehouse.
In this blog post, we describe how we applied our knowledge in machine learning and computer vision to prevent one of the most common safety breaches among builders. The DB Best team came up with a solution utilizing the YOLO object detection algorithm for a neural network to detect those not wearing hard hats on a construction site. Our algorithms reached 98% accuracy in identifying troublemakers analyzing video materials in real-time!
This case study features our experience of measuring data science algorithms to optimize the ETL processing for data cleansing.