The DATA MINING CUP (DMC for short) 2018 ended on 17 May. The focus of the task was on optimizing prices for sporting goods. About 200 teams from over 40 countries took part. prudsys AG will award prizes to the winners during the personalization & pricing summit on 26/27 June 2018 in Berlin.
The DMC 2018 came to a close on 17 May with the submission of the solutions. Once again, the renowned competition for intelligent data analytics enjoyed great popularity. A total of 193 teams from 148 institutions in 47 countries worked on a tricky pricing task. This year’s goal was to come up with a sales prediction for the sale of sporting goods. To do this, historical transaction data from a real German online sporting goods shop was made available to the students. The online shop adapts its prices dynamically. Participants had a total of six weeks to submit a predictive model.
The teams with the best sales prediction can look forward to attractive cash prizes and participation in the personalization & pricing summit 2018 in Berlin. The leading conference for the earnings-boosting application of artificial intelligence in omnichannel retail will take place on 26 and 27 June at the nhow Hotel in Berlin with the tagline “Reinforcing Retail”. Over 200 experts will discuss news and trends for successful personalization, automated price optimization and AI-optimized processes. Among other things, visitors can look forward to keynote speakers Prof. Dr. Heinemann and Joachim Graf as well as best practice presentations from OBI (requested), 11teamsports, 1-2-3.tv, GK Software and BikerBoarder, to name a few.
prudsys will present awards to the ten best DMC teams on the evening of the first day of the conference. For the 19th time, prudsys AG, the leading supplier of agile AI technologies for omnichannel retail, called on students from around the world to test their know-how on a practical problem. The DMC tasks over the last few years have covered a number of retail topics. Examples: predicting returns, predicting shopping cart value, predicting coupon redemption and developing agents that predict the probability of an order.