As a leading supplier in the institutional food service industry, Land O’Lakes provides various processed cheese products via annual competitive contracts. Each bid requires a custom price, set individually per contract, trading off win probabilities (low prices) and profit margins (high prices).
To provide accurate estimates of win probabilities, the Land O’Lakes Data Science team set out to build a classification model, predicting likelihood of winning a bid at a given price, but training such a model is challenging due to limited data and overfitting. In this presentation we will share two innovative techniques in data augmentation and de-overfitting, which enable a classification model reliable enough to optimize prices and maximize expected profit.
Ian is Manager of Data Science, leading Land O’Lakes’ advanced analytics and machine learning efforts. Prior to Land O’Lakes, Ian has held analytic positions at Centriam, G&K Services, and SUPERVALU.
