Offers are important sales drivers in the fast-food industry. Being able to segment customers based on their offer preferences and assigning the best offer sets to each segment is critical for a customer-centric recommendation system to enhance user shopping experience.
Directly applying recommendation models to this use case would cause the “black box” problem where marketers can have difficulty understanding the model’s decision logic. It’s also challenging for marketers to apply different offer strategies to different customers. On the other hand, traditional customer segmentation approaches won’t generate offer recommendations and therefore they still rely on marketers to manually assign offers to each user segment.
At Burger King, we have developed our own offer recommendation system that leverages pre-trained BERT and Inception models on Apache Spark to extract feature representations directly from offer descriptions and images, followed by Spark MLlib to form the user segmentations. Such a system can discover customer segmentations based on their offer preference and can also directly generate offer recommendations from these resulting segments.
Furthermore, it is robust enough to handle the constantly changing offer pool and newly joined customers without the need to keep retraining the model from time to time. In this session, we would discuss our offer recommendation system in detail.
Kai Huang is a software engineer at Intel. His work mainly focuses on developing and supporting deep learning frameworks on Apache Spark. He has successfully helped many enterprise customers work o...
Luyang Wang is a Sr. Manager on the Burger King data science team at Restaurant Brands International, where he works on developing large scale recommendation systems and machine learning services. Pre...