Choose this use-case if you wish to develop a model that analyzes customer data to predict which promotions each customer will respond most positively to. Given datasets containing user data (demographics, purchase history, browsing history), item data (category, price, description), interaction data (items viewed, added to cart, and purchased), and promotion data (type, discount amount, items discounted), you can generate a model that personalizes customer promotions, increasing customer engagement, satisfaction, and sales.
Dataset and Feature Group RequirementsThis section specifies the Datasets / Feature Groups requirements to successfully train a Personalized Promotions model. Feature requirements include recommendations on additional datasets that might enhance model performance.
Training Models - Training Options and MetricsThis section describes all the available model training options that can be used to create a Personalized Promotions model. You can utilize the metric explanations to better understand how they measure the performance of the model you trained.
Evaluating PredictionsThis section contains a quick model evaluation guide that helps you understand how well your model is performing.
Prediction APIThis section discusses the prediction API method so that you could properly generate predictions from the model you deployed.