Choose this use-case if you wish to develop a model that identifies sales leads most likely to convert into paying customers. Predictive lead scoring may be useful in prioritizing sales efforts, personalizing marketing campaigns, and forecasting sales. Given a dataset of lead information (e.g. age, job title, industry, website visits, email opens, downloads, etc.) you can generate a model that assigns each lead a score representing the likelihood of that person converting into a paying customer.
Dataset and Feature Group RequirementsThis section specifies the Datasets / Feature Groups requirements to successfully train a Predictive Lead Scoring 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 Predictive Lead Scoring 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.