Predicting causes of churn and allocating fulfillment resources based on prediction

A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps comprising:

• identifying churn of a customer of a computer system, wherein churn of the customer indicates a decrease in interaction by the customer with the computer system;
• identifying an event to which the churn of the customer is attributed, wherein the identifying is based on characteristics of the customer and attributes describing fulfillment of one or more orders received from the customer;
• generating a picker score for each of a set of pickers, the picker score for a picker comprising a probability of the customer performing a specific action within a threshold time period of the picker fulfilling a subsequent order from the customer, the picker score generated by applying a picker scoring model to the characteristics of the customer, characteristics of the picker, the identified event, and one or more prior orders from the customer, the picker scoring model trained by:
• obtaining a score training dataset including a plurality of score training examples, each score training example including characteristics of a customer included in the score training example, characteristics of a picker included in the score training example, an event to which churn of the customer included in the score training example was attributed, and one or more prior orders from the customer included in the score training example, each score training example having a label indicating whether the customer in the score training example performed the specific action within the threshold time period after fulfillment of an order by the picker in the score training example;
• applying the picker scoring model to each training example of the training dataset to generate a predicted probability of the customer in the score training example performing the specific action within the threshold time period after fulfillment of an order by the picker in the score training example;
• scoring the picker scoring model using a loss function and the label of the score training example;
• and
• updating one or more parameters of the picker scoring model by backpropagation based on the scoring until one or more criteria are satisfied;
• and
• selecting, based on the picker scores, a picker of the set of pickers to fulfill the subsequent order from the customer.

A computing system comprising:

• one or more processors;
• and
• a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by the processor, cause the processor to perform steps comprising:
• identifying churn of a customer of the computer system, wherein churn of the customer indicates a decrease in interaction by the customer with the computer system;
• identifying an event to which the churn of the customer is attributed, wherein the identifying is based on characteristics of the customer and attributes describing fulfillment of one or more orders received from the customer;
• generating a picker score for each of a set of pickers, the picker score for a picker comprising a probability of the customer performing a specific action within a threshold time period of the picker fulfilling a subsequent order from the customer, the picker score generated by applying a picker scoring model to the characteristics of the customer, characteristics of the picker, the identified event, and one or more prior orders from the customer, the picker scoring model trained by:
• obtaining a score training dataset including a plurality of score training examples, each score training example including characteristics of a customer included in the score training example, characteristics of a picker included in the score training example, an event to which churn of the customer included in the score training example was attributed, and one or more prior orders from the customer included in the score training example, each score training example having a label indicating whether the customer in the score training example performed the specific action within the threshold time period after fulfillment of an order by the picker in the score training example;
• applying the picker scoring model to each training example of the training dataset to generate a predicted probability of the customer in the score training example performing the specific action within the threshold time period after fulfillment of an order by the picker in the score training example;
• scoring the picker scoring model using a loss function and the label of the score training example;
• and
• updating one or more parameters of the picker scoring model by backpropagation based on the scoring until one or more criteria are satisfied;
• and
• selecting, based on the picker scores, a picker of the set of pickers to fulfill the subsequent order from the customer.