Turning predicted abandoned BOPIS orders into same-day delivery

A computer system comprising:

• one or more processors which process program instructions;
• a memory device connected to said one or more processors;
• and
• program instructions residing in said memory device for converting a buy-online pickup-in-store (BOPIS) order for a product by a customer to a delivery order by establishing a threshold deadline for pickup of the product representing a likelihood that the online order is at risk of abandonment using a risk perdition stored in memory of a computer system, monitoring, using a BOPIS system stored in the memory of the computer system a pickup status of the online order to determine that the product has not been picked up by the threshold deadline, the monitoring using an abandonment detection module, determining that delivery of the product after the threshold deadline from a location of the store to an address of the customer is still feasible for a known delivery charge, the determining of the feasibility using the abandonment detection module, wherein the abandonment detection module communicates with the store via a network, the store providing information pertaining to the online order to the abandonment detection module, communicating with a shipper via a network to inquire as to availability of a shipper to pick up the product at the store and deliver it to the address of the customer, as well as the cost for the delivery and time constraints;
• and transmitting a notification to the customer that the product can be delivered with proposed delivery details including the delivery charge, using a delivery options module and a notification module in the memory of the computer system;
• receiving, using a cognitive system having a neural network, a confirmation for delivery from the customer responsive to the notification, the cognitive system being stored in the memory of the computer system,
• collecting, using the cognitive system, additional information from the customer for scheduling a delivery,
• analyzing a current customer profile and historical data using natural language processing;
• analyzing the natural language by generating models for scoring and ranking results for training based on large sets of inputs and outputs as answers to determine a confidence level of an answer of the answers to a query, the query including when the product is picked and packed;
• communicating, using the cognitive system, with a store associate for confirming the product has been picked and packed, and
• scheduling, using the cognitive system, a delivery for the product.

A computer program product comprising:

• a computer readable storage medium;
• and
• program instructions residing in said storage medium for converting a buy-online pickup-in-store (BOPIS) order for a product by a customer to a delivery order by establishing a threshold deadline for pickup of the product representing a likelihood that the online order is at risk of abandonment, using a risk perdition stored in memory of a computer system,
• monitoring, using a BOPIS system stored in the memory of the computer system a pickup status of the online order to determine that the product has not been picked up by the threshold deadline, the monitoring using an abandonment detection module, determining that delivery of the product after the threshold deadline from a location of the store to an address of the customer is still feasible for a known delivery charge, the determining of the feasibility using the abandonment detection module, wherein the abandonment detection module communicates with the store via a network, the store providing information pertaining to the online order to the abandonment detection module, communicating with a shipper via a network to inquire as to availability of a shipper to pick up the product at the store and deliver it to the address of the customer, as well as the cost for the delivery and time constraints;
• transmitting a notification to the customer that the product can be delivered with proposed delivery details including the delivery charge, using a delivery options module and a notification module in the memory of the computer system;
• and receiving, using a cognitive system having a neural network, a confirmation for delivery from the customer responsive to the notification, the cognitive system being stored in the memory of the computer system,
• collecting, using the cognitive system, additional information from the customer for scheduling a delivery,
• analyzing a current customer profile and historical data using natural language processing;
• analyzing the natural language by generating models for scoring and ranking results for training based on large sets of inputs and outputs as answers to determine a confidence level of an answer of the answers to a query, the query including when the product is picked and packed;
• communicating, using the cognitive system, with a store associate for confirming the product has been picked and packed, and
• scheduling, using the cognitive system, a delivery for the product.