Cognitive Prediction System for BOPIS Order Conversion
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Solution Overview
Problem
A significant percentage of buy-online pickup-in-store (BOPIS) orders are abandoned, resulting in lost sales for retailers, as customers often fail to pick up their orders within a reasonable timeframe, leading to refunds and inventory issues.
Innovation Solution
A method that uses a cognitive prediction system to establish a threshold deadline for order pickup, monitors the order status, and converts unpicked BOPIS orders to delivery orders by analyzing customer profiles, historical data, and real-time data to determine the likelihood of abandonment, then notifies customers via optimal communication channels about same-day delivery options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If BOPIS orders are offered to customers, then inventory optimization and fewer returns are achieved, but a significant percentage of orders are abandoned resulting in lost sales
Solution Approach 1:
The system performs preliminary actions by predicting which BOPIS orders are at risk of abandonment before they actually occur. Machine learning models analyze customer behavior patterns, order history, and real-time data to identify at-risk orders early, allowing the system to proactively convert them to delivery orders before the customer abandons them completely.
Solution Approach 2:
The system implements continuous feedback loops by monitoring order status, customer interactions, and pickup behavior in real-time. This feedback is fed into machine learning models that continuously refine their predictions about order abandonment risk, enabling dynamic adjustments to conversion strategies based on actual customer behavior patterns.
2Measurement precision
If a threshold deadline is established for pickup, then order abandonment can be predicted, but customers may feel pressured or confused by automated conversions to delivery
Solution Approach 1:
The system dynamically adjusts the threshold deadline and conversion timing based on real-time customer behavior and order status. Rather than using a fixed deadline, the system continuously monitors pickup attempts, customer communications, and order modifications to determine the optimal moment for conversion, making the process adaptive to individual customer circumstances.
Solution Approach 2:
The system changes key parameters such as the threshold deadline timing, notification frequency, and conversion offers based on analyzed customer patterns. Machine learning models adjust these parameters dynamically for different customer segments and order types, optimizing both prediction accuracy and customer experience without applying uniform pressure to all customers.
3Productivity
If BOPIS orders are converted to delivery orders, then sales are saved, but delivery charges and logistics complexity increase
Solution Approach 1:
The system extracts and isolates only the subset of BOPIS orders that are most likely to be abandoned and therefore most suitable for conversion to delivery. By using machine learning to identify at-risk orders based on specific customer behavior patterns and order characteristics, the system selectively converts only those orders that would result in net positive value, avoiding unnecessary conversion of orders that would be profitable to fulfill in-store.
Solution Approach 2:
The system changes operational parameters such as delivery timing, charge amounts, and fulfillment methods based on real-time analysis of order risk and customer preferences. This allows the system to optimize the balance between conversion benefits and logistics costs by adjusting parameters dynamically rather than applying uniform policies to all orders.
Data Source
AI summary
A buy-online pickup-in-store (BOPIS) order at risk of abandonment is saved by converting the order to same-day delivery. A risk deadline is established using a combination of cognitive prediction, a customer-specific time frame, order constraints, and real-time data. The cognitive prediction uses a customer profile having historical data pertaining to previous BOPIS orders from this customer. If the product is not picked up by the risk deadline, a check is made to see if same-day delivery is feasible. If so, a notification is transmitted to the customer with proposed delivery details. The notification can be sent at an optimal time based on previous notifications sent to the customer and time constraints related to the order, and using an optimal communication channel based on previous engagement rates with the customer over different communication channels. Once confirmation is received from the customer, the product is shipped, saving the sale.