Methods and apparatuses for evaluating substitutions made during order fulfillment processes

1. A system comprising at least one computing device coupled to one or more data sources comprising at least one server,

the at least one computing device configured to:

receive order data from the one or more data sources indicating an order placed by a customer on an e-commerce platform;

route the order into a test group or a control group when the order data indicates that the order will be filled by a store participating in a comparison test;

retrieve a first version of a trained machine learning customer understanding model and a control version of the trained machine learning customer understanding model from the one or more data sources;

determine recommended test substitute items based on overall test substitution scores of possible substitute items using the first version of the trained machine learning customer understanding model, the recommended test substitute items intended to replace items ordered by the customer that are unavailable;

determine recommended control substitute items based on overall control substitution scores of possible substitute items using the control version of the trained machine learning customer understanding model, the recommended control substitute items intended to replace items ordered by the customer that are unavailable;

send the recommended test substitute items and the recommended control substitute items to a store order fulfillment system associated with the store participating in the comparison test, the store order fulfillment system comprising at least one store workstation; and

determine one or more performance metrics of the first version of the trained machine learning customer understanding model.

2. The system of claim 1 , wherein the order data indicates that the customer desires to pick-up the order at a physical retail store.

3. The system of claim 1 , wherein the order data comprises an order identification number, store data characterizing a store location at which the customer desires to pick-up the order and item data characterizing ordered items.

4. The system of claim 1 , wherein the overall test substitution score is calculated by the at least one computing device by combining a relevance score and a preference score for each possible substitute item.

5. The system of claim 4 , wherein the relevance score indicates a similarity between a possible substitute item and an anchor item and the preference score indicates a preference for one or more attributes of a possible substitute item.

6. The system of claim 1 , wherein the computing device is further configured to receive acceptance data that indicates whether the customer accepted or rejected the at least one recommended substitute item.

7. The system of claim 1 , the computing device further configured to receive test configuration data indicating one or more test conditions characterizing the comparison test, wherein the order is routed into the test group when the order satisfies the one or more test conditions.

8. A method by at least one computing device coupled to one or more data sources, wherein the one or more data sources comprises at least one server, the method comprising:

receiving, by the at least one computing device from one of the one or more data sources, order data indicating an order placed by a customer on an e-commerce platform;

routing, by the at least one computing device, the order into a test group or a control group when the order data indicates that the order will be filled by a store participating in a comparison test;

retrieving, by the at least one computing device, a first version of a trained machine learning customer understanding model and a control version of the trained machine learning customer understanding model from the one or more data sources;

determining, by the at least one computing device, recommended test substitute items based on overall test substitution scores of possible substitute items using the first version of the trained machine learning customer understanding model, the recommended test substitute items intended to replace items ordered by the customer that are unavailable;

determining, by the at least one computing device, recommended control substitute items based on overall control substitution scores of possible substitute items using the control version of the trained machine learning customer understanding model, the recommended control substitute items intended to replace items ordered by the customer that are unavailable;

sending the recommended test substitute items and the recommended control substitute items to a store order fulfillment system associated with the store participating in the comparison test, the store order fulfillment system comprising at least one store workstation; and

determining, by the computing device, one or more performance metrics of the first version of the trained machine learning customer understanding model.

9. The method of claim 8 , wherein the order data indicates that the customer desires to pick-up the order at a physical retail store.

10. The method of claim 8 , wherein the order data comprises an order identification number, store data characterizing a store location at which the customer desires to pick-up the order and item data characterizing ordered items.

11. The method of claim 8 , wherein the overall test substitution score is calculated by the at least one computing device by combining a relevance score and a preference score for each possible substitute item.

12. The method of claim 11 , wherein the relevance score indicates a similarity between a possible substitute item and an anchor item and the preference score indicates a preference for one or more attributes of a possible substitute item.

13. The method of claim 8 , further comprising receiving acceptance data that indicates whether the customer accepted or rejected the at least one recommended substitute item.

14. The method of claim 8 , further comprising receiving test configuration data indicating one or more test conditions characterizing the comparison test, wherein the order is routed into the test group when the order satisfies the one or more test conditions.

15. A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause a device to perform operations comprising:

receiving order data from one or more data sources indicating an order placed by a customer on an e-commerce platform, the one or more data sources comprising at least one server;

routing the order into a test group or a control group when the order data indicates that the order will be filled by a store participating in a comparison test;

retrieving a first version of a trained machine learning customer understanding model and a control version of the trained machine learning customer understanding model from the one or more data sources;

determining recommended test substitute items based on overall test substitution scores of possible substitute items using the first version of the trained machine learning customer understanding model, the recommended test substitute items intended to replace items ordered by the customer that are unavailable;

determining recommended control substitute items based on overall control substitution scores of possible substitute items using the control version of the trained machine learning customer understanding model, the recommended control substitute items intended to replace items ordered by the customer that are unavailable;

sending the recommended test substitute items and the recommended control substitute items to a store order fulfillment system associated with the store participating in the comparison test, the store order fulfillment system comprising at least one store workstation; and

determining one or more performance metrics of the first version of the trained machine learning customer understanding model.

16. The non-transitory computer readable medium of claim 15 , wherein the order data indicates that the customer desires to pick-up the order at a physical retail store.

17. The non-transitory computer readable medium of claim 15 , wherein the order data comprises an order identification number, store data characterizing a store location at which the customer desires to pick-up the order and item data characterizing ordered items.

18. The non-transitory computer readable medium of claim 15 , wherein the overall test substitution score is calculated by combining a relevance score and a preference score for each possible substitute item, wherein the relevance score indicates a similarity between a possible substitute item and an anchor item, and the preference score indicates a preference for one or more attributes of a possible substitute item.

19. The non-transitory computer readable medium of claim 15 , wherein the instructions, when executed by the at least one processor, cause the device to perform operations further comprising receiving acceptance data that indicates whether the customer accepted or rejected the at least one recommended substitute item.

20. The non-transitory computer readable medium of claim 15 , wherein the instructions, when executed by the at least one processor, cause the device to perform operations further comprising receiving test configuration data indicating one or more test conditions characterizing the comparison test, wherein the order is routed into the test group when the order satisfies the one or more test conditions.