Modified ordering recommendations
Disclosed embodiments may include systems and methods for providing modified order recommendations. Line item data from a set of transactions associated with a vendor can be collected. The line item includes transactional data such as price, quantity, and frequency. The set of transactions are associated with a plurality of customers of the vendor. Correlations between line item data can be determined via one or more machine learning techniques to train a recommendation model. An order recommendation to a customer can be determine based on the correlations of the recommendation model. An order of the customer can be modified according to the order recommendation.
1. A system comprising:
one or more processors and non-transitory media storing instructions that, when executed by the one or more processors, cause operations comprising:
analyzing a set of transaction data from a transaction data stream that includes data associated with a plurality of customers that ordered a product, wherein the set of transaction data includes, order quantity, order frequency, and order price;
training a recommendation model based on a result of analyzing of the set of transaction data, wherein the recommendation model is trained to identify, for a vendor of a plurality of vendors, quantities and frequencies for recurring orders that result in lower prices from the vendor;
in response to detecting a recurring order, of a customer, comprising a customer-approved quantity of the product, a customer-approved frequency of the recurring order, and a customer-approved price per unit of the product, inputting the recurring order into the recommendation model to obtain an order recommendation comprising (i) the customer-approved frequency of the recurring order as a recommended frequency and (ii) a recommended quantity and a recommended price, for each instance of a modified recurring order associated with both the customer and an other customer, that are respectively different from the customer-approved quantity and the customer-approved price in the recurring order; and
in place of the detected recurring order and an other order of the other customer, transmitting, to a vendor system associated with the vendor, the modified recurring order comprising (i) the recommended frequency and (ii) the recommended quantity, and the recommended price that are respectively different from the customer-approved quantity and the customer-approved price in the recurring order.
2. The system of claim 1 , the operations further comprising:
in response to (i) transmitting the modified recurring order to the vendor system and (ii) detecting processing of a transaction event for the recurring order, blocking the transaction event to prevent the processing of the transaction event from completing.
3. The system of claim 1 , the operations further comprising:
in response to (i) transmitting the modified recurring order to the vendor system and (ii) detecting processing of a transaction event for the recurring order, initiating a hold on the transaction event to temporarily pause the processing of the transaction event.
4. The system of claim 1 , wherein obtaining the order recommendation comprises:
invoking the recommendation model to compare the recurring order of the customer with the set of transaction data;
determining, based on comparing the recurring order with the set of transaction data, an optimized order recommendation for the customer; and
notifying the customer of the optimized order recommendation.
5. The system of claim 4 , wherein the optimized order recommendation is a determined bulk price that is not paid by the customer.
6. The system of claim 1 , wherein transmitting the modified recurring order comprises:
automatically modifying, via an interface of the vendor system, the recurring order of the customer based on the order recommendation.
7. A method comprising:
executing, by one or more processors, to perform operations comprising:
accessing a recommendation model (i) trained using a set of transaction data, comprising order prices, order quantities, and order frequencies for a product ordered by a plurality of customers, as a training data set (ii) trained to identify, for a vendor of a plurality of vendors, quantities and frequencies for recurring orders that result in lower prices from the vendor;
receiving a recurring order of a customer, the recurring order comprising a customer-approved frequency of the recurring order, a customer-approved quantity of the product, and a customer-approved price per unit of the product;
inputting the recurring order into the recommendation model to determine an order recommendation comprising (i) the customer-approved frequency as a recommended frequency and (ii) a recommended quantity and a recommended price, different from the customer-approved price in the recurring order, for each instance of a modified recurring order; and
in place of the recurring order, transmitting, to a vendor system associated with the vendor, the modified recurring order comprising the recommended frequency, the recommended quantity, and the recommended price different from the customer-approved price in the recurring order, and receiving, from the vendor, an approval for the modified recurring order.
8. The method of claim 7 , the operations further comprising:
in response to (i) transmitting the modified recurring order to the vendor system and (ii) detecting processing of a transaction event for the recurring order, blocking the transaction event to prevent the processing of the transaction event from completing.
9. The method of claim 7 , the operations further comprising:
in response to (i) transmitting the modified recurring order to the vendor system and (ii) detecting processing of a transaction event for the recurring order, initiating a hold on the transaction event to temporarily pause the processing of the transaction event.
10. The method of claim 7 , wherein determining the order recommendation comprises:
invoke the recommendation model to compare the recurring order of the product with the set of transaction data; and
determine a bulk discount is available based on the comparing, wherein the bulk discount is available for a set of circumstances.
11. The method of claim 10 , further comprising:
determine that the order recommendation satisfies the set of circumstances to achieve the bulk discount, wherein a previous order of the customer has not satisfied the set of circumstances.
12. The method of claim 7 , wherein transmitting the modified recurring order comprises:
automatically modify, via an interface of the vendor system, the recurring order of the product based on the order recommendation.
13. The method of claim 7 , wherein generating the order recommendation comprises generating the order recommendation comprising (i) the recommended quantity different from the customer-approved quantity in the recurring order and (ii) the recommended price different from the customer-approved price in the recurring order.
14. The method of claim 7 , wherein transmitting the modified recurring order comprises transmitting the modified recurring order to the vendor system in place of both the recurring order of the customer and an other order of an other customer.
15. One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause operations:
accessing a recommendation model (i) trained using transaction data, comprising order prices, order quantities, and order frequencies for a product ordered by a plurality of customers, as a training data set (ii) trained to identify, for a vendor of a plurality of vendors, quantities and frequencies for recurring orders that result in lower prices from the vendor;
receiving a recurring order of a customer, the recurring order comprising a customer-approved frequency of the recurring order, a customer-approved quantity of the product, and a customer-approved price per unit of the product;
generating, via the recommendation model, based on the recurring order, an order recommendation comprising (i) the customer-approved quantity as a recommended quantity and (ii) a recommended frequency and a recommended price, different from the customer-approved price in the recurring order, for each instance of a modified recurring order; and
transmitting, to the vendor, the modified recurring order comprising the recommended quantity, the recommended frequency, and the recommended price different from the customer-approved price in the recurring order.
16. The one or more non-transitory computer-readable media of claim 15 , the operations further comprising:
in response to (i) transmitting the modified recurring order to the vendor and (ii) detecting processing of a transaction event for the recurring order, blocking the transaction event to prevent the processing of the transaction event from completing.
17. The one or more non-transitory computer-readable media of claim 15 , the operations further comprising:
in response to (i) transmitting the modified recurring order to the vendor and (ii) detecting processing of a transaction event for the recurring order, initiating a hold on the transaction event to temporarily pause the processing of the transaction event.
18. The one or more non-transitory computer-readable media of claim 15 , wherein transmitting the modified recurring order to the vendor comprises:
interfacing with a vendor system to replace the recurring order with the modified recurring order.
19. The one or more non-transitory computer-readable media of claim 15 , wherein generating the order recommendation comprises generating the order recommendation comprising (i) the recommended frequency different from the customer-approved frequency in the recurring order and (ii) the recommended price different from the customer-approved price in the recurring order.