Adaptive scheduling of electronic messaging based on predictive consumption of the sampling of items via a networked computing platform
Various embodiments relate generally to data science and data analysis, computer software and systems, and digital messaging and control systems to determine at an adaptive distribution platform using a distribution predictor a point of time associated with a scheduled delivery of an item coinciding substantially with a zone of time associated with a predicted distribution date of the item, to transmit to a device an electronic message having data associated with an item characteristic and other data associated with a control user input configured to adapt the scheduled delivery, to receive a response to the electronic message indicating one or more actions to manage an order associated with the item, to determine whether a replenishment adjustment of the item is requested, and to transmit a control signal to a merchant computing system to perform one or more data operations associated with the order.
1. A method, comprising:
determining at an adaptive distribution platform using a distribution predictor a point of time associated with a scheduled delivery of an item coinciding substantially with a zone of time associated with a predicted distribution date of the item;
transmitting from the adaptive distribution platform an electronic message to a device, the electronic message having data associated with an item characteristic and other data associated with a control user input configured to adapt the scheduled delivery of the item using a data communication protocol;
receiving another electronic message at the adaptive distribution platform, the another electronic message comprising a response to the electronic message and comprising data associated with one or more actions configured to manage an order associated with the item;
determining whether a replenishment adjustment of the item is requested of the adaptive distribution platform using a transaction controller implementing a machine learning algorithm to identify a request, the request being identified by the machine learning algorithm by parsing other messages transmitted to the adaptive distribution platform, the transaction controller operating with a predictive model configured to operate on one or more XML formatted messages to generate a control signal to replenish the item;
transmitting the control signal to a merchant computing system in data communication with the adaptive distribution platform to perform one or more data operations associated with the order;
implementing an information feedback predictor module configured to predict automatically a sample of another item for a user based on one or more classified attributes associated with the item and the user, the information feedback predictor module configured to form one or more clusters of data through application of a machine learning application to generate an adaptive schedule for transmitting electronic messages to automatically facilitate feedback at intervals of time during which the user predictively consumed the sample to provide an assessment whether to generate an order or subscription for delivery of the another item based on the sample; and
applying the machine learning application to enhance a sample-to-purchase conversion metric including a ratio of a number of the another items to a number of samples sent over a number of users to generate data representing analytic insights to automatically refine a predicted date of consumption of the sample,
wherein the machine learning application is configured to access stored data associated with predicting consumption of the sample and data representing the feedback to refine the predicted date of consumption of the sample, the machine learning application further configured to adjust functionality automatically to optimize outputted data, which includes data representing the feedback associated with the sample-to-purchase conversion metric to select and to provide the sample of the another item automatically, the another item being different than the item.
2. The method of claim 1 , wherein the determining whether the replenishment adjustment is requested further comprises recalibrating the control signal if the replenishment adjustment is requested.
3. The method of claim 1 , wherein the determining whether the replenishment adjustment is requested further comprises recalibrating a transmission of the control signal if the replenishment adjustment is requested.
4. The method of claim 1 , wherein the zone of time is determined by a zone generator associated with the adaptive distribution platform.
5. The method of claim 1 , wherein the data communication protocol is a short message service.
6. The method of claim 1 , wherein the data communication protocol is SMS.
7. The method of claim 1 , wherein the data communication protocol is a text message service.
8. The method of claim 1 , wherein the electronic message is a text message.
9. The method of claim 1 , wherein the electronic message is a text message configured to be transmitted using a short message service.
10. The method of claim 1 , wherein the electronic message is a text message configured to be transmitted using SMS.
11. A method, comprising:
determining at an adaptive distribution platform a date associated with a subscription of an item coinciding substantially with a date range associated with a predicted distribution date of the item in accordance with the subscription;
transmitting from the adaptive distribution platform a text message to a device, the text message comprising a request having data associated with an item characteristic and a control user input and being configured to be displayed on the device;
receiving another text message in response to the request, the another text message comprising other data associated with an action configured to manage an order associated with the item;
determining whether an action associated with a subscription of the item is requested of the adaptive distribution platform using a transaction controller implementing a machine learning algorithm to identify the request, the request being identified by the machine learning algorithm by parsing other messages transmitted to the adaptive distribution platform, the transaction controller operating with a predictive model configured to operate on one or more XML formatted messages to generate a control signal to replenish the item;
transmitting the control signal from the adaptive distribution platform to a merchant computing system in data communication with the adaptive distribution platform to perform one or more data operations associated with the order;
implementing an information feedback predictor module configured to predict automatically a sample of another item for a user based on one or more classified attributes associated with the item and the user, the information feedback predictor module configured to form one or more clusters of data through application of a machine learning application to generate an adaptive schedule for transmitting electronic messages to automatically facilitate feedback at intervals of time during which the user predictively consumed the sample to provide an assessment whether to generate an order or subscription for delivery of the another item based on the sample; and
applying the machine learning application to enhance a sample-to-purchase conversion metric including a ratio of a number of the another items to a number of samples sent over a number of users to generate data representing analytic insights to automatically refine a predicted date of consumption of the sample,
wherein the machine learning application is configured to access stored data associated with predicting consumption of the sample and data representing the feedback to refine the predicted date of consumption of the sample, the machine learning application further configured to adjust functionality automatically to optimize outputted data, which includes data representing the feedback associated with the sample-to-purchase conversion metric to select and to provide the sample of the another item automatically, the another item being different than the item.
12. The method of claim 11 , wherein the date is determined by a distribution predictor in data communication with the adaptive distribution platform.
13. The method of claim 11 , wherein the date range is determined by the adaptive distribution platform using the date predicted by a distribution predictor.
14. The method of claim 11 , further comprising associating the date with the date range.
15. The method of claim 11 , wherein the text message is generated when the date is determined by a distribution predictor to be within the date range.
16. The method of claim 11 , wherein the control user input is configured to perform a data operation on the subscription.
17. The method of claim 11 , wherein the control user input comprises input data used to modify the subscription when the another text message is received by the adaptive distribution platform.
18. The method of claim 11 , wherein the control user input is configured to adapt the subscription when the another text message is received at the adaptive distribution platform.
19. The method of claim 11 , wherein the control user input is configured to modify the order associated with the item, the order being associated with the subscription.
20. A non-transitory computer readable medium having one or more computer program instructions configured to perform a method, the method comprising:
determining at an adaptive distribution platform using a distribution predictor a point of time associated with a scheduled delivery of an item coinciding substantially with a zone of time associated with a predicted distribution date of the item;
transmitting from the adaptive distribution platform an electronic message to a device, the electronic message having data associated with an item characteristic and other data associated with a control user input configured to adapt the scheduled delivery of the item using a data communication protocol;
receiving another electronic message at the adaptive distribution platform, the another electronic message comprising a response to the electronic message and comprising data associated with one or more actions configured to manage an order associated with the item;
determining whether a replenishment adjustment of the item is requested of the adaptive distribution platform using a transaction controller implementing a machine learning algorithm to identify a request, the request being identified by the machine learning algorithm by parsing other messages transmitted to the adaptive distribution platform, the transaction controller operating with a predictive model configured to operate on one or more XML formatted messages to generate a control signal to replenish the item;
transmitting the control signal to a merchant computing system in data communication with the adaptive distribution platform to perform one or more data operations associated with the order;
implementing an information feedback predictor module configured to predict automatically a sample of another item for a user based on one or more classified attributes associated with the item and the user, the information feedback predictor module configured to form one or more clusters of data through application of a machine learning application to generate an adaptive schedule for transmitting electronic messages to automatically facilitate feedback at intervals of time during which the user predictively consumed the sample to provide an assessment whether to generate an order or subscription for delivery of the another item based on the sample; and
applying the machine learning application to enhance a sample-to-purchase conversion metric including a ratio of a number of the another items to a number of samples sent over a number of users to generate data representing analytic insights to automatically refine a predicted date of consumption of the sample,
wherein the machine learning application is configured to access stored data associated with predicting consumption of the sample and data representing the feedback to refine the predicted date of consumption of the sample, the machine learning application further configured to adjust functionality automatically to optimize outputted data, which includes data representing the feedback associated with the sample-to-purchase conversion metric to select and to provide the sample of the another item automatically, the another item being different than the item.