METHODS AND SYSTEMS FOR ARTIFICIAL INTELLIGENCE INSIGHTS FOR RETAIL LOCATION

1 . A method of managing a retail environment, comprising:

collecting data from a plurality of retail information systems including at least an inventory system, a loss prevention system, and a retail traffic system;

predicting a condition based on at least one machine-learning model and a combination of the collected data from at least two systems of the plurality of retail information systems; and

pushing an alert to a user identifying the condition and a recommended action, the user having a user persona matching a persona associated with the condition.

2 . The method of claim 1 , further comprising:

receiving a voice query, from the user having the user persona;

interpreting the voice query based on the user persona to generate a machine query for a predicted condition;

answering the query using the at least one machine-learning model to generate a predicted condition and a recommended action; and

providing a voice response identifying the answer and the recommended action.

3 . The method of claim 1 , wherein predicting the condition comprises:

determining a predicted rate of consumption of a product based on historical inventory levels, a historical traffic level, and a current traffic level;

determining a time that a current inventory of the product will be depleted at the predicted rate of consumption; and

detecting a low inventory condition if the time is within a threshold time.

4 . The method of claim 1 , wherein predicting the condition comprises:

identifying, by the at least one machine-learning model, a pattern in the collected data; and

detecting a deviation of current data collected within a threshold time period from the pattern.

5 . The method of claim 4 , wherein identifying the pattern comprises, correlating by the at least one machine learning model, a performance indicator with the combination of the collected data from at least two systems of the plurality of retail information systems.

6 . The method of claim 1 , wherein the at least one machine-learning model is trained on training sets that are subsets of the data from the plurality of retail information systems that have been labeled with corresponding events.

7 . The method of claim 6 , further comprising:

detecting an occurrence of an event based on business rules applied to the combination of the collected data;

labeling a data pool of the combination of the collected data prior to the occurrence of the event with the event to generate one of the training sets; and

training the at least one machine learning model to classify a current combination of the collected data into events based on the training sets.

8 . A non-transitory computer readable medium storing computer executable instructions that when executed by a processor cause the processor to:

collect data from a plurality of retail information systems including at least an inventory system, a loss prevention system, and a retail traffic system;

predict a condition based on at least one machine-learning model and a combination of the collected data from at least two systems of the plurality of retail information systems; and

push an alert to a user identifying the condition and a recommended action, the user having a user persona matching a persona associated with the condition.

9 . The non-transitory computer readable medium of claim 8 , further comprising code to:

receive a voice query, from the user having the user persona;

interpret the voice query based on the user persona to generate a machine query for a predicted condition;

answer the query using the at least one machine-learning model to generate an answer and a recommended action; and

provide a voice response identifying the answer and the recommended action.

10 . The non-transitory computer readable medium of claim 8 , wherein the code to predict the condition comprises code to:

determine a predicted rate of consumption of a product based on a historical inventory a historical traffic level, and a current traffic level;

determine a time that a current inventory of the product will be depleted at the predicted rate of consumption; and

detect a low inventory condition if the time is within a threshold time.

11 . The non-transitory computer readable medium of claim 8 , wherein the code to predict the condition comprises code to:

identify, by the at least one machine-learning model, a pattern in the collected data; and

detect a deviation of current data collected within a threshold time period from the pattern.

12 . The non-transitory computer readable medium of claim 11 , wherein the code to identify the pattern comprises code to correlate, by the at least one machine learning model, a performance indicator with the combination of the collected data from at least two systems of the plurality of retail information systems.

13 . The non-transitory computer readable medium of claim 8 , wherein the machine-learning model is trained on training sets that are subsets of the data from the plurality of retail information systems that have been labeled with corresponding events.

14 . The non-transitory computer readable medium of claim 13 , further comprising code to:

detect an occurrence of an event based on business rules applied to the combination of the collected data;

label a data pool of the combination of the collected data prior to the occurrence of the event with the event to generate one of the training sets; and

train the machine learning model to classify a current combination of the collected data into events based on the training sets.

15 . A system for managing a retail environment, comprising:

a plurality of retail information systems including at least an inventory system, a loss prevention system, and a retail traffic system; and

a computer system comprising a memory storing computer executable instructions and a processor configured to execute the instructions to:

collecting data from the plurality of retail information systems;

predict a condition based on a machine-learning model and a combination of the collected data from at least two systems of the plurality of retail information systems; and

push an alert to a user identifying the condition and a recommended action, the user having a user persona matching a persona associated with the condition.

16 . The system of claim 15 , wherein the processor is configured to execute the instructions to:

receive a voice query, from the user having the user persona;

interpret the voice query based on the user persona to generate a machine query for a predicted condition;

answer the query using the machine-learning model to generate an answer and a recommended action; and

provide a voice response identifying the answer and the recommended action.

17 . The system of claim 15 , wherein the processor is configured to execute the instructions to:

determine a predicted rate of consumption of a product based on a historical inventory a historical traffic level, and a current traffic level;

determine a time that a current inventory of the product will be depleted at the predicted rate of consumption; and

detect a low inventory condition if the time is within a threshold time.

18 . The system of claim 15 , wherein the processor is configured to execute the instructions to:

identify, by the machine-learning model, a pattern in the collected data; and

detect a deviation of current data collected within a threshold time period from the pattern.

19 . The system of claim 15 , wherein the processor is configured to execute the instructions to correlate, by the machine learning model, a performance indicator with the combination of the collected data from at least two systems of the plurality of retail information systems.

20 . The system of claim 15 , wherein the processor is configured to execute the instructions to:

detect an occurrence of an event based on business rules applied to the combination of the collected data;

label a data pool of the combination of the collected data prior to the occurrence of the event with the event to generate a the training set; and

train the machine learning model to classify a current combination of the collected data into events based on the training set.