ATTRITION PREDICTING AND MITIGATING

1 . A method, comprising:

obtaining customer data associated with customers of an enterprise;

processing classification machine-learning models (MLMs) on the customer data producing feature-labeled data, each classification MLM identifies and labels one or more features from the customer data to produce a portion of the featured-labeled data;

processing a prediction MLM on the feature-labeled data to produce at least one list that predicts first customers predicted to leave the enterprise and second customers predicted to stay with enterprise when provided an incentive; and

providing the at least one list to one or more of an enterprise interface and an enterprise promotion system to mitigate customer attrition with respect to the second customers via the incentive.

2 . The method of claim 1 further comprising, iterating to the obtaining at preconfigured intervals of time.

3 . The method of claim 1 further comprising, processing a Recency, Frequency, Monetary (RFM) analyzer on the customer data to produce a second list of customers predicted by the RFM analyzer to leave the enterprise.

4 . The method of claim 3 further comprising, calculating evaluation metrics including accuracy and precision rates for the at least one list and the second list and an F1 accuracy metric for the at least one first list in view of actual subsequently observed results associated with specific customers that left the enterprise.

5 . The method of claim 4 further comprising, flagging customer data associated with inaccurate predictions produced by the prediction MLM in the customer data as training data based on comparing the at least one list and the second list in view of the subsequently observed results.

6 . The method of claim 5 further comprising, training the prediction MLM using the flagged customer data and the subsequently observed results to improve the F1 accuracy metric of the prediction MLM.

7 . The method of claim 6 further comprising, iterating to the obtaining at preconfigured intervals of time.

8 . The method of claim 1 , wherein obtaining further includes obtaining the customer data from a plurality of data stores associated with the enterprise.

9 . The method of claim 8 , obtaining further includes separating the customer data into sets of data, each set associated with a specific customer.

10 . The method of claim 9 , wherein separating further includes separating the customer data into the sets of data using non-personal identification information associated with each customer to anonymize the sets of data associated with the customers.

11 . A method, comprising:

labeling customer data for customer of an enterprise with features created labeled data;

training a machine-learning model (MLM) to use the labeled data as input and labeled actual results associated with customers leaving the enterprise as an expected output of the MLM;

obtaining current customer data for the enterprise;

labeling the current customer data with the features creating current labeled data;

providing the current labeled data as input to the MLM and receiving as output a first list of customers predicted to leave the enterprise and a second list of customers predicted to stay with the enterprise when provided an incentive from the enterprise; and

providing the first list and the second list to the enterprise.

12 . The method of claim 11 further comprising, comparing a third list produced by a Recency, Frequency, Monetary (RFM) analyzer against the first list and the second list in view of actual observed results for specific customer that left the enterprise.

13 . The method of claim 12 further comprising, identifying select current labeled data associated with inaccurate predictions of the MLM in the first list or the second list.

14 . The method of claim 13 further comprising, re-training the MLM using the select current labeled data causing the MLM to update after the re-training.

15 . The method of claim 14 further comprising, iterating to the labeling of the customer data to obtain updated customer data for the customers of the enterprise at preconfigured intervals of time.

16 . The method of claim 11 , wherein labeling the customer data further includes processing the customer data through classification MLMs to obtain the labeled customer data.

17 . The method of claim 16 , wherein processing further includes processing first classification MLMs in parallel against the customer data and merging output produced by the first classification into an intermediate labeled data.

18 . The method of claim 17 , wherein processing further includes processing at least one second classification MLM on the intermediate labeled data to obtain the labeled customer data.

19 . A system, comprising:

a cloud server comprising at least one processor and a non-transitory computer-readable storage medium,

the non-transitory computer-readable storage medium comprising executable instructions,

wherein the executable instructions, when executed by the at least one processor cause the at least one processor to perform operations comprising:

training classification machine learning models (MLMs) to identify features from customer data of customers associated with an enterprise;

merging labeled customer data into intermediate customer data;

training an attrition prediction MLM to take as input the intermediate customer data and produce as output a first list of customers predicted to leave the enterprise and a second list of customers predicted to stay with the enterprise when provided an incentive;

at predefined intervals of time obtaining updated customer data from data stores of the enterprise;

during each interval:

processing the classification MLMs to obtain current labeled feature data;

merging the current labeled featured data into current intermediate customer data;

processing the attrition prediction MLM to obtain a current first list of customers and a currency second list of customers;

using an application programming interface (API) to provide the current first list and the current second list to an enterprise interface and to an enterprise promotion system;

processing a Recency, Frequency, Monetary (RFM) analyzer against the updated customer data and obtaining a third list of customer predicted by the RFM analyzer to leave the enterprise;

comparing current the first list, the current second list, and the third list against actual observed results where specific customers left the enterprise and identifying in the current intermediate customer data portions associated with inaccurate predictions made by the attrition prediction MLM; and

re-training the attrition prediction MLM on the current intermediate customer data portions as a continuous feedback loop to improve an F1 accuracy metric of the attrition prediction MLM in providing the current first list and the current second list.

20 . The system of claim 19 , wherein the operations associated with the using the API further includes rendering the first list and the second list within a dashboard screen associated with the enterprise interface.