| CPC G06F 16/906 (2019.01) [G06F 16/164 (2019.01); G06Q 30/016 (2013.01)] | 16 Claims |
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1. A method for providing automated customer feedback monitoring in real-time to facilitate identification and resolution of errors, the method being implemented by at least one processor, the method comprising:
ingesting, by the at least one processor via an application programming interface, data from at least one source, the data including feedback information from at least one customer,
wherein the feedback information includes at least one social media complaint that relates to at least one from among a product and a service;
wherein the feedback information includes reviews from an application storefront, the application storefront corresponding to an application marketplace; and
wherein the data is ingested in real-time via the application programming interface when the feedback information is published by the at least one customer;
correlating, by the at least one processor, the feedback information to a specific customer by using a customer profile identifier, the feedback information including the customer profile identifier;
attributing, by the at least one processor, the feedback information to the specific customer based on the correlating;
persisting, by the at least one processor, the data in at least one file format, the at least one file format including a tabular file format;
enriching, by the at least one processor, the persisted data with keyword information, categorization information, and label information;
appending, by the at least one processor, the enriched data with historical feedback data based on a corresponding customer and a corresponding sentiment;
filtering, by the at least one processor, the appended data based on a rating and a keyword,
wherein the filtering narrows the feedback information to a subset that targets computing system issues;
identifying, by the at least one processor, at least one category for the filtered data based on at least one characteristic of the filtered data;
determining, by the at least one processor, whether at least one log file corresponds to the filtered data based on the identified at least one category, the at least one log file including at least one error log file that corresponds to an issue, and
when the at least one log file corresponds to the filtered data:
correlating, by the at least one processor, the filtered data with the at least one determined log file; and
determining, by the at least one processor, a priority level for the issue by using the correlated data and the at least one log file, and
when the at least one log file does not correspond to the filtered data:
tagging, by the at least one processor, the filtered data;
determining, by the at least one processor, whether the tagged data corresponds to at least one known issue by using a database exclusion table, and
when the tagged data does not correspond to the at least one known issue:
generating, by the at least one processor, at least one new issue tracking ticket for the tagged data, the at least one new issue tracking ticket corresponding to a new issue,
wherein the at least one new issue tracking ticket includes a data label, an assigned owner, a title, and a description; and
wherein the description includes information that relates to a customer, a customer comment, a rating, a timestamp, a device, a browser, a related exception, and a corresponding query;
associating, by the at least one processor, the tagged data with the at least one new issue tracking ticket; and
determining, by the at least one processor, a new priority level for the new issue by using the tagged data.
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