Patent Publication Number: US-2022222604-A1

Title: Feedback visualization tool

Description:
TECHNICAL FIELD 
     The present disclosure relates generally to a feedback visualization tool. 
     BACKGROUND 
     Across various enterprises, feedback is oftentimes collected as part of improving business. For example, an enterprise may collect and review feedback from a plurality of different audiences to gain an understanding of wants and needs of its customers, employees, and/or other business partners and/or affiliates, and may further collect feedback to identify customers&#39; level of satisfaction with products or services. Such feedback is usually submitted as unstructured text, which may include a variety of terminology and may not specifically identify a particular product, service, or issue for which the feedback is being provided. As can be appreciated, this can make it difficult to determine when multiple pieces of feedback may be related to a particular product, service, or issue, and can result in erroneously excluding relevant content from being properly aggregated. As such, an enterprise may be unable to gain a holistic view of received feedback, ideas, and/or suggestions, which can prevent the enterprise from discovering and implementing measures that may allow the business to operate more efficiently and/or to otherwise improve business. 
     SUMMARY 
     In summary, the present disclosure relates to methods, systems, and a computer readable storage device for providing feedback insights and visualization. In the context of the present disclosure, a feedback insights and visualization system is provided that collects feedback received from a plurality of different channels, and uses a modifiable and customizable synonyms list to automatically aggregate related feedback that may be described using a variety of terminology within unstructured text to ensure that insights, such as trends, common issues, and/or issues and solutions across an enterprise, are represented and discoverable in aggregated feedback. This allows for a visualization of insights to be generated and displayed in a user interface and for various systems to make determinations about trends and issues within an enterprise from a holistic vantage point. Various aspects are described in this disclosure, which include, but are not limited to, the following aspects. 
     In one aspect, a system for providing feedback insights and visualization includes a computing system that includes at least one processor and a memory communicatively coupled to the at least one processor such that when instructions stored on the memory are executed by the at least one processor, operate to: store, in a synonyms list, a terminology grouping comprising at least a first term and a second term, wherein inclusion of the first term and the second term in the terminology grouping causes the terms to be linked together and to a subtopic-of-interest; receive a plurality of feedback records from a plurality of different feedback sources; and in response to an indication of a selection of a topic-of-interest: determine feedback records associated with the topic-of-interest; identify, in the feedback records determined to be associated with the topic-of-interest, an inclusion of the first term or the second term; determine metrics associated with the feedback records determined to be associated with the topic-of-interest, wherein the metrics include a frequency of inclusion of the first term or the second term in the feedback records; and generate a data visualization for display in a user interface, the data visualization including the subtopic-of-interest, wherein display attributes of the subtopic-of-interest are indicative of the frequency of inclusion of the first term or the second term in the feedback records determined to be associated with the topic-of-interest. 
     In another aspect, a method of providing feedback insights and visualization comprises: storing, in a synonyms list, a terminology grouping comprising at least a first term and a second term, wherein inclusion of the first term and the second term in the terminology grouping causes the terms to be linked together and to a subtopic-of-interest; receiving a plurality of feedback records from a plurality of different feedback sources; and in response to receiving an indication of a selection of a topic-of-interest: determining feedback records associated with the topic-of-interest; identifying, in the feedback records determined to be associated with the topic-of-interest, an inclusion of the first term or the second term; determining metrics associated with the feedback records determined to be associated with the topic-of-interest, wherein the metrics include a frequency of inclusion of the first term or the second term in the feedback records; and generating a data visualization for display in a user interface, the data visualization including the subtopic-of-interest, wherein display attributes of the subtopic-of-interest are indicative of the frequency of inclusion of the first term or the second term in the feedback records determined to be associated with the topic-of-interest. 
     In another aspect, a computer-readable storage device comprises computer-executable instructions which, when executed by a computing system, cause the computing system to perform a method of providing feedback insights and visualization, the method comprising storing, in a synonyms list, a terminology grouping comprising at least a first term and a second term, wherein inclusion of the first term and the second term in the terminology grouping causes the terms to be linked together and to a subtopic-of-interest; receiving a plurality of feedback records from a plurality of different feedback sources; and in response to receiving an indication of a selection of a topic-of-interest: determining feedback records associated with the topic-of-interest; identifying, in the feedback records determined to be associated with the topic-of-interest, an inclusion of the first term or the second term; determining metrics associated with the feedback records determined to be associated with the topic-of-interest, wherein the metrics include a frequency of inclusion of the first term or the second term in the feedback records; and generating a data visualization for display in a user interface, the data visualization including the subtopic-of-interest, wherein display attributes of the subtopic-of-interest are indicative of the frequency of inclusion of the first term or the second term in the feedback records determined to be associated with the topic-of-interest. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of an example operating environment for a feedback system according to an embodiment; 
         FIG. 2  is a block diagram of an example schematic diagram of an example feedback system according to an embodiment; 
         FIG. 3  is an illustration of an example synonyms list; 
         FIGS. 4A and 4B  are illustrations of an example user interface including a visual representation of subtopics-of-interest identified as associated with a user-selected topic-of-interest; 
         FIG. 5  is a flow chart depicting general stages of an example method for providing flexible feedback aggregation and visualization; and 
         FIG. 6  is a block diagram of an example computing system. 
     
    
    
     DETAILED DESCRIPTION 
     Various embodiments will be described in detail with reference to the drawings, wherein like reference numerals represent like parts and assemblies throughout the several views. Reference to various embodiments does not limit the scope of the claims attached hereto. Additionally, any examples set forth in this specification are not intended to be limiting and merely set forth some of the many possible embodiments for the appended claims. In general, the present disclosure relates to a system and method for providing flexible feedback aggregation and visualization. More specifically, the present disclosure relates to a system and method for providing commonly-unrelated terminology aggregation for flexible categorization of feedback related to a topic-of-interest and collected from a plurality of feedback sources across different feedback channels. The system and method include utilization of a synonyms list for providing a linkage of terms that may ordinarily be unrelated and for generating a data visualization representing at least a frequency of inclusion of the terms in received feedback related to the topic-of-interest. The data visualization enables a user of the system to easily identify or detect common issues or issues and solutions across multiple enterprise locations and/or other enterprise divisions. 
     In general, the present application relates to a feedback aggregation and presentation tool. More particularly, the present disclosure describes a system, method, and a computer readable storage device for providing commonly-unrelated terminology aggregation for generating a subtopic-of-interest data visualization based on the commonly-unrelated terminology aggregation that enables a detection of common issues or issues and solutions across multiple enterprise locations. 
       FIG. 1  is a schematic diagram of an example operating environment  100  for flexible feedback aggregation and visualization. The example operating environment  100  includes an enterprise  102  that is comprised of multiple locations  104   a - n  (generally  104 ) and/or other enterprise-level divisions  106   a - n  (generally  106 ), such as departments, operation centers, etc., that all collaborate to achieve big-picture business objectives. Each location  104  or division  106  may be comprised of further divisions (i.e., sub-divisions  108   a - n  (generally  108 )). According to one illustrative example, the enterprise  102  is a retail enterprise that provides a flow of inventory from a vendor/supplier  112  to a customer  110 . The retail enterprise  102  may include various retail locations  104 , and may be further comprised of other enterprise divisions  106 , such as a headquarters and one or more operation centers (e.g., inventory receive centers, flow centers). Although examples are presented primarily regarding the retail industry, these are presented as non-limiting examples, as enterprises  102  in other goods, service, healthcare, educational, professional, and other industries may also make use of the present disclosure. 
     According to an aspect, the enterprise  102  may collect feedback records  118  from a plurality of feedback sources  120  to improve business (e.g., improve efficiencies, reduce inefficiencies, improve customer satisfaction, improve employee satisfaction). For example, the enterprise  102  may collect feedback records  118  to gain an understanding of wants and needs of its customers  110 , employees  114 , and/or other enterprise affiliates  116 , and may further collect feedback records  118  to identify customers&#39; level of satisfaction with products and/or services. Feedback records  118  may be submitted voluntarily, in response to a request for feedback, and/or as part of a recording (with permission) of a conversation (e.g., help session, chat session, customer service session). In some examples, a feedback record  118  may be requested in association with a particular product, service, or user experience with the enterprise  102 . The feedback records  118  can be submitted by a plurality of different feedback sources  120  (e.g., customers  110 , employees  114 , and/or other enterprise affiliates  116 ) via a variety of feedback channels  122   a - n  (generally  122 ). In some examples, such channels  122  can include using a telephone or phone application operating on a computing device  126  to provide, via a voice call, a feedback record  118  to a feedback insights and visualization system, herein referred to as a feedback system  124  (e.g., in association with a voice call to a customer service agent  134 ). In other examples, the channels  122  can include using a computing device  126 , such as a mobile phone/computing device  126   a , a desktop or laptop computing device  126   b , or other type of computing device  126  to provide a feedback record  118  to the feedback system  124 . For example, the computing device  126  may communicate with a network, which communicates with a server computer of the feedback system  124  where the feedback records  118  may be aggregated and stored. In some examples, one or more intermediary server computers (e.g., a server computer associated with a service  220  ( FIG. 2 )) may receive, process, and store (e.g., in a data store  216 ) submitted feedback records  118  prior to being aggregated and stored by the feedback system  124 . 
     According to one example, a customer  110  may use a computing device  126 , such as a mobile phone  126   a , to access a help function, wherein a feedback record  118  may be received in association with the customer  110  using the help function. In some examples, the help function may operate in conjunction with a chatbot  128 . As another example, a customer  110  may use a computing device  126 , such as a desktop or laptop computer  126   b , to access a support function, wherein feedback record  118  may be provided by the customer  110  in association with using the support function. As another example, a customer  110  may use a computing device  126  to access a webpage of the enterprise  102  to provide a feedback record  118  via a feedback module. As another example, another enterprise affiliate  116 , such as a transportation or delivery worker, may provide a feedback record  118  in association with transporting inventory to the enterprise  102  or to a customer  110 . As another example, a customer  110  may use a telephone or phone application operating on a computing device  126  to speak with a customer service agent  134  (e.g., via a voice call), and a feedback record  118  in association with the voice call with the customer service agent  134  may be captured and/or provided. As another example, a customer  110 , supplier/vendor  112 , employee  114 , or other enterprise affiliate  116  may provide a feedback record  118  via a survey  132  (e.g., an online survey, a phone survey, an in-person survey). In some examples, the survey  132  may be in association with a purchased product or service, and may identify the product or service by its brand name or using other terminology. In other examples, the survey  132  may be in association with a particular user experience with the enterprise  102 , such as an in-person experience at an enterprise location  104 , an online experience, a telephone call experience, a delivery experience, or an experience with another enterprise division  106 . The user experience may be experienced by one of various types of affiliates of the enterprise  102 , such as a customer  110 , a supplier/vendor  112 , employee  114 , or other enterprise affiliate  116 . As another example, an employee  112  may provide a feedback record  118  via utilization of an employee chat function  130 . As should be appreciated, the above examples are presented as non-limiting examples. Other feedback sources  120  and/or feedback channels  122  are possible and are within the scope of the present disclosure. 
     According to an aspect, a feedback record  118  may be provided/submitted as unstructured text. Unstructured text may include a variety of terminology and may or may not specifically identify a particular product, service, or issue for which the feedback record  118  is being provided. For example, terminology used in unstructured text included in submitted feedback records  118  may vary based on the feedback source  120 , a location/region in association with the feedback source  120 , the division of the enterprise  102  (e.g., store/location  104 , and/or other enterprise division  106 ) in association with submitted feedback records  118 , the product, service, or issue for which a feedback record  118  is being submitted, or other reason. As can be appreciated, variations in terminology used in unstructured text can make it difficult to determine when multiple pieces of feedback records  118  may be related to a particular product, service, or issue. Moreover variations in terminology used in unstructured text can make it difficult to properly aggregate that terminology according to a unified set of categorized content that can easily be displayed based on a selectable subset of the data (e.g., by store/location  104  or region) to identify a subtopic-of-interest, such as a common or specific issue encountered by customers  110 , suppliers/vendors  112 , employees  114 , and/or other enterprise affiliates  116 . For example, as a result of difficulties in properly aggregating terminology, feedback records  118  relevant to a subtopic-of-interest may not be determined as relevant and may be excluded from a presentation or analysis of feedback associated with a topic-of-interest. Accordingly, the enterprise  102  may be unable to gain a holistic view of received feedback records  118 , ideas, and/or suggestions, which can prevent the enterprise  102  from discovering and implementing measures that may allow the business to operate more efficiently and/or to otherwise improve business. To overcome at least the above problems with accurately identifying feedback topics-of-interest, the feedback system  124  of the present disclosure is configured to provide flexible feedback aggregation and visualization based on a modifiable and customizable synonyms list, which enables a rich text search to be performed across all columns of data and a data visualization  136  associated with related feedback to be generated and displayed in a user interface  138  of a computing device  126   c . For example, the feedback system  124  of the present disclosure is configured to generate and extract insights from unstructured text received from a plurality of different audiences, and the data visualization  136  enables users  140  to easily identify subtopics-of-interest that are relevant to a topic-of-interest. Example components of the feedback system  124  and operations of the components are described below. 
       FIG. 2  is a schematic diagram of an example feedback system  124  according to an embodiment. With reference now to  FIG. 2 , the example feedback system  124  comprises various subsystems including: an aggregator  204 , an aggregated data store  218 , a synonyms list generator  206 , a synonyms list  208 , a classifier  210 , and a visualization engine  212 . Each of the subsystems may be implemented as one or more separate computing systems  600  ( FIG. 6 ), software applications, program modules, or the like. 
     The aggregator  204  is operative or configured to aggregate feedback records  118  submitted from various feedback sources  120  and received via various feedback channels  122 . The aggregator  204  is configured to access a data store  216  configured to house received feedback records  118 . According to an aspect, at least a portion of the feedback records  118  includes unstructured text. In some examples, at least a portion of feedback records  118  received from feedback sources  120  may be received by a service  220 , such as a publish-subscribe messaging system, and then stored in the data store  216 . In other examples, at least a portion of feedback records  118  received from feedback sources  120  may be published to the data store  216  via utilization of an application programming interface (API). For example, the data store  216  may provide an API that allows the feedback channel  122  to communicate with the data store  216  for storage of feedback records  118 . In other examples, the feedback system  124  may expose an API via which feedback records  118  can be automatically received and processed by the feedback system  124  as described below. 
     In various aspects, as part of aggregating feedback records  118 , the aggregator  204  is operative or configured to capture a set of parameters around unstructured text included in a feedback record  118 , and store the parameters in association with the unstructured text in an aggregated data store  218 . In some examples, the aggregated data store  218  is a data table. In some examples, the set of parameters may include at least an identification of a particular enterprise location  104  (e.g., a retail store location). In some examples, the set of parameters may include at least an identification of a particular issue or topic that the submitted feedback record  118  is concerned (e.g., an out-of-stock item, a delivery issue). In some examples, the set of parameters may further include an identification of other/additional aspects about a received feedback record  118 . In some examples, the aggregator  204  is operative or configured to apply, to feedback data, metadata tags corresponding to the parameters. 
     The synonyms list generator  206  is operative or configured to receive and store, in a synonyms list  208 , a listing of grouped or linked words/terminology, wherein the grouped or linked words/terminology may otherwise be considered separate and unrelated. According to an aspect, the synonyms list  208  is modifiable and customizable. The synonyms list  208  may be stored in a data store  222  included in or in communication with the feedback system  124 . According to an example, a feedback record  118  received from a first feedback source  120  may include unstructured text that uses wording/terminology to describe a product/inventory item, service, experience, or other enterprise function that varies from wording/terminology used in another feedback record  118  received from a second feedback source  120  to describe the same or a similar product/inventory item, service, experience, or other enterprise function. In various aspects, the varying wording/terminology used in two different feedback records  118  may normally be considered separate and/or unrelated; however, when the varying wording/terminology are included and grouped/linked in the synonyms list  208 , feedback records  118  that include the varying wording/terminology may then be determined to be related. 
     An example synonyms list  208  is illustrated in  FIG. 3 . With reference to  FIG. 3 , the example synonyms list  208  includes a plurality of terminology groupings  300   a - n  (generally  300 ), wherein each terminology grouping  300  includes at least a first term  302   a - n  (generally  302 ) and a second term  304   a - n  (generally  304 ), and the inclusion of the terms  302 , 304  in the terminology grouping  300  causes the terms  302 , 304  to be linked for aggregating feedback records  118  according to an associated subtopic-of-interest  306   a - n  (generally  306 ). In some examples, the subtopic-of-interest  306  is the second term  304 . In other examples, the subtopic-of-interest  306  is a normalized term. According to an aspect, the first term  302  or the second term  304  can include one or a combination of a single word, a plurality of words, a phrase, an abbreviation, an acronym, numbers, spaces, special characters, symbols, etc. As will be described below, a plurality of subtopics-of-interest  306  may be determined as associated with feedback records  118  about a particular issue or topic, a particular enterprise location  104 , a region comprised of a plurality of enterprise locations  104 , a particular enterprise location subdivision  108 , or a particular other enterprise division  106 , etc. The plurality of subtopics-of-interest  306  may be presented in a user interface  138 , and when a subtopic-of-interest  306  is selected, feedback records  118  that include the first term  302  or the second term  304  may be aggregated and presented as output to a user  140 . According to an aspect, the synonyms list  208  provides an additional layer of customization that can be applied across any search that allows for aggregating or treating as common words/terminology (i.e., terms  302 , 304 ) that may otherwise be considered unrelated. 
     As a first example, a first grouping  300   a  may link or associated a first term  302   a  “crooked dock” with a second term  304   a  “driver_feedback.” As a second example, in a second grouping  300   b , a different first term  302   a  “driver did not” may be linked/associated with a second term  304   b  “driver_feedback.” In this example, the second term  304   b  “driver_feedback” is the same as the second term  304   a  “driver_feedback” in the first example grouping  300   a . Thus, multiple first terms  302  can be linked or associated with a particular second term  304 , and may be further linked or associated with a particular subtopic-of-interest  306  term that is displayed to a user  140  in a user interface  138 . Similarly, in third and fourth example groupings  300   c,d , different first terms  302   c,d  “out of date” and “too cold” are both linked to the same second term  304   c,d  “food_quality,” and in fifth, sixth, and seventh example groupings  300   e,f,g,h , different first terms  302   e,f,g,h  “pillar,” “column,” and “pole” are linked to the same second term  304   e,f,g,h  “obstruction.” As can be appreciated, the first terms  302  and second terms  304  that are linked in the example groupings  300  may, in various contexts, be otherwise considered as unrelated. 
     In some examples, the synonyms list generator  206  is operative or configured to receive a manual user-entry of two or more terms  302 , 304  and to store the two or more terms  302 , 304  in a grouping  300  such that the two or more terms  302 , 304  are linked. In other examples, the synonyms list generator  206  is operative or configured to generate the synonyms list  208  based on a set of business rules. In other examples, the synonyms list generator  206  is operative or configured to use supervised machine learning methods to learn, based on a training dataset, a mapping function from an input variable to an output variable, wherein the mapping function can be applied to identify and group/link two or more terms  302 , 304  in the synonyms list  208 . The training dataset can include previously-linked terms  302 , 304 . In other examples, the synonyms list generator  206  is operative or configured to use unsupervised machine learning methods to discover inherent groupings of two or more terms  302 , 304  in feedback records  118 . In other examples, the synonyms list generator  206  is operative or configured to use semi-supervised machine learning methods to generate the synonyms list  208 . 
     With reference again to  FIG. 2 , the classifier  210  is operative or configured to receive an indication of a request for feedback output associated with a topic-of-interest, and to generate a query on the aggregated data store  218  for feedback content related to the topic-of-interest. According to some aspects, the topic-of-interest corresponds to a metadata tag applied to a feedback record  118  stored in the aggregated data store  218 . As an example, a topic-of-interest may be a particular issue or topic, a particular enterprise location  104 , a region comprised of a plurality of enterprise locations  104 , a particular enterprise location subdivision  108 , or a particular enterprise division  106 , etc., about which the submitted feedback record  118  is concerned. For example, the classifier  210  may perform a search on metadata tags for a parameter associated with the topic-of-interest, and determine search results comprising one or more feedback records  118  that match the topic-of-interest. 
     In some examples, the classifier  210  is further operative or configured to perform a text mining operation for discovering terms  302 , 304  included in the determined search results. In other examples, a text mining operation may be performed prior to receiving an indication of a request for feedback output associated with a topic-of-interest, and keywords may be extracted from and stored in association with the feedback record  118  from which they are extracted in the aggregated data store  218 . In various aspects, the classifier  210  is further operative or configured to normalize the discovered terms  302 , 304  to their associated subtopics-of-interest  306  terms, and provide the search results comprising feedback records  118  that match the topic-of-interest and the subtopics-of-interest  306  to the visualization engine  212  for presentation to a user  140 . 
     The visualization engine  212  is operative or configured to provide the user interface  138  with which a user  140  interacts and via which feedback output is presented to the user  140 . An example user interface is illustrated in  FIGS. 4A and 4B . With reference to  FIG. 4A , an example user interface  138  is shown displayed on a display  402  of a computing device  126   c  of a user  140 . The computing device  126   c  may execute a front-end application  404 , which can be implemented as a thick client application, which may be stored locally on the computing device  126   c  or implemented as a thin client application (i.e., web application) that may reside on a remote server  202  and be accessible over a network  214  or combination of networks (e.g., the Internet, wide area networks, local area networks, software defined networks). The front-end application  404  is configured to communicate with the feedback system  124 . The computing device  126   c  may be one of various types of computing devices (e.g., a server device, a desktop computer, a tablet computing device, a mobile device, a laptop computer, a laptop/tablet hybrid computing device, a large screen multi-touch display, or other type of computing system) configured to execute instructions for performing a variety of tasks. 
     According to an aspect, the visualization engine  212  is configured to provide, in the user interface  138 , one or more search fields  406   a - f  (generally  406 ) via which the user  140  can enter or select one or more search criteria/terms  410 . For example, an entered or selected search term  410  may be received by the front-end application  404  and communicated to the feedback system  124 , where it is received as an indication of a request for feedback output associated with the search term  410 . According to some aspects, the search term  410  may be related to a particular issue or topic, a particular brand name, a particular item or service, a particular enterprise location  104 , a region comprised of a plurality of enterprise locations  104 , a particular enterprise location subdivision  108 , or a particular enterprise division  106 , etc., about which the submitted feedback record  118  is concerned. That is, the search term  410  may be identified as the topic-of-interest, and in response to receiving the search term  410 , the classifier  210  may generate a query on the aggregated data store  218  for feedback content related to the search term  410 /topic-of-interest as described above. Although described as a search field  406 , in other examples, the search term  410  can be received via alternate user interface methods (e.g., selection of a displayed topic-of-interest, voice input). Further, as described above, the classifier  210  may perform a text mining operation to discover terms  302 , 304  included in the query/search results based on the synonyms list  208 , extract and normalize the discovered terms  302 , 304  to their associated subtopics-of-interest  306  terms (based on the synonyms list  208 ), and provide the query/search results comprising feedback records  118  that match the topic-of-interest and the subtopics-of-interest  306  to the visualization engine  212 . 
     According to various aspects, the visualization engine  212  is further operative or configured to receive, from the classifier  210 , the query/search results comprising feedback records  118  that are determined to be related to the topic-of-interest and the associated subtopics-of-interest  306 , determine metrics associated with the related feedback records  118 , and update the user interface  138 . For example and as illustrated in  FIGS. 4A and 4B , the visualization engine  212  may update the user interface  138  to include a display of one or a combination of: the related feedback records  118 , the metrics  408   a - k  (generally  408 ), and a graphic data visualization  136  of the subtopics-of-interest  306  discovered in the feedback records  118 . In some examples and as illustrated in  FIG. 4B , the data visualization  136  of the subtopics-of-interest  306  is a word cloud. For example, the data visualization  136  may be an image comprised of words, wherein the words are the subtopics-of-interest  306  determined to be associated with the entered or selected search term(s)  410 /topic(s)-of-interest based on the synonyms list  208 , and the display attributes (e.g., size and color/shade intensity) of each word is indicative of its frequency of inclusion (e.g., as the first term  302 , the second term  304 , or the subtopic-of-interest  306  term) in the related feedback records  118 . As can be appreciated, the frequency of inclusion may be an indicator of the importance of a word/subtopic-of-interest  306 . According to an aspect of the present disclosure, utilization of the synonyms list  208  provides a linkage of terms  302 , 304  that may ordinarily be unrelated, which enables the visualization engine  212  to generate an accurate representation of the frequency of inclusion of the terms  302 , 304  and further enables a user  140  of the feedback system  124  to easily identify or detect common issues or issues and solutions across multiple enterprise locations  104  and/or other enterprise divisions  106 . As should be appreciated, the user interface  138  is not limited to the above examples and can include additional and/or alternative information. 
     In example embodiments, the visualization engine  212  may be implemented, in whole or part, using one or more open source data visualization libraries. For example, in some embodiments, the visualization engine  212  may use a data visualization dashboard such as Kibana, an open source visualization tool from Elastic NV of Mountain View, Calif. Other types of data visualization tools may be used as well. 
       FIG. 5  is a flow diagram depicting general stages of an example method  500  for providing flexible feedback aggregation and visualization according to an embodiment. At OPERATION  502 , a synonyms list  208  is generated and maintained (e.g., updated). The synonyms list  208  is comprised of a plurality of terminology groupings  300 , wherein each terminology grouping  300  includes at least a first term  302  and a second term  304 , and the inclusion of the terms  302 , 304  in the terminology grouping  300  causes the terms  302 , 304  to be linked for aggregating feedback records  118  according to the terms  302 , 304 . In some examples, a terminology grouping  300  further includes a subtopic-of-interest  306 , which may be the second term  304  or a normalized term. The synonyms list  208  may be generated manually, based on a set of business rules, using supervised, unsupervised, and/or semi-supervised machine learning methods. The synonyms list  208  may be stored in a data store  222 . 
     At OPERATION  504 , a plurality of feedback records  118  may be received from a plurality of different audiences (i.e., different feedback sources  120 ) via a plurality of different feedback channels  122 . The feedback records  118  may include unstructured text, and due at least in part to the variation of feedback sources  120  and/or the feedback channels  122  used to receive the feedback records  118 , the language/terminology used in the unstructured text may differ although the records may include feedback content that are related. As an be appreciated, utilization of different language/terminology across feedback records  118  can make it difficult to properly aggregate that terminology according to a unified set of categorized content that can easily be displayed based on a selectable subset of the data (e.g., by store or region) to identify common or specific issues encountered by customers  110 , suppliers/vendors  112 , employees  114 , and/or other enterprise affiliates  116  in those areas. 
     At OPERATION  506 , received feedback records  118  may be stored. In some examples, the feedback records  118  include a set of parameters around the unstructured text, and the parameters are stored in association with the unstructured text in the aggregated data store  218 . In some examples, the parameters are stored as metadata tags. The set of parameters may include an identification of a particular enterprise location  104  (e.g., a retail store location), an identification of a particular issue or topic with which the submitted feedback record  118  is concerned (e.g., an out-of-stock item, a delivery issue), and/or an identification of other/additional aspects about a received feedback record  118 . OPERATIONS  502 - 506  may be performed continually in a loop. 
     At OPERATION  508 , an indication of a request for feedback output in association with an entered or selected search term  410 /topic-of-interest is received. For example, a user  140  may utilize a frontend application  404  executing on a computing device  126   c  to enter or select a search term  410  that is related to a topic-of-interest, and the entered or selected search term  410  may be communicated to and received by the feedback system  124 . In some examples, a plurality of search terms  410  or search criteria are entered or selected by the user  140 . For example, the user  140  may wish to view feedback records  118  or metrics  408  about feedback records  118  associated with a particular location, prototype, region, state, brand, etc. 
     At OPERATION  510 , in response to receiving the indication of the quest for feedback output in association with the entered or selected search term(s)  410 /topic-of-interest, a query may be performed for feedback records  118  associated with the entered or selected search term(s)  410 /topic-of-interest. Query results may include a plurality of feedback records  118  that match the search term(s)  410 /topic-of-interest. In some examples, the query results include feedback records  118  that are determined to match based at least in part on the set of parameters stored in association with the unstructured text that identify at least one of: a particular enterprise location  104  (e.g., a retail store location), a particular issue or topic (e.g., an out-of-stock item, a delivery issue), and other/additional aspects about a received feedback record  118 . 
     At OPERATION  512 , a text mining process may be performed to discover, in the feedback record  118  results, differing terminology that may be used to describe a same or similar subtopic-of-interest  306 . For example, when a term  302  is included in both the synonyms list  208  and in a feedback record  118  (included in the query results), the term  302  and any additional instances of the term  302  or its linked term  304  in the feedback record  118  results may be may be linked and normalized (OPERATION  514 ) to its associated subtopic-of-interest  306  term. 
     At OPERATION  516 , various metrics are determined about the feedback records  118  that are determined to match the entered or selected search term(s)  410 /topic-of-interest and the linked subtopics-of-interest  306  discovered in the feedback records  118 . According to one example, an overall frequency of inclusion of a grouped term  302 , 304  or associated subtopic-of-interest  306  in the feedback records  118  may be determined. 
     At OPERATION  518 , the user interface  138  displayed to the user  140  may be updated to include a display of one or a combination of: the related feedback records  118 , the metrics  408 , and a graphic data visualization  136  of the subtopics-of-interest  306  discovered in the feedback records  118 . In some examples, the data visualization  136  is a word cloud comprised of the subtopics-of-interest  306  determined to be associated with the entered or selected search term  410 /topic-of-interest based on the synonyms list  208 , and the size and color/shade intensity of each word may be indicative of its frequency of inclusion in the feedback records  118 . In some examples, the user  140  may be further enabled to select a subtopic-of-interest  306  included in the data visualization  136 , wherein in response to the selection, the feedback records  118  that include the selected subtopic-of-interest  306  may be included in the user interface  138  for display to the user  140 . Accordingly, the user  140  is enabled to accurately identify and drill down into common or specific issues encountered by its customers  110 , employees  114 , and/or other enterprise affiliates  116  for specific enterprise locations  104 , regions, or other enterprise division  106 . 
     Referring now to  FIG. 6 , an example block diagram of a computing system  620  is shown that is useable to implement aspects of the feedback system  124  of  FIGS. 1 and 2 . In the embodiment shown, the computing system  620  includes at least one central processing unit (“CPU”)  602 , a system memory  608 , and a system bus  632  that couples the system memory  608  to the CPU  602 . The system memory  608  includes a random access memory (“RAM”)  610  and a read-only memory (“ROM”)  612 . A basic input/output system that contains the basic routines that help to transfer information between elements within the computing system  620 , such as during startup, is stored in the ROM  612 . The computing system  620  further includes a mass storage device  614 . The mass storage device  614  is able to store software instructions and data. 
     The mass storage device  614  is connected to the CPU  602  through a mass storage controller (not shown) connected to the system bus  632 . The mass storage device  614  and its associated computer-readable storage media provide non-volatile, non-transitory data storage for the computing system  620 . Although the description of computer-readable storage media contained herein refers to a mass storage device, such as a hard disk or solid state disk, it should be appreciated by those skilled in the art that computer-readable data storage media can include any available tangible, physical device or article of manufacture from which the CPU  602  can read data and/or instructions. In certain embodiments, the computer-readable storage media comprises entirely non-transitory media. 
     Computer-readable storage media include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable software instructions, data structures, program modules or other data. Example types of computer-readable data storage media include, but are not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROMs, digital versatile discs (“DVDs”), other optical storage media, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computing system  620 . 
     According to various embodiments of the invention, the computing system  620  may operate in a networked environment using logical connections to remote network devices through a network  622 , such as a wireless network, the Internet, or another type of network. The computing system  620  may connect to the network  622  through a network interface unit  604  connected to the system bus  632 . It should be appreciated that the network interface unit  604  may also be utilized to connect to other types of networks and remote computing systems. The computing system  620  also includes an input/output controller  606  for receiving and processing input from a number of other devices, including a touch user interface display screen, or another type of input device. Similarly, the input/output controller  606  may provide output to a touch user interface display screen or other type of output device. 
     As mentioned briefly above, the mass storage device  614  and the RAM  610  of the computing system  620  can store software instructions and data. The software instructions include an operating system  618  suitable for controlling the operation of the computing system  620 . The mass storage device  614  and/or the RAM  610  also store software instructions, that when executed by the CPU  602 , cause the computing system  620  to provide the functionality discussed in this document. For example, the mass storage device  614  and/or the RAM  610  can store software instructions that, when executed by the CPU  602 , cause the computing system  620  to receive and analyze inventory and demand data. 
     Referring to  FIGS. 1-6  generally, it is noted that the methods and systems have a number of advantages in terms of providing flexible feedback aggregation and visualization. For example, utilization of the synonyms list  208  provides a linkage of terms  302 , 304  that may ordinarily be unrelated, which enables the visualization engine  212  to generate an accurate visual representation of the frequency of inclusion of subtopics-of-interest associated with those terms included in feedback records  118  collected from across the operational spectrum of the enterprise  102 . The visual representation (i.e., data visualization  136 ) enables a user  140  of the feedback system  124  to easily identify or detect common issues or issues and solutions across multiple enterprise locations  104  and/or other enterprise divisions  106 , despite feedback being collected from a variety of disparate entities and via a variety of heterogeneous communication mechanisms. 
     Embodiments of the present invention, for example, are described above with reference to block diagrams and/or operational illustrations of methods, systems, and computer program products according to embodiments of the invention. The functions/acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. 
     The description and illustration of one or more embodiments provided in this application are not intended to limit or restrict the scope of the invention as claimed in any way. The embodiments, examples, and details provided in this application are considered sufficient to convey possession and enable others to make and use the best mode of claimed invention. The claimed invention should not be construed as being limited to any embodiment, example, or detail provided in this application. Regardless of whether shown and described in combination or separately, the various features (both structural and methodological) are intended to be selectively included or omitted to produce an embodiment with a particular set of features. Having been provided with the description and illustration of the present application, one skilled in the art may envision variations, modifications, and alternate embodiments falling within the spirit of the broader aspects of the claimed invention and the general inventive concept embodied in this application that do not depart from the broader scope.