CUSTOMER-SENTIMENT DRIVEN WORKFLOW BASED ON SOCIAL MEDIA DATA

A system for leveraging an Application Programming Interface (API) feed to improve the accuracy of a sentiment analysis performed on a customer support request is provided. The system includes a receiver that receives a plurality of logins to the API feed. Each of the logins initiates a customer support request. The customer support request includes a date of the customer support request, a time of the customer support request, a location of computer that was used to generate the customer support request, a username associated with the customer support request and a message. The system also includes a processor that harvests from the API feed a first and second plurality of artifacts. Each artifact includes sentiment information relevant to the customer support request. The processor calculates a sentiment value, based, at least in part, on the first plurality of artifacts, the second plurality of artifacts, the message, and historical information associated with the user. The processor is further configured to route the customer request based on at least the message, the sentiment value, and one of the date, time, username and the location.

FIELD OF TECHNOLOGY

This disclosure relates to processing customer support requests.

BACKGROUND OF THE DISCLOSURE

The disclosure is directed to receipt and processing of workflow associated with customer support requests. Often these customer support requests include information. This information typically provides indication(s) where, within an entity, to direct the customer support requests based on the indications.

Processing customer support requests appropriately can impact customer churn, company reputation, and sales revenue. A major aspect of processing customer support requests relates to routing customer support requests. Determining the optimal way, or at least a more efficient way, to route customer support requests can be difficult. Contributing to the difficulty of determining the optimal way, or at least a more efficient way, to route customer support requests is the high level of complexity associated with determining the customer's emotional state and the customer's needs attributable, at least in part, to his emotional state.

For example, routing a call from an irate customer to an automated response system may potentially further deteriorate the customer experience. Moreover, routing a call from an irate customer to an automated response system may potentially further deteriorate the customer relationship in general.

Conversely, a contented customer with a simple question may not want to be routed to a hold queue for a live operator conversation. In the case of a contented customer, directing the contented customer to an automated system or FAQ may often be appropriate.

In addition, public-facing industries incorporate trust as part of their value-add for product offerings. Thus, appropriately routing customer support requests directed to a public-facing institution often has a large impact on the reputational health of public-facing institutions.

Yet, available information directly associated with the workflow is often insufficient to completely accurately process the workflow associated with customer support requests. Accordingly, it would be desirable to increase the accuracy of the processing of workflow associated with customer support requests.

SUMMARY OF THE DISCLOSURE

A method is provided. The method leverages an Application Programming Interface (API) feed to improve the accuracy of a sentiment analysis performed on a customer support request. The method includes receiving a plurality of logins to the API feed. Each of the logins initiates a customer support request associated with the API feed. The customer support request includes a date of the customer support request, a time of the customer support request, a location of computer that was used to generate the customer support request, a username associated with the customer support request and a message.

The method further includes harvesting from the API feed, for each customer support request, a plurality of artifacts. Each of the plurality of artifacts includes sentiment information relevant to the customer support request.

The method also includes calculating for each customer support request a sentiment value. The calculating is based, at least in part, on the plurality of artifacts, the message, and historical information associated with the user. Finally, the method includes routing the customer support request based on the message, the sentiment value and at least one of the date, time, username and the location.

DETAILED DESCRIPTION OF THE DISCLOSURE

Sentiment Analysis

The following discussion relates to obtaining customer-sentiment information from third party sources. These third party sources may or may not be directly related to customer support requests.

Individuals, groups and/or entities typically generate and receive private or public messages. Each of the messages typically includes some level of sentiment. Such sentiment may be used to analyze the messages to efficiently process the messages. Moreover, shifts in the sentiment from positive to negative and negative to positive, can be analyzed to help mitigate the effects of such shifts and/or to augment the benefits coincident with such shifts.

Since the advent of the digital world, the internet has provided and continues to provide a source of opinion-based information. This information may be culled from a variety of internet channels in which an entity may voice an opinion. Such internet channels may include blogs, emails, social media, chats, text messaging, message services or any other suitable opinion-voicing channel. Because of the ease associated with providing opinions, testimonials and comments on the internet, there has been a proliferation of written opinions available regarding a wide variety of topics.

Opinion-based information is used by various industries for a variety of purposes. Opinions may be used to understand the public's attitude towards a product, company or relationship. Public discourse in online sources, such as social media, may be correlated with the occurrence of real-world behavior.

It would be desirable to analyze the sentiment of publicly available consumer identifiable data to provide indicators to inform a system how to more efficiently process customer support requests.

It would be further desirable to analyze the sentiment of publicly available consumer-identifiable data to detect and then remediate difficulty or augment efficiency in communications associated with customer support requests.

Support requests, and historic communications-related thereto, in the form of email, Instant Messaging Service (IMS), phone calls, video chats, Twitter communications such as Tweets™, and other elements (e.g., response time, escalations, etc.) may be analyzed to define the sentiment of the interactions of an individual, group and/or entity towards one or more individuals, groups and/or entities and to provide a current snapshot thereof. Furthermore, these items may be used to analyze the sentiment associated with a customer as it relates to a specific customer support request.

In the current disclosure, public or semi-public information—semi-public referring to, for the purposes of this application, protected information accessible using a password or similar access code or device—can be leveraged. For example, social media information or data can be leveraged to better tune the routing and processing current or future customer support requests.

In certain embodiments, social media data may be retrieved using publically-available APIs (“Application Programming Interfaces”) such as the Twitter™ API. This data may then be parsed and transformed into structured data which is then stored in a database. For the purposes of this disclosure, at least the following data points may be tracked: date, time, location username and message.

Once social media data has been retrieved, it can be parsed for sentiment analysis utilizing any number of libraries such as the Natural Language Toolkit Sentiment Library. The resulting sentiment score may be stored in a column in a table (see below, sentiment mapping table106inFIG. 1) tied to the relevant record.

Topic Analysis

The foregoing has been devoted, primarily, to using sentiment analysis to improve the accuracy and efficiency of responding to customer support requests. In addition, topic analysis, as described below, may also be utilized, preferably in combination with sentiment analysis but also, at times by itself, to improve the accuracy and efficiency of responding to customer support requests.

For example, if the sentiment associated with a user has been determined to be happy (sentiment analysis) and the user is asking questions regarding financial instruments (topic analysis), it could be beneficial to route that customer to a new financial advisor so the new financial advisor could build up their client book with a happy user.

Conversely, if the customer is unhappy (sentiment analysis) and the customer is asking questions about financial instruments (topic analysis) it may be beneficial to route the unhappy customer to a financial advisor or portfolio manager with many years of experience. Sending an unhappy customer to a new financial advisor could further frustrate the customer.

The foregoing was one illustration of mining a combination of sentiment analysis—i.e., the sentiment state of the user—and topic analysis—i.e., the direct subject matter towards which the customer was directing his or her customer support request. It should be noted that the topic analysis could be used for many different type of topics, but that such information could preferably be mined from the customer support request using such utilities as the aforementioned libraries including, but not limited to, the Natural Language Toolkit library.

Figures

The steps of methods may be performed in an order other than the order shown or described herein. Embodiments may omit steps shown or described in connection with illustrative methods. Embodiments may include steps that are neither shown nor described in connection with illustrative methods.

Apparatus may omit features shown or described in connection with illustrative apparatus. Embodiments may include features that are neither shown nor described in connection with the illustrative apparatus. Features of illustrative apparatus may be combined. For example, an illustrative embodiment may include features shown in connection with another illustrative embodiment.

FIG. 1shows a possible database implementation of a system according to the invention. Specifically,FIG. 1shows an entity relationship map100for customer sentiment. While map100relates to customer sentiment, it should be noted that map100could be used to illustrate any suitable relationship sentiment according to the embodiments set forth herein.

Message102(which is in the form of a table which is a database object) contains various attributes relating to the message. The exemplary information included in message table102is a message ID110, source ID112, username114, date/time116, location118, message (text)120, and/or sentiment score122.

Message ID110is the primary key (indicated by a key icon labelled PK) for message table102. As such, message ID110represents the only necessarily unique attribute of message102.

Source104(which is also in the form of a table) provides attributes regarding the source ID. Attributes for source104include source ID126and name128. Source ID126is the primary key for source104. As such, source ID126represents the only necessarily unique attribute of source104.

Sentiment mapping106(which is also in the form of a table) provides attributes regarding the formation and utilization of the sentiment score. Attributes for sentiment mapping106include sentiment mapping ID132, support channel ID134, minimum score136and maximum score137. Sentiment mapping ID132is the primary key for sentiment mapping106. As such, sentiment mapping ID132represents the only necessarily unique attribute of sentiment mapping106.

Table108is a support channel table. Attributes for support channel may include support channel ID140and name142. Support Channel ID140is the primary key for support channel108. As such, support channel ID140represents the only necessarily unique attribute of support channel108.

The tables described above inFIG. 1may be leveraged, in some embodiments, as follows: Message102may preferably include a message, which includes a support request. Source ID104may preferably include lineage information relating to the data in the message. Sentiment mapping106may preferably include information, based at least in part on sentiment, regarding how to appropriately respond to the content as further tuned using sentiment in the message. In some embodiments, the triggering device (not shown) may preferably monitor the information and trigger a sentiment mitigation (to offset a negative sentiment) or sentiment augmentation (to enhance a positive sentiment) response when instructed.

Some embodiments present a hierarchical response (seeFIGS. 11 and 12below, and the portion of the specification corresponding thereto) to represent and/or help guide the routing of the support request based on the entity hierarchy.

Social media data sets are typically extremely large and unstructured. The large size and lack of structure can make social media data sets challenging to analyze and manipulate through traditional methods. It should be noted that a visual interface accordingly to the embodiments simplifies analysis and enables users to more quickly address requests—which may be particularly significant when those requests include negative sentiment. Further—the visual interface can be used to automatically trigger response(s) to such detected sentiment(s) or sentiment trends. Such responses may remediate alert conditions and/or correct sentiment issues preferably simultaneously to the display of such conditions. Such responses may alternatively include augmenting positive results obtained from requests associated with positive sentiment.

Also, in the event that the support requests, and ensuing communications exchanged between a first individual, group or entity and a second individual, group or entity, are less positive than the support requests, and ensuing communications between the first individual, group or entity and a third individual, group or entity, requests between the first individual, group or entity and the second individual, group or entity may be rerouted for response by the third individual, group or entity and not for response by the second individual, group or entity.

FIG. 2shows an illustrative flow diagram showing intake of a customer support request, at202. At step204, the diagram shows reviewing social media artifacts relating to an originator of the support request. Finally, at step206, the diagram shows routing the customer support request based on 1) a localized context and various request parameters associated with the request in combination with 2) the customer sentiment derived from social media artifacts.

FIG. 3shows a more specific rendering of an illustrative flow diagram for a method associated with a customer support request routing system. In the diagram inFIG. 3, an API, such as Twitter™, receives a support request. This is shown at step302.

Step304shows examining historical events on the API feed which received the support request. In addition, embodiments may include examining historical events on an API feed other than the API feed which received the support request. In any case, embodiments teach routing the customer support request based at least in part on historical API feed information, as shown at308.

Some embodiments, as shown at step306, may include examining other social media artifacts to derive (additional) customer sentiment regarding the customer associated with the transmitting of the customer support request. Such customer may be identifiable based on information in the request.

Thereafter, step310shows routing customer support request based on other API feed information and/or relevant social media artifacts. From the foregoing it has been shown that a customer support request may be routed based on information derived from examination of historical API feeds as well as relevant social media history.

FIG. 4shows an illustrative flow diagram which sets forth a general overview of the embodiments of the disclosure. At400, a support request from an individual is received. Mining module, shown at402, may mine a plurality of artifacts, as shown at412. The artifacts may be received in the form of biometrics414, telephone calls416, verbal statements420, conversations422, SMS (“Short message service”)422and phone calls424. The artifacts may be associated with the customer support requests.

Upon retrieval of one or more artifacts by artifact mining module402, sentiment analysis scoring module404may analyze each of the support requests in view of the artifacts retrieved that relate to the support request.

The support requests may be analyzed based on a variety of different scoring models. The variety of different scoring models may include a polarity-based scoring model, a multi-dimensional vector-based scoring model and a two-dimensional scoring model. The different scoring models will be described in greater detail below.

The sentiment analysis scoring module may determine a score for each support request. The score may be a composite score retrieved from numerous scoring models. The score may be a single number score. The score may be a vector.

Upon determination of a score for each of the support requests, a receiving individual, group or entity may be determined for each support request. It should be appreciated that the score determination may be updated periodically, or continuously, after a customer support request transmission.

As described above, a plurality of artifacts may be retrieved in connection with the support request. The plurality of artifacts may be derived, and scored, in connection with a transmitting individual, group and/or entity, as shown at406.

In certain embodiments, the sentiment score derived at406may be added to an aggregate score, as shown at408. Once normalized, the aggregate score may more accurately reflect the sentiment state of the customer requesting the support. Step410shows escalating scores or aggregate scores that are higher than a predetermined score, or higher than a predetermined aggregate score, respectively.

An optimal, or appropriate, receiving designee may be determined based on an algorithmic assessment of a responder to whom the support request should be sent. Thereafter, a communication link for bilateral communication between the transmitter and receiver may be determined.

The communication link may link the transmitting individual, group and/or entity to the receiving receiver. In certain embodiments, a communication link, a receiver, or any other suitable designee may be associated with an aggregated score.

In one approach, the artifact and scores are maintained and the average is completely re-executed each time a new artifact is received.

Scores may range from healthy and balanced support request scores to non-healthy and urgent support request scores. Scores that are greater than a predetermined score may include scores that indicate a support request that may be weighted for a high level of urgency or otherwise weighted.

There may be various response and/or remediation measures that may be implemented to respond to the customer support request and to lower the score in a dynamic fashion, as described in more detail below. The measures may include routing the support request to a live responder, or other similar high-resource type response, as opposed to sending the support request for response by an automated response queue, and/or implementing any other suitable remediation measures.

FIG. 5shows an illustrative communications map. The illustrative communications map may include a variety of individuals, groups and/or entities. The individuals, groups and/or entities shown include entity A (shown at502), entity B (shown at504), C (shown at506), D (shown at508), E (shown at510), F (shown at512), G (shown at514) and H (shown at516).

Individuals, groups and/or entities A, B, C and D are shown as transmitting individuals, groups and/or entities. A, B, C and D may represent support requestors. Individuals, groups and/or entities E, F, G and H are shown as receiving individuals, groups and/or entities. E, F, G and H may represent support request responders.

Each individual, group and/or entity may be in communication with one or more of the other individuals, groups and/or entities. The communications may be conducted over communication lines. The communication lines may be virtual communication lines, wired communication lines, wireless communication lines, communication lines that utilize a network or any other suitable communication lines.

Each communication line shown may connect two or more individuals, groups and/or entities. It should be appreciated that, although the communication lines shown connect A, B, C and D to E, F, G and H, there may be additional communication lines that are not shown. In some embodiments, communication lines may enable communication among requestors A, B, C and D, and and/or responders E, F, G and H.

Each communication line may enable one-way or two-way communications. Communication lines that enable one-way communication may push communications from a first individual, group or entity to a second individual, group or entity. Communication lines that enable two-way communications may push communication from a first individual, group or entity to a second individual, group or entity, and from the second individual, group or entity to the first individual, group or entity. Communication lines that are one-way may be parallel to a second communication line that enables the reverse of the one-way communication line. For example, if a first communication line enables one-way communication from entity A to entity E, a parallel communication line may enable one-way communication from entity E and entity A.

Communication lines shown may include518(A-E),520(A-F),522(A-G),524(A-H),526(B-E),528(B-F),530(B-G),532(B-H),534(C-E),536(C-F),538(C-G),540(C-H),542(D-E),544(D-F),546(D-G) and548(D-H).

FIG. 6shows another illustrative communications map. The communications map may show individual, group or entity E (shown at602), individual, group or entity F (shown at604), individual, group or entity G (shown at606) and individual, group or entity H (shown at608) communicating with individual, group or entity A (shown at610), individual, group or entity B (shown at612), individual, group or entity C (shown at614) and individual, group or entity D (shown at616).

It should be appreciated that, although the communication lines shown connect individuals, groups or entities E, F, G and H to individuals, groups or entities A, B, C and D, there may be additional communication lines that are not shown. In some embodiments, communication lines may enable communication among individuals, groups or entities E, F, G and H, and among individuals, groups or entities A, B, C and D.

Each communication line may enable one-way or two-way communications. Communication lines that enable one-way communication may push communications from a first individual, group or entity to a second individual, group or entity. Communication lines that enable two-way communications may push communication from a first individual, group or entity to a second individual, group or entity, and from the second individual, group or entity to the first individual, group or entity. Communication lines that are one-way may be parallel to a second communication line that enables the reverse of the one-way communication line. For example, if a first communication line enables one-way communication between individual, group or entity A and individual, group or entity E, a parallel communication line may enable one-way communication between individual, group or entity E and individual, group or entity A.

Communication lines shown may include618(E-A),620(E-B),622(E-C),624(E-D),626(F-A),628(F-B),630(F-C),632(F-D),634(G-A),636(G-B),638(G-C),640(G-D),642(H-A),644(H-B),646(H-C) and648(H-D).

FIG. 7shows an illustrative scoring scale. There may be various different methods or scales for scoring support requests to formulate an aggregate score for the support request. For example, a support request may be scored based on its immediate characteristics. In addition, the score for the support request may be influenced by positive or negative sentiment derived from artifacts associated with the requestor.

A support request may be scored based on polar emotions, such as happy or sad. A support request may be scored in a non-polar scale, such as a vector scaling model. A support request may be scored on a collection of multiple sentiment scoring methods or models.

Polarity-based scoring scale702is shown inFIG. 7. In such a scoring scale, each support request is scored on a polar scale using linguistic scoring methodology. Linguistic scoring methodology may utilize various language scoring methods, such as natural language processing, computational linguistics and biometrics. For the purposes of this application, natural language processing should be understood to refer to Natural Language Processing (NLP) is a subfield of linguistics, computer science, information engineering and artificial intelligence concerned with the interactions between computers and human (natural) languages. In particular, NLP refers to how to program computers to process and analyze large amounts of natural language data.

The language scoring methodology may also include text analysis. The text analysis may analyze various components of the text. It should be appreciated that, to a human reader, certain text components, such as sarcasm, exaggerations or jokes may be easily understood. However, a computer may require special methods to ensure that such linguistic terms are not misinterpreted. Therefore, the text analysis may analyze key words and phrases, emoticons, characters, length of response, response time between artifacts, related artifacts, negation, exaggeration, jokes and sarcasm.

Based on the linguistic scoring methodology, each artifact may be scored on a scale of 0% to 100%, as shown at704and706, respectively. 0% may indicate most positive and 100% may indicate most negative, or in the alternative 0% may indicate most negative and 100% may indicate most positive.

It should be appreciated that a polarity-based scale may include two opposite emotions, whether positive and negative, happy and sad or any other suitable opposite emotions. Therefore, each support request scored on a polarity-based score may only be given a sentiment score based on the polarity of the support request. However, at times, in order to compensate for the shortcomings of the polarity-based scoring models, an artifact may be scored on multiple polarity-based scoring models, and, the results of the scoring models may be combined.

FIG. 8shows a multi-dimensional scoring scale. The multi-dimensional scoring scale may include a plurality of vectors. Each of the vectors may correspond to a different emotion or sentiment. The emotions, or sentiments shown, may include positive (802), encouraged (804), satisfied (806), happy (808), calm (810), assurance (812), unintelligent (814), prevented (816), negative (818), aggravated (820), frustrated (822), sad (824), anger (826), fear (828), intelligent (830) and promoted (832).

Vector834may be a vector generated from a support request. The support request may include a plurality of attributes. The support request may be broken down into component parts. The attributes and the component parts may be used to plot the support request on the multi-dimensional scoring scale.

The sentiment of the support request plotted as vector834may be shown in-between intelligent and promoted. It should be appreciated that the multi-dimensional scoring scale may be used to determine the sentiment of a support request—with or without sentiment adjustment associated with retrieved artifacts.

The multi-dimensional scoring scale may include a plurality of other emotions, not shown. In some embodiments, the multi-dimensional scoring scale may utilize any suitable emotion chart.

FIG. 9shows another multi-dimensional scoring scale. The multi-dimensional scoring scale may be three-dimensional. The three-dimensional scoring scale may include an x-dimension (horizontal), a y-dimension (vertical) and a z-dimension (depth; in the two-dimensional representation of the figure, into and out of the plane of the page). Vectors that represent emotions may be plotted on the three-dimensional scoring scale.

A vector may have multiple dimensions, such as an x-dimension, a y-dimension and a z-dimension. As such, a vector may be plotted on the three-dimensional scoring scale that comprises an x-dimension, y-dimension and z-dimension. Each plotted emotion may be represented by a vector, such as vector902that represents emotion1, vector904that represents emotion2, vector906that represents emotion3and vector908that represents emotion4.

Build of a vector, or orientation of a vector, could be based on one or more of a combination of sentiments or emotions. In some embodiments, vector length could correspond to magnitude or intensity of a vector.

Each plotted vector that represents an emotion may have two extremes. For example, a vector may represent a range of happiness and sadness. Each point of the vector may represent a different value in the range of happiness and sadness. At the (0,0,0) point, the vector may represent neutrality—e.g., neither happy nor sad. The further a location points found on the vector is above the (0,0,0) point may represent an increasing degree of happiness over neutrality, while the further a location point found below the (0,0,0) point may represent an increasing degree of sadness over neutrality.

Upon the receipt of a support request, the support request may be broken down into component parts. The component parts may be used to generate a vector. The vector may be plotted on a multi-dimensional scoring scale, such as the one shown in the matrix depicted inFIG. 9. Such a vector may be shown at910. Vector910may represent the sentiment of artifact1. Such an artifact may be retrieved as it relates to a pre-determined customer support request.

Because sentiment of a support request may be multi-faceted—i.e., may include multiple emotions—vector910may represent the sentiment of support request with respect to the emotion vectors.

In some embodiments, the emotion vector, or vectors, that most closely represents the sentiment of the support request may be displayed to the user. In certain embodiments, a detailed score including the various components of the support request may be shown.

FIG. 10shows an exemplary customer support request sentiment analysis report. In the sentiment analysis report, the various parameters that may affect the sentiment analysis used for routing of the customer support request, may be analyzed separately.

The parameters may include support requests in the form of biometrics (1002), verbal sentiments (1004), conversational tone (1006), telephone call parameters (1008) and thread analysis parameters (1010). Exemplary components of the analysis for each of the parameters may be shown at1012(body temperature, bodily movement),1014(voice analysis, syntax, sentence structure),1016(content, relationship to other party),1018(call circumstance, location of other party) and1020(historical statements in thread, other social media thread statements, etc.). It should be appreciated that the analysis shown inFIG. 10may be based on a polarity-based scoring model (as described above in more detail). However, any suitable scoring model may be used to generate an analysis.

Such a sentiment analysis report may be useful in determining which support request is the most urgent. Such an analysis report also preferably takes into account the requester's current state of emotions, as well as the historical context in which the request is being made.

FIG. 11shows an illustrative diagram of a hierarchy1108of an exemplary support center1100according to certain embodiments. Support requester call-in devices are shown at1102. These devices are shown as calling in to a customer support request routing engine1104. Engine1104interfaces between the requesters and center1100.

At1112, escalation option is shown. This escalation option preferably enables a support center employee to escalate a matter to support center middle management. Support center middle management may include one or more customer center managers1114(shown as Manager A, M.A., and Manager B, M.B.).

Executive management shows vice president1117, accessible by escalation option1115, and president1118, accessible by escalation option1116.

Hierarchy1108visually indicates that calls may be routed from call-in devices1102to customer support request routing engine1104. From customer support request routing engine1104calls may be routed to one of the support center employees A-C, or auto-response systems such as auto-response system1106. In certain exceptional situations, calls may be routed from engine1104directly to a manager1114(M.B.).

Engine1104may preferably route the customer support request based on context and request parameters of the request in combination with the customer sentiment derived at least in part from social media artifacts, as shown in detail inFIGS. 2-4, and especially at element206ofFIG. 2.

FIG. 12shows a customer center hierarchy1206similar to the support center hierarchy1106shown inFIG. 11. As depicted, elements1202,1204,1206,1210,1212,1214,1215,1216,1217, and1218are all similar to the corresponding elements1102,1104,1106,1110,1112,1114,1115,1116,1117, and1118shown inFIG. 11.

FIG. 12additionally shows an API feed1230, a parsing engine1232for parsing the API feed and a response system1234. It should be noted that, in some embodiments, API feed1230and parsing engine1232may be included as components of response system1234. In other embodiments, API feed1230and parsing engine1232may be separate from response system1234. Both of the foregoing options—i.e., where API feed1230and parsing engine1232are included as components of response system1234, and where API feed1230and parsing engine1232are separate from response system1234, are within the scope of the current disclosure.

API feed1230preferably acts a conduit to receive support requests in the form of social media communications such as Tweets. Once the support requests have been received, the support requests may be parsed by parsing engine1232for date, time, location, name of requester and message content. Thereafter, response system1234may redirect the support request to either an employee in the support center1210, auto-response system in the support center1206or a manager1214.

Algorithms at use in response system1234may preferably take into account the sentiment score related to the support request, such as sentiment score122set forth inFIG. 1. In some embodiments, response system1234may route the request based on the historical information available in API feed1230associated with the incoming support request. As such, response system1234may query API feed1230

Furthermore, response system1234may also, in certain embodiments, preferably route the customer support request based on context and request parameters of the request in combination with the customer sentiment derived at least in part from social media artifacts, as shown in detail inFIGS. 2-4, and especially at element206ofFIG. 2.

FIG. 13shows, in more detail, an API response system (or other social communications media system). The system shown inFIG. 13shows an exemplary schematic rendering of response system1234shown inFIG. 12.

The system shown inFIG. 13may include, for example, an API feed1310. This may be an internal feed—i.e., within an entity or the system itself—or an external feed that receives support requests.

The system may include a natural language toolkit1308. Toolkit1308may be used for parsing an incoming support request for sentiment analysis.

Once the support request has been parsed, and analyzed for customer sentiment, the support request may be routed to 1) a human operator1302, 2) an auto-response system1314and/or3) a frequently asked questions (FAQ) repository1306.

FIG. 14shows an illustrative flow diagram1400for detecting a negative trend and responding thereto. Illustrative flow diagram1400may utilize a public API or other device to collect artifacts, as shown at1402.

Based on this information, illustrative flow diagram1400may detect a negative sentiment trend, as shown at1404. It should be noted that this detection may occur at the dashboard level, or using a dashboard. In any case, flow1400may exist with or without dashboard utilization.

At step1406, flow1400may include auto-selecting, in response to detection of a negative sentiment trend at1404, one or more trend-mitigating options. Such selection may be based on machine learning (ML) that is based on the success or failure of historical trend mitigating options. Furthermore, such selection can be tuned, as set forth in more detail below with regards to the portion of the specification relating to post trend mitigation feedback1410.

Such trend-mitigating options may include transmission of one or more e-mails (to relevant parties)1412, transmission of one or more electronic-text messages (to relevant parties)1414, transmission of one or more electronically-generated telephone calls (to relevant parties)1416and transmission of one or more electronically-generated chat communications (to relevant parties)1418. Such transmission, with trend-mitigating messaging, may serve to offset other trend-generating stimuli.

Thereafter, flow1400may include invoking trend-mitigating option1406. Following invocation of trend-mitigating option1406, flow1400may include receiving post trend mitigation feedback, as shown at1410. Such feedback1410may be used to select one or more additional trend-mitigating options as shown at1406in an additional round(s) of trend mitigation. It should be noted that ML may be used to select which option should be used to further mitigate. For example, trend-mitigating text-messaging may be invoked when an immediate trend-mitigation response is called for.

Thus, a customer-sentiment driven workflow based on social media data, is provided. Persons skilled in the art will appreciate that the present invention can be practiced by other than the described embodiments, which are presented for purposes of illustration rather than of limitation. The present invention is limited only by the claims that follow.