1. Field of the Invention
Embodiments of the invention relate generally to information handling systems. More specifically, a method and system are disclosed for providing near-real-time competitive insights associated with user interactions within a social media environment.
2. Description of the Related Art
As the value and use of information continues to increase, individuals and businesses seek additional ways to process and store information. One option available to users is information handling systems. An information handling system generally processes, compiles, stores, and/or communicates information or data for business, personal, or other purposes thereby allowing users to take advantage of the value of the information. Because technology and information handling needs and requirements vary between different users or applications, information handling systems may also vary regarding what information is handled, how the information is handled, how much information is processed, stored, or communicated, and how quickly and efficiently the information may be processed, stored, or communicated. The variations in information handling systems allow for information handling systems to be general or configured for a specific user or specific use such as financial transaction processing, airline reservations, enterprise data storage, or global communications. In addition, information handling systems may include a variety of hardware and software components that may be configured to process, store, and communicate information and may include one or more computer systems, data storage systems, and networking systems.
These same information handling systems have been just as instrumental in the rapid adoption of social media into the mainstream of everyday life. Social media commonly refers to the use of web-based technologies for the creation and exchange of user-generated content for social interaction. As such, it currently accounts for approximately 22% of all time spent on the Internet. More recently, various aspects of social media have become an increasingly popular for enabling customer feedback, and by extension, they have likewise evolved into a viable marketing channel for vendors. This new marketing channel, sometimes referred to as “social marketing,” has proven to not only have a higher customer retention rate than traditional marketing channels, but to also provide higher demand generation “lift.”
Traditional methods of measuring the effectiveness of a social media channel include Social Media Analytics (SMA), determining a Net Promoter Score (NPS), and likewise determining a Brand Health Score (BHS). NPS is a customer loyalty metric intended to reduce the complexity of implementation and analysis frequently associated with measures of customer satisfaction with the objective of creating more “Promoters” and fewer “Detractors.” As such, a Net Promoter Score is intended to provide a stable measure of business performance that can be compared across business units and even across industries while increasing interpretability of changes in customer satisfaction trends over time. Currently, several approaches are known for defining, calculating and monitoring a Brand Health Score. In general, these approaches typically include the generation of a score card that comprises a mix of leading and lagging indicators of the health of a brand, whether individually or as part of a brand portfolio.
Such social media scores can also be used to assist executives in developing competitive strategies for products. However, determining whether or not a given product is competitive may not be as simple as it appears. For example, a product marketer may believe that certain features or capabilities of a product may be attractive or compelling to a target market segment, when it fact they are not. As a result, market penetration or take-up goals may not be realized and the product's viability may be at risk. Furthermore, various factors can affect the competitiveness of a given product during its lifecycle. For example, a competing product may offer additional features or capabilities for the same price. Likewise, less capable, yet lower priced products may be more attractive to a majority of customers. Moreover, it is not uncommon for users to express their thoughts and opinions related to various aspects of a product in social media environments. However, known approaches to the generation of social media scores fail to provide actionable sentiment and advocacy information in near-real-time that can be used to determine how competitive a product is.
A method and system are disclosed for providing near-real-time competitive insights associated with user interactions within a social media environment. In various embodiments, a first set of social media data is processed to generate a first set of social network advocacy (SNA) data in near-real-time. The resulting first set of SNA data is then processed to generate a first set of SNA Pulse (SNAP) metric data, which indicates a near-real-time measurement of sentiment and advocacy for a given aspect of a first product. In turn, the first set of SNAP metric data is processed to generate a first set of competitive insight data, which provides a near-real-time measurement of sentiment and advocacy related to one or more aspects of the first product.
In certain embodiments, a second set of social media data is processed to generate a second set of social network advocacy (SNA) data, likewise in near-real-time. The resulting second set of SNA data is then processed to generate a second set of SNAP metric data, which in turn is processed to generate a second set of competitive insight data. In these embodiments, the first and second sets of SNA data are respectively associated with a first and second set of user interactions within a social media environment corresponding to the first and second product. In various embodiments, the first and second sets of competitive insight data are processed to generate a set of competitive insight differential data, which indicates a corresponding improvement or decline in sentiment or advocacy related to the first and second products.
In various embodiments, the first set of competitive insight data is processed to generate a first aggregate competitive insight value, and the second set of competitive insight data is processed to generate a second aggregate competitive insight value. In certain embodiments, first and second sets of competitive insight data are processed to generate an aggregate competitive insight differential value. In various embodiments, the first and second sets of competitive insight data respectively correspond to a set of product aspects that may include one or more of a product's features, capabilities, performance metrics, pricing quality, purchase experience, delivery experience, or associated customer service. In certain embodiments, a predetermined weighting factor is applied to individual members of the first and second sets of competitive insight data to generate a first and second set of weighted competitive insight data.
The present invention may be better understood, and its numerous objects, features and advantages made apparent to those skilled in the art by referencing the accompanying drawings. The use of the same reference number throughout the several figures designates a like or similar element.
a-b is a generalized flowchart showing the performance of competitive insight generation operations in near-real-time.
A method and system is disclosed for providing near-real-time competitive insights associated with user interactions within a social media environment. For purposes of this disclosure, an information handling system may include any instrumentality or aggregate of instrumentalities operable to compute, classify, process, transmit, receive, retrieve, originate, switch, store, display, manifest, detect, record, reproduce, handle, or utilize any form of information, intelligence, or data for business, scientific, control, or other purposes. For example, an information handling system may be a personal computer, a network storage device, or any other suitable device and may vary in size, shape, performance, functionality, and price. The information handling system may include random access memory (RAM), one or more processing resources such as a central processing unit (CPU) or hardware or software control logic, ROM, and/or other types of nonvolatile memory. Additional components of the information handling system may include one or more disk drives, one or more network ports for communicating with external devices as well as various input and output (I/O) devices, such as a keyboard, a mouse, and a video display. The information handling system may also include one or more buses operable to transmit communications between the various hardware components.
In various embodiments, an algorithm is implemented with the SNA system to integrate the contextual influence of user behavior within a social media environment with transactional data, such as purchase of a vendor's product, to generate near-real-time feedback to pro-active marketing responses. As a result, the SNA system provides vendors answers to question such as what was the initial reaction to the product prior to general availability, and how did social media user interactions change after the product was released? It will be appreciated that other marketing-related questions can be answered, such as how the initial marketing efforts were received, especially for an online demand generator (ODG), and who were the primary promoters that drove positive social media conversations and responses. Likewise, the question of what were influencers saying about a product or one of its features can not only be answered, but also with a metric showing the quantifiable affect of their user interactions. Those of skill in the art will recognize that statistically significant changes in net advocacy represent opportunities for changes in pricing, brand health change, and other aspects related to the health of a business.
In various embodiments, an SNA system 118 is implemented to monitor user interactions and generate proactive marketing responses within a social media environment. In certain of these embodiments, the SNA system 118 comprises an SNA categorization module 122 and an SNA category analysis module 124. In these and other embodiments, a social media environment user 216 uses an information handling system 218 to log on to a social media environment, or site, enabled by a social media system 212, which is implemented on a social media server 210. As used herein, an information handling system 218 may comprise a personal computer, a laptop computer, or a tablet computer operable to exchange data between the social media environment user 216 and the social media server 210 over a connection to network 140. The information handling system 218 may also comprise a personal digital assistant (PDA), a mobile telephone, or any other suitable device operable to display a social media and vendor site user interface (UI) 220 and likewise operable to establish a connection with network 140. In various embodiments, the information handling system 218 is likewise operable to establish an on-line session over network 140 with the SNA system, which is implemented on an SNA server 202.
In this embodiment, SNA operations are performed by the SNA system 118 to monitor social media interactions related to a target subject, such as vendor's product. In one embodiment, the social media interactions are monitored and collected by a social media crawler operable to perform crawling operations in a target social media environment. The collected social media interactions are then stored in the SNA data repository 224. If it is determined that an increase in social media traffic related to the target subject is detected, then the social media traffic related to the target subject is processed to determine whether the subject traffic is positive or negative. If it is determined that the subject traffic is negative, then it is processed by the SNA system 118 to prioritize the most negative interactions. The source(s) (e.g., social media environment user 216) of the most negative interactions are identified and they are then displayed in an SNA system user interface (UI) 234 implemented on an SNA administrator system 232. Once displayed, the sources are reviewed by an SNA system administrator 230 to determine the issues causing the negative interactions. Once the issues have been determined, proactive actions are performed by the SNA system administrator 230, or a designated SNA system agent, to address the identified issue(s). Thereafter, the primary source(s) of the subject traffic is contacted by the SNA system administrator 230, or a designated SNA system agent, to gain a better understanding of the issues causing the negative interactions. Additional proactive actions are then performed by the by the SNA system administrator 230, or a designated SNA system agent, while tracking the results of the proactive actions and the relationship with the primary source(s) of the subject traffic.
As an example, a social media participant may read a highly-complimentary review of a product he or she may be considering purchasing during the publicly-expressed sentiment phase 304. As a result of that social media interaction, the social media participant may perform additional product research during the engagement action phase 306. Likewise, if additional product research is positive, such as user reviews of the product, then the social media participant may proceed to the vendor's web site in the subsequent purchase intent phase 308 to obtain additional information about the product. Assuming that the additional product information is appealing, and the social media participant has the means to execute a purchase, then he or she may purchase the product purchase phase 310. Likewise, once the product is received, and if the purchaser is happy with the product, then he or she may write a complimentary review for of the product during the post-purchase experience phase 312 for posting on a social media site.
From the foregoing, it will be apparent to those of skill in the art that a potential purchaser of a product may be either encouraged or dissuaded from purchasing the product based on pro or con sentiments about the product expressed by other members within a social media community. Accordingly, the ability to emphasize (e.g., “showcase”) positive comments, or mitigate the effects of negative comments, may have a direct and measurable effect on sales of a product.
More specifically, a conversation is defined as a set of comments in a thread of user interactions within a social media environment. Each conversation has an author and a topic assigned to it, referenced to a predetermined ontology. In different embodiments, a conversation may originate from within a volume of user interactions, which in turn occur within one or more social media environments. Over time, the conversation may grow as additional users perform additional interactions, which are linked to the thread or related threads. In various embodiments, a conversation is defined as:
Conversation_j={Author_j, Context_j, Thread_j, Relevance_j, Date_j}_j
where:
Context_J={(URL_j, Topic_j, Ontology_Node_j)}
Relevance J=={(SearchEngine_rank_j, Campaign_j)}
Thread_j={(Comment_ji, Author_ji)_ji}_i
Author_i={UserID_i, CommunityID_i}
Comment_ji={“Text”_ij, Date_ij}
CommunityID_i={UserID_i, (DomainID_k, NetworkID_ik)_k}
where each networkID_ik has pairs of UserIDs and the weightage of the link is for the pair. It will be apparent to those of skill in the art that many such embodiments are possible and the foregoing is not intended to limit the spirit, scope, or intent of the invention.
In this embodiment, users of a social media environment 404 conduct conversations as described in greater detail herein. Without the implementation of an SNA system, reactive actions 402 are performed resulting in negative results, whereas with the implementation of an SNA system, proactive actions 422 are performed resulting in positive results. As an example, without the implementation of a SNA system, a user may post 406 a negative comment about a vendor's product in a user forum 408. In response, additional users may respond 410 with their own postings, either requesting additional details or perhaps adding negative comments of their own. Likewise, the negative comments may be collected 412 by a content collector 414 familiar to those of skill in the art. In turn, the collected negative comments, and their web address, may be referenced 416 by another posting by a user in the user forum 408. The collected negative comments may also be sourced 418 by various media agencies resulting in negative mass media exposure 420.
In contrast, with the implementation of an SNA system, a user may post 424 a negative comment about a vendor's product in a personal blog 426. In response, readers of the personal blog 426 may respond 428 with requests for additional details or perhaps adding negative comments of their own. However, since the personal blog 426 is monitored by an SNA system operated by the vendor, then such issues, questions, and negative comments are captured as they are posted and the vendor is notified so they can act proactively. As an example, a representative of the vendor may request additional information about the product issue with a promise to research a solution and provide it to the author of the personal blog. Likewise, the author of the personal blog may broadcast or otherwise provide 430 their posting, directly or indirectly, to one or more additional social media environments 432. In response, users of those additional social media environments 432 may respond 434 with their own questions, responses, or negative comments. However, since the additional social media environments 432 are likewise monitored by an SNA system operated by the vendor, the vendor can act proactively in a like manner as previously described. Through the monitoring and collection 436 of the negative responses, and the resulting proactive activities performed by the vendor, the possibility of negative mass media exposure is mitigated 438.
As likewise shown in
As shown in
The applications subsystem 580, as shown in
In this and other embodiments, data processing operations familiar to those of skill in the art are performed on data extracted from the SNA data repository 224 to generate conversation analysis data 630. As shown in
However, if it is determined in step 706 that an increase in social media traffic related to the target subject is detected, then the social media traffic related to the target subject is processed to determine whether the subject traffic is positive or negative. A determination is then made in step 710 whether the subject traffic is negative. If not, then the process is continued, proceeding with step 724. Otherwise, the subject traffic is processed in step 712 to prioritize the most negative interactions. The source(s) of the most negative interactions are then identified in step 714 and they are then reviewed in step 716 to determine the issues causing the negative interactions. Once the issues have been determined, proactive actions are performed in step 718 to address the identified issue(s). Thereafter, the primary source(s) of the subject traffic is contacted in 720 to gain a better understanding of the issues causing the negative interactions. Additional proactive actions are then performed in step 722 while tracking the results of the proactive actions and the relationship with the primary source(s) of the subject traffic. The process is then continued, proceeding with a making a determination in step 724 whether to continue SNA operations. If so, then the process is continued, proceeding with step 704. Otherwise, SNA operations are ended in step 726.
In turn the SNA system 118 accesses SNA data stored in the SNA data repository 224, which is then used to perform SNA operations likewise described in greater detail herein. The SNA operations result in the generation of measurements of user interactions within various social media environments and vendor sites, which are then processed 926 to generate associated SNA reports 928. In this and other embodiments, an SNA analytics module 930 likewise accesses SNA data stored in the SNA data repository 224, which is then used to perform SNA analysis operations. In various embodiments, natural language processing (NLP) approaches familiar to skilled practitioners of the are used to perform the analysis operations. These analysis operations, in combination with SNA reports 928, result in the generation of SNA analyses 932. In one embodiment, the SNA analyses 932 comprise key performance indicators (KPIs) 934. In turn, the SNA analyses 932, and KPIs 934 if included, are used by a SNA topic categorization module 122 to categorize the SNA data into predetermined SNA categories.
As used herein, an SNA category broadly refers to a class, or grouping, of user interactions within a social media environment that share certain properties or characteristics. As such, an SNA category may variously refer to a geography (e.g., “Southwest region”), a market segment (e.g., “consumer”), a group (e.g., a company's field service technicians), an industry (e.g., “), an object (e.g., a product), a customer, a business function (e.g., customer service), a topic of discussion (e.g., product features and benefits), and so forth. An SNA category may also be a member of a set of SNA categories. As an example, SNA categories “Owning and Using,” “Service,” “Choose a Product,” and “Waiting and Delivery” may all be peer members of an SNA “Customer Journey” group category. Likewise, an SNA category may comprise a set of SNA category subsets. As an example, an SNA “Service” category may comprise “Resolving Query,” “Post Purchase,” “Service Rep,” “Hardware,” “WinX Operating System,” “Rep,” “Guides and Instructions,” and “Software-Service” SNA category subsets. To further the example, the SNA category subsets may be topics related to the SNA “Service” category. Skilled practitioners of the art will recognize that many such examples are possible and the foregoing is not intended to limit the spirit, scope or intent of the invention.
Analysis operations are then performed on the resulting categorized SNA data by an SNA topic statistical analysis module 124. In various embodiments, the statistical analysis operations are performed by the SNA topic statistical analysis module 124 interacting with the SNA analytics module 930. In these and other embodiments, an SNA value is generated for each SNA category. In one embodiment, a plurality of SNA category values is processed to generate an aggregate SNA value for the associated SNA categories. In another embodiment, the aggregate SNA value is a simple average of the plurality of SNA category values. In yet another embodiment, the aggregate SNA value is a weighted average of the plurality of SNA category values. In still another embodiment, statistical operations familiar to those of skill in the art are used to generate the aggregate SNA value from the plurality of SNA category values.
In various embodiments, a first set of SNA category values are processed with a second set of SNA category values to generate a set of SNA category variance values, which in turn respectively correspond to the plurality of SNA categories. In this and other embodiments, the first set of SNA category values are associated with a first time interval and the second set of SNA category values are associated with a second time interval. In these various embodiments, the SNA category variance values respectively correspond to the increase or decrease of each SNA category value over a period of time. In various embodiments, a first aggregate SNA value is processed with a second aggregate SNA value to generate an aggregate SNA variance value.
The SNA topic statistical analysis module 124 then provides the categorized SNA data and associated statistical analyses 942 for display within an SNA system user interface (UI) 234 implemented on an SNA administrator system 232. In various embodiments, the categorized SNA data comprises the first set of SNA category values, the second set of SNA category values, the first aggregate SNA category value, the second aggregate SNA category value, the set of SNA category variance values, and the aggregate SNA category variance value. The categorized SNA data and statistical analyses displayed within the SNA system UI 234 are then used by the SNA system administrator 230 to generate 950 proactive marketing responses within one or more social media environments and the vendor's web site.
In this embodiment, social media conversations corresponding to predetermined topics of interest are monitored in block 1202. The monitored conversations are then analyzed in block 1204 to respectively assess each conversations' the top-level of interest, whether pro, con, or indifferent, and assign a value. In various embodiments, natural language processing (NLP) approaches familiar to skilled practitioners of the art used to perform these analysis operations.
Then, in block 1206, the influence level of each author of a social media conversation is determined and respectively assigned a value. The values generated in blocks 1204 and 1206 are then processed in block 1208 to generate an aggregated real-time SNAP metric. Once generated, the SNAP metric, along with various social media conversation sub-categories that can be monitored, analyzed and acted upon, are presented to the user in block 1210.
As used in reference to this and other embodiments, sentiment 1304 is a measure of a social media participant's positive, negative or neutral opinion of a predetermined aspect of an organization, such as a business. For example, these aspects may include a product's features, capabilities and quality, its associated purchase and delivery experience, or subsequent customer service. It will be appreciated that many such aspects are possible and the foregoing are not intended to limit the spirit, scope or intent of the invention. In various embodiments, sentiment 1304 may be measured at the level of a social media conversation, an individual sentence or statement of a social media conversation, or a topic.
As likewise used herein, gravity 1306 refers to the degree of sentiment 1304 expressed by a social media participant. In various embodiments, gravity 1306 is expressed as a value (e.g., −5 to +5) on a numeric scale. As such, a gravity 1306 value can provide differentiation between social media communications such as, “I like my product. It does the job.” and “I really like my product and would recommend it to others.” As used herein, domain influence 1308 refers to the relative influence of a given social media venue as it relates to a corresponding social media communication, such as a conversation. For example, a comment made on an industry forum would likely have a more significant impact than one made on a personal social media page. Likewise, reach 1310 is used herein to refer to the number and the quality of the followers of the author of a predetermined social media communication.
Relevance 1312 likewise refers to the relevance of the social media communication to the organization, either directly or indirectly. For example, relevance 1312 may determine whether the organization is the primary or secondary topic of the communication. In certain embodiments, relevance 1312 relates to recency, which provides an indication as to whether the communication is related to a recent announcement made by the organization. In one embodiment, relevance 1312 provides an indication of where the author of a social media communication is within a buying cycle of a product. In various embodiments, one or more weighting factors are respectively applied to the values associated with the sentiment 1304, gravity 1306, domain influence 1308, reach 1310, and relevance 1312 factors to generate the SNAP metric 1302. The method of determining the respective weighting factors, and their application, is a matter of design choice and is not intended to limit the spirit, scope or intent of the invention.
As shown in
The ingestion of the aforementioned data is then followed by the filtering and aggregation of product-related and other ingested data 1426 in ongoing process step 1406, which results in filtered data 1430. Then, in ongoing process step 1408, product pricing and other filters 1428 familiar to those of skill in the art are applied to data provided by the repositories of loyalty survey data 1416, technical support call logs 1418, social media profiles 1420, and CRM data 1422 to generate various sets of filtered data. In various embodiments, the filtered data contains product-related information associated with a target product. In certain embodiments, the product-related information contains information associated with one or more competitive products. The resulting filtered and aggregated data is then stored with the filtered data 1430 in a repository of aggregated and filtered data 1434. In various embodiments, the repository of aggregated and filtered data 1434 is part of a data warehouse 1432, familiar to skilled practitioners of the art. In one embodiment, the data warehouse also includes repositories of sentiment and influence data 1444 and SNAP data 1448, which is described in greater detail herein.
Then, in ongoing process step 1410, the filtered and aggregated data 1434 is provided to the SNAP analysis system 1436 for processing to generate sentiment and influence data. In various embodiments, the sentiment and influence data is related to a variety of information associated with a target product, a competitive product, or both. In certain embodiments, the information associated with these products may include data related to product features, capabilities, performance metrics, availability, pricing, shipping, quality or support. In various embodiments, the information associated with these products may likewise include information associated with a product's purchase experience, delivery experience, or associated customer service. Those of skill in the art will recognize that many such examples of product-related data are possible and that the foregoing is not intended to limit the spirit, scope or intent of the invention.
In certain embodiments, the SNAP analysis system 1436 includes various data processors 1442 familiar to skilled practitioners of the art, as well as a sentiment analysis system 1440, and a social media author hub 1438. In various embodiments, the sentiment analysis system 1440 is used to process sentiment and influence data that contains information associated with products. In certain embodiments, the social media author hub 1438 correlates sentiment and other data to various social media authors.
The resulting sentiment and influence data is then stored in the repository of sentiment and influence data 1444. Thereafter, it is provided to a SNAP calculator system 1446, followed by the selection of a target product and one or more product aspects are selected. In one embodiment, the target product and the one or more product aspects are selected by a user. In another embodiment, the target product and the one or more product aspects are selected by an automated process. The method by which the target product and the one or more product aspects are selected is a matter of design choice.
Product-related information contained in the sentiment and influence data is then processed in ongoing process step 1412 to generate competitive insight data. In various embodiments, the SNAP calculator 1446 includes a competitive insight analysis system 1456, which is used to generate the competitive insight data in near-real-time. In turn, the generated competitive insight data is then stored in the data warehouse 1432 in ongoing process step 1518. The resulting SNAP data generated by the SNAP calculator system 14446 is then stored in the repository of SNAP data 1448. Once stored, the competitive insight data is made available for presentation to a user in ongoing process step 1414. In one embodiment, the first and second sets of competitive insight data are processed to generate an aggregate competitive insight differential value.
In various embodiments, the resulting competitive insight data is in the form of one or more SNAP metrics, described in greater detail herein. In certain embodiments, a first set of data acquired from public feeds 1424 from social media environments is processed to generate a first set of competitive insight data and a second set of data acquired from public feeds 1424 from social media environments is processed to generate a second set of competitive insight data. In these embodiments, the first and second sets of competitive insight data are respectively associated with a first and second set of user interactions within a social media environment. In various embodiments, the first and second sets of competitive insight data are then processed to generate a competitive insight differential value, which indicates whether sentiment or advocacy of one or more aspects of a target product has improved or declined.
In various embodiments, a first set of SNAP metric data is processed to generate a first set of competitive insight data corresponding to a first product, and a second set of SNAP metric data is processed to generate a second set of competitive insight data corresponding to a second product. In certain embodiments, the first and second sets of competitive insight data are processed to generate a set of competitive insight differential data. In various embodiments, the first set of competitive insight data is processed to generate a first aggregate competitive insight value and the second set of competitive insight data is processed to generate a second aggregate competitive insight value. In certain embodiments, a predetermined weighting factor is applied to individual members of a first and second set of competitive insight data to generate a first and second set of weighted competitive insight data. In these embodiments, the first and second sets of competitive insight data respectively correspond to a first and second product.
In various embodiments, the resulting SNAP data stored in the repository of SNAP data 1448, the SNAP calculator system 1446, and the competitive insight analysis system 1456 is administered through the implementation of a predetermined SNAP calculator process 1450, likewise in ongoing process step 1412. The method by which the repository of SNAP data 1448, the SNAP calculator system 1446, and the competitive insight analysis system 1456 is administered is a matter of design choice. In various embodiments, the repository of SNAP data 1448, the SNAP calculator administration process 1450, and the competitive insight analysis system 1456 is administered through the use of an administration application 1452 in ongoing process step 1414. Likewise, user interaction with the SNAP system 1400 is provided in ongoing process step 1414 through the implementation of a user front-end 1454. In various embodiments, competitive insight data, and associated competitive insight differential values, are presented to the user through the implementation of the user front-end 1454.
In various embodiments, various requirements associated with a target product are changed through the implementation of a product requirements system 1460. In one embodiment, the product requirements system 1460 is used to manage requirements related to product features, capabilities, performance, availability, pricing, shipping or support. Those of skill in the art will recognize that many such examples of product-related requirements are possible and that the foregoing is not intended to limit the spirit, scope or intent of the invention.
In one embodiment, the product requirements system 1460 is used to manage requirements associated with a target product in various online commerce venues. In another embodiment, the product requirements system 1460 is used to manage requirements associated with a target product in various physical commerce venues. In certain embodiments, the product requirements system 1460 is administered through the user front-end 1454. In various embodiments, the product requirements system 1460 is used to communicate product requirements information to the SNAP calculator system 1446, where it is in turn used by the competitive insight analysis system 1456. The method by which user interaction with the SNAP system 1400 is provided through the user front-end 1454 is a matter of design choice.
a-b is a generalized flowchart showing the performance of operations implemented in accordance with an embodiment of the invention to generate competitive insights in near-real-time. In this embodiment, competitive insight generation operations are begun in step 1502, followed by ingesting raw social media and other related data in ongoing process step 1504. The ingested data is then filtered and aggregated in ongoing process step 1506. In various embodiments, the filtering operations are performed to identify product-related data within the raw data and other related data. The resulting filtered and aggregated data is then stored in a data warehouse in ongoing process step 1508.
Then, in ongoing process step 1510, the filtered and aggregated data is provided to a SNAP analysis system for processing to generate sentiment and influence data. The resulting sentiment and influence data is then stored in the data warehouse in ongoing process step 1512. Once stored, the sentiment and influence data is provided to a SNAP calculator in ongoing process step 1514, followed by the selection of a target product and one or more product aspects in step 1516. Product-related information contained in the sentiment and influence data is then processed in step 1518 to generate competitive insight data corresponding to the selected product and product aspects. In various embodiments, the SNAP calculator includes a competitive analysis analysis system, which is used to analyze the competitive insight data. In turn, the generated competitive insight data is then stored in the data warehouse in ongoing process step 1520. Once stored, the competitive insight data is made available for presentation to a user in ongoing process step 1522.
A determination is then made in step 1524 whether to administer the process by which the SNAP calculator generates the competitive insight data. If so, then the process by which the SNAP calculator generates the competitive insight data is administered in step 1526 and the process is continued, proceeding with step 1524. Otherwise, a determination is made in step 1528 whether to administer the SNAP and competitive insight data stored in the data warehouse. If so, then the SNAP and competitive insight data is administered in step 1530 and the process is continued, proceeding with step 1524. Otherwise, the competitive insight data is processed in step 1532 to assess the need to generate input for product requirements. A determination is then made in step 1534 whether to generate input for product requirements. If so, then input for product requirements is generated in step 1536 and the process is continued, proceeding with step 1524. Otherwise, a determination is made in step 1538 whether to end competitive insight generation operations. If not, then the process is continued, proceeding with step 1516. Otherwise competitive insight generation operations are ended in step 1540.
From the foregoing, it will be appreciated that certain trending indicators, such as Net Promoter Score (NPS), provide an indicator of brand purchase favorability, their accuracy is dependent upon the timing of survey compilation, is limited to existing customers, and is broad-bushed rather than granular. In contrast, SNAP continually assesses the impact of individual social media conversations in near-real-time and monitors how individuals and segments form and change opinions. More specifically, various embodiments of SNAP provide competitive insights, which provide an indication of sentiment and advocacy for various aspects of a target product in near-real-time.
Furthermore, unlike typical implementations of trending indicators such as NPS, SNAP is not restricted to existing customers. Instead, it is a more leading and accurate indicator of purchase intent by both existing and prospective customers, especially as it relates to various aspects of a target product. Furthermore, various implementations of SNAP recognize that not all promoters and demoters are equal. Instead, each asserts varying influence upon brand perception and product favorability. Moreover, certain implementations of SNAP are able to glean the reasons for brand perception and product favorability, or lack thereof, and provide insights for corrective actions. Additionally, various implementations of SNAP are able to process customer transaction data to assist in determining which features and levels of advocacy, including various aspects of a product, have the greatest impact on purchase behavior.
The present invention is well adapted to attain the advantages mentioned as well as others inherent therein. While the present invention has been depicted, described, and is defined by reference to particular embodiments of the invention, such references do not imply a limitation on the invention, and no such limitation is to be inferred. The invention is capable of considerable modification, alteration, and equivalents in form and function, as will occur to those ordinarily skilled in the pertinent arts. The depicted and described embodiments are examples only, and are not exhaustive of the scope of the invention.
For example, the above-discussed embodiments include software modules that perform certain tasks. The software modules discussed herein may include script, batch, or other executable files. The software modules may be stored on a machine-readable or computer-readable storage medium such as a disk drive. Storage devices used for storing software modules in accordance with an embodiment of the invention may be magnetic floppy disks, hard disks, or optical discs such as CD-ROMs or CD-Rs, for example. A storage device used for storing firmware or hardware modules in accordance with an embodiment of the invention may also include a semiconductor-based memory, which may be permanently, removably or remotely coupled to a microprocessor/memory system. Thus, the modules may be stored within a computer system memory to configure the computer system to perform the functions of the module. Other new and various types of computer-readable storage media may be used to store the modules discussed herein. Additionally, those skilled in the art will recognize that the separation of functionality into modules is for illustrative purposes. Alternative embodiments may merge the functionality of multiple modules into a single module or may impose an alternate decomposition of functionality of modules. For example, a software module for calling sub-modules may be decomposed so that each sub-module performs its function and passes control directly to another sub-module. Consequently, the invention is intended to be limited only by the spirit and scope of the appended claims, giving full cognizance to equivalents in all respects.
This application is a continuation-in-part of U.S. patent application Ser. No. 13/683,551, filed Nov. 21, 2011, entitled “Social Net Advocacy for Providing Categorical Analysis of User Generated Content” by inventors Shesha Shah, Rajiv Narang and Munish Gupta, which is a continuation-in-part of U.S. patent application Ser. No. 13/027,607, filed Feb. 5, 2011, entitled “Social Net Advocacy Process and Architecture” by inventors Shesha Shah and Rajiv Narang, both of which are incorporated by reference in their entireties. U.S. patent application Ser. No. ______ (DC-102646.01), filed on even date herewith, entitled “Method And Apparatus To Create A Mash-Up Of Social Media Data. And Business Data To Derive Actionable Insights For The Business” by inventors Shree A. Dandekar and Munish Gupta, which is incorporated by reference in its entirety. U.S. patent application Ser. No. ______ (DC-102647.01), filed on even date herewith, entitled “Method And Apparatus To Calculate Real-Time Customer Satisfaction And Loyalty Metric Using Social Media Analytics” by inventors Munish Gupta, Shree A. Dandekar, Dongxia Chen, Keisha Daruvalla, Brian Melinat, and Guhan Palaniandavan, which is incorporated by reference in its entirety. U.S. patent application Ser. No. ______ (DC-102648.01), filed on even date herewith, entitled “Method And Apparatus To Calculate Social Pricing Index To Determine Product Pricing In Real-Time” by inventors Shree A. Dandekar and Munish Gupta, which is incorporated by reference in its entirety. U.S. patent application Ser. No. 13/027,607, filed on Feb. 15, 2011, entitled “Social Net Advocacy Process and Architecture” by inventors Shesha Shah and Rajiv Narang, which is incorporated by reference in its entirety. U.S. patent application Ser. No. 13/027,651, filed on Feb. 15, 2011, entitled “Social Net Advocacy Business Applications” by inventors Shesha Shah and Rajiv Narang, describes is incorporated by reference in its entirety. U.S. patent application Ser. No. 13/027,682, filed on Feb. 15, 2011, entitled “Social Net Advocacy Measure” by inventors Shesha Shah and Rajiv Narang, which is incorporated by reference in its entirety. U.S. patent application Ser. No. 13/027,738, filed on Feb. 15, 2011, entitled “Social Net Advocacy Contextual Text Analytics” by inventors Shesha Shah and Rajiv Narang, which is incorporated by reference in its entirety.
Number | Date | Country | |
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Parent | 13683551 | Nov 2012 | US |
Child | 14221371 | US | |
Parent | 13027607 | Feb 2011 | US |
Child | 13683551 | US |