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Text Analytics Command Center - Vendor Briefing
The document discusses the challenges and opportunities associated with real-time text analytics for both social and private data. It emphasizes the need for a unified approach to analyze large volumes of unstructured data using technologies like natural language processing and semantic analysis. Collective Intellect offers a Text Analytics Command Center to streamline this analysis, providing valuable insights for businesses in various operational areas.
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Text Analytics Command Center - Vendor Briefing
- 1. REAL TIME TEXTANALTYICSVendor BriefingSeptember 7, 2011Proprietary and confidential. Not to be used or reproduced without the consent of Collective Intellect, Inc.
- 2. Making Sense ofSocial & Private Data DelugeHow does an organization unify social & private text analytics?Proprietary and confidential. Not to be used or reproduced without the consent of Collective Intellect, Inc.
- 3. Making Sense ofSocial & Private Data DelugeBig data presents both unique challenges & potential business insightsShifting categorization or theme patterns
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- 6. Processing large volumesof data quickly
- 7. Real-time, unsolicited, voiceof customer or employees
- 8. Weak signal canbe sign of emerging trend or issuesProprietary and confidential. Not to be used or reproduced without the consent of Collective Intellect, Inc.
- 9. Building a Platformfor Handling Social & Private - A Text Analytics Command Center(TACC)Proprietary and confidential. Not to be used or reproduced without the consent of Collective Intellect, Inc.
- 10. Social Media CharacteristicsChangingthe way consumers interact with brands, products & servicesVolume. The number of consumers adopting and using social media platforms continues to grow
- 11. Immediate. Real-time, unsolicited,true voice-of-customer
- 12. Brand and productmentions but they may be embedded with other themes, topics and interests
- 13. Niche but expanding.May represent only a portion of a business’ total consumer audience
- 14. Narrow S-CRM focus.Need is expanding beyond Marcom/PR to loyalty, customer service, product development. Proprietary and confidential. Not to be used or reproduced without the consent of Collective Intellect, Inc.
- 15. Analyzing Social MediaAnumber of technologies use keyword and Boolean expressionsPros
- 16. Inexpensive and easyto set up
- 17. Quick results for“exact” terms and phrase matching
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- 19. Inefficient at white-spaceor open-ended analysis
- 20. Become more brittleas each expression is added to include or exclude data
- 21. Problems with ambiguityand granular filteringProprietary and confidential. Not to be used or reproduced without the consent of Collective Intellect, Inc.
Editor's Notes
- #3 A white paper released today from IDC revised the research firm's earlier estimates to show that by 2011, the amount of electronic data created and stored will grow to 10 times the 180 exabytes that existed in 2006, reflecting a compound annual growth rate of almost 60%.There is lots of data out there much of it unstructured, which may contain enormous business insights. Much of this unstructured content is from social media conversations or if it is internal data, it might be surveys, chat or video transcripts or email threads. How do you address the analytical requirements of both social/private data? How do you begin to unify and integrate the analysis and research?
- #4 "Over the last 20 to 25 years, companies have been focused on leveraging maybe up to 5% of the information available to them," said Brian Hopkins, a principal analyst at Forrester Research Inc. in Cambridge, Mass. "Everything we didn't know what to do with hit the floor and fell through the cracks. In order to compete well, companies are looking to dip into the rest of the 95% of the data swimming around them that can make them better than anyone else.”http://searchbusinessanalytics.techtarget.com/news/2240039382/Big-data-poses-big-challenges-for-traditional-analytics-approaches
- #6 2011 values - Twitter 75m user accounts, LinkedIn over 50m members & Facebook 350m active users
- #7 If you are wanting to conduct open-ended or white space analysis, Keyword & Boolean is simply unable to derive meaning and context for large data sets.
- #8 Existing systems – organization may have processes or systems in place that may not scale or are unable precise insights
- #10 The command center will serve customers having various social media and private data analytics needs, allowing them to simply use or integrate our data and technology into their business environment serving various departments. The following diagram describes the overall functionalities offered by the command center. CI will own the first three layers (CI Inputs, CI Engine and CI Outputs) and will integrate through partners/client own applications.
- #11 This is not a new way of doing business – delighting your customer with the right message. But never has the customer had such a powerful and amplifying platform to inform you of their opinion and perspective. A successful business engagement requires a two-way conversation with the customer. Without collecting and understanding your consumer’s input and responses, you are missing out on valuable and increasingly critical information
- #12 Industry research estimates 127 million people, or 57.5% of internet users visited a social networking site at least once a month in 2010. Not only is the number of users growing quickly, but the audience demographics continue to widen. In 2010, it’s estimated that 59.2% of adult internet users will visit social networks monthly, up from 52.4% in 2009.Research estimates predict a steady rise in social media users by 2014, with 2/3 of all internet users, 164.9 million people, visiting social network sites on a regular basis. Ideally, your listening tool is able to manage both unstructured social data but also private, internal data. Otherwise you are analyzing data in a vacuum.
- #13 -LSA in particular is the "secret sauce. It is an evolving system versus an analysis of word groupings at a single point in time, this makes it far more flexible/nimble than competitors in the NLP space. It compares 600,000 documents for the meaning of each word results in more accurate analysis and better "listening". The semantic services layer essentially broadens the end market to anyone who needs more accurate search - this includes web search (as long as the user is willing to "wait"), e-discovery, email archiving, and potentially more accurate video search based on descriptions/reviews/tagging, etc.CI’s semantic search and analytics technology is unique with its proprietary approach to how data is handled, categorized and measured for relevancy. The proprietary technologies isolate important attributes from groups of authors and reveal unique considerations and preferences in addition to providing the ability to identify unknown associations occurring through natural online conversation. CI’s technology is used in a compounding fashion, starting with topic categorization, to theme extraction, then to trait extraction.Based on highly precise categorization functionality, once the semantic processing engine has been trained for accurate categorization ongoing analysis becomes repeatable, scalable and reliable.
- #14 Applying semantic technology to large volumes of data LSA in particular is the "secret sauce. It is an evolving system versus an analysis of word groupings at a single point in timefar more flexible/nimble than competitors in the NLP space. It compares 600,000 documents for the meaning of each word results in more accurate analysis and better "listening". LSA is a method for exposing latent contextual-meaning within a large body of text – more relevant terms carry more weight to construct more accurate vectors of how consumers are talking about a category, brand or productAble to apply contextual meaning to topics – select conversations based on meaning Social Search - Categorizing ConversationsThe semantic services layer essentially broadens the end market to anyone who needs more accurate search - this includes web search (as long as the user is willing to "wait"), e-discovery, email archiving, and potentially more accurate video search based on descriptions/reviews/tagging, etc.semantic technology is able to isolate and categorize contentGet all the conversation, not filtered like a google or yahoo searchSemantically Surfaced Author DetailsAssign important attributes to authors or groups of authors and reveal unique considerations and preferencesExamine actual language used to describe the company, brand or productApply traits to posts then average these traits together to produce author profile
- #15 Semantic analysis is able to differentiate between “goldfish” the fish and goldfish the cracker.
- #17 Latent Semantic analysis allows users to perform an advanced form of filtering called dimensions to extract language around pricing, quality, loyalty. Simply cannot be done using keyword
- #20 Dimensions Extract specific language around customer service, pricing or issuesDemographics