Collective Intellect is a social media analytics company founded in 2005 in Boulder, Colorado. Collective Intellect has spent 6 years building a holistic approach to deliver enterprise class text analytics solutions needed for Social Business Collaboration. YourStory caught up with CI to understand the features of this social media tool and dashboard.Collective Intellect’s Listening Platform identifies the right prospects and customers and their relevant conversations to enable the right business function to take measurable action. Our technology uses a semantic technology based on Latent Semantic Analysis (LSA), which is a method for exposing latent contextual-meaning within a large body of text. So, what does this really mean? It means that using Collective Intellect’s CI:Insight you are able to categorize an enormous volume of social media conversations for customer preferences, intent, sentiment, even demographics. Collective Intellect’s software supports a repeatable, consistent and scalable approach to listening, including:
• Semantic Topic Setup and Configuration, which achieves 95%+ categorization accuracy compared to single digits with keyword/boolean search technology for ambiguous terms
• Organic Insight Discovery, to automate insight detection
• Easy Conversion from Insights to Metrics for Repeatable Measurement.
CI’s technology is able to isolate important demographic and psycho-graphic attributes from groups of authors and reveal unique considerations and preferences. By collecting and assigning the traits of the posts back to a given author, you can then average these trait values together to produce an “author profile”.
There are multiple applications for this type of technology:
About Semantic search and real time search
Statistical Language Modeling (SLM) has really revolutionized social media market research. CI semantic filtering uses Latent Semantic Analysis (LSA), which is an advanced form of SLM. This technique extracts specialized language features from a large data set and selects conversations based on their meaning. By isolating the contextual meaning of a topic, semantic filtering minimizes mis-categorizations (false positives) and inappropriate rejections (false negatives) that can otherwise occur when using other techniques and technologies. The resulting data is more relevant and pertinent to a research query.
When using semantic based text analytics, you can start with a simple query, and then let the semantic engine organically cluster similar conversations. By choosing the right conversation clusters, you can quickly get to 95%+ accuracy. The semantic engine can show you conversations grouped by sentiment, key terms your customers/prospects are using in their conversations, and a multitude of other filters to help “pop” insights that help you better understand your customers and prospects.
About the kind of data you can collect and infer from the dashboard
We collect social media conversation from a variety of different platforms, including Twitter, Facebook, message boards and blogs. What makes Collect Intellect unique is that we collect and store this type of data for 12 months so you are able to do historical analysis and derive trends. This means that when you are doing research you have access to millions of social media conversations. You need a powerful language modeling solution to be able to surface customer insights, preferences and even demographics.
We recommend different interfaces for specific audiences:
Some of CI'sclients
Some of our clients include: CBS, Hallmark, iVillage, iconoculture. Our customers include Market Research Firms, Public Relations Firms and the Fortune 1000 that realized the benefits listed on the right because they use Collective Intellect's Social CRM Insight platform.