What about Analytics in Social Media monitoring?

I was speaking to a client the other day.  This company was very excited about tracking its brand using one of the many listening posts out on the market.  As I sat listening to him, I couldn’t help but think that a) it was nice that his company could get its feet wet in social media monitoring using a tool like this and b) that they might be getting a false sense of security because the reality is that these social media tracking tools provide a fairly rudimentary analysis about brand/product mentions, sentiment, and influencers.  For those of you not familiar with listening posts here’s a quick primer.

Listening Post Primer

Listening posts monitor the “chatter” that is occurring on the Internet in blogs, message boards, tweets, etc.  They basically:

  • Aggregate content from across many,  many Internet sources.
  • Track the number of mentions of a topic (brand or some other term) over time and source of mention.
  • Provide users with positive or negative sentiment associated with topic (often you can’t change this, if it is incorrect).
  • Provide some sort of Influencer information.
  • Possibly provide a word cloud that lets you know what other words are associated with your topic.
  • Provide you with the ability to look at the content associated with your topic.

They typically charge by the topic.  Since these listening posts mostly use a search paradigm (with ways to aggregate words into a search topic) they don’t really allow  you to “discover” any information or insight that you may not have been aware of unless you happen to stumble across it while reading posts or put a lot of time into manually mining this information.  Some services allow the user to draw on historical data.  There are more than 100 listening posts on the market.

I certainly don’t want to minimize what these providers are offering.  Organizations that are just starting out analyzing social media will certainly derive huge benefit from these services.  Many are also quite easy to use and the price point is reasonable. My point is that there is more that can be done to derive more useful insight from social media.  More advanced systems typically make use of text analytics software.   Text analytics utilizes techniques that originate in computational linguistics, statistics, and other computer science disciplines to actually analyze the unstructured text.

Adding Text Analytics to the Mix

Although still in the early phases, social media monitoring is moving to social media analysis and understanding as text analytics vendors apply their technology to this problem.  The space is heating up as evidenced by these three recent announcements:

  • Attensity buys Biz 360. The other week, Attensity announced its intention to purchase Biz360, a leading listening post. In April, 2009, Attensity combined with two European companies that focus on semantic business applications to form Attensity Group (was formerly Attensity Corporation).  Attensity has sophisticated technology which makes use of “exhaustive extraction” techniques (as well as nine other techniques) to analyze unstructured data. Its flagship technology automatically extracts facts from parsed text (who did what to whom, when, where, under what conditions) and organizes this information.  With the addition of Biz360 and its earlier acquisitions, the Biz360 listening post will feed all Attensity products.  Additionally, the  Biz360 SaaS platform will be expanded to include deeper semantic capabilities for analysis, sentiment, response and knowledge management utilizing Attensity IP.  This service will be called Attensity 360.  The service will provide listening and deep analysis capabilities.  On top of this, extracted knowledge will be automatically routed to the group in the enterprise that needs the information.  For example, legal insights  about people, places, events, topics, and sentiment will be automatically routed to legal, customer service insights to customer service, and so on. These groups can then act on the information.  Attensity refers to this as the “open enterprise.” The idea is an end-to-end listen-analyze-respond-act process for enterprises to act on the insight they can get from the solution.
  • SAS announces its social media analytics software. SAS purchased text analytics vendor Teragram last year.  In April, SAS announced SAS® Social Media Analytics which, “Analyzes online conversations to drive insight, improve customer interaction, and drive performance.”  The product provides deep unstructured data analysis capabilities around both internal and external sources of information (it has partnerships with external content aggregators, if needed) for brand, media, PR, and customer related information.  SAS has then coupled with this the ability to perform advanced analytics such as predictive forecasting and correlation on this unstructured data.  For example, the SAS product enables companies to forecast number of mentions, given a history of mentions, or to understand whether sentiment during a certain time period was more negative, say than a previous time period.  It also enables users to analyze sentiment at a granular level and to change sentiment (and learn from this), if it is not correct.  It can deal with sentiment in 13 languages and supports 30 languages.
  • Newer social media analysis services such as NetBase are announced. NetBase is currently in limited release of its first consumer insight discovery product called ConsumerBase.  It has eight  patents pending around its deep parsing  and semantic modeling technology.  It combines deep analytics with a content aggregation service and a reporting capability.  The product provides analysis around likes/dislikes, emotions, reasons why, and behaviors.  For example, whereas a listening post might interpret the sentence, “Listerine kills germs because it hurts” as either a negative or neutral statement, the NetBase technology uses a semantic data model to understand not only that this is a positive statement, but also the reason it is positive.

Each of these products and services are slightly different.  For example, Attensity’s approach is to listen, analyze, relate (it to the business), and act (route, respond, reuse) which it calls its LARA methodology.   The SAS solution is part of its broader three Is strategy: Insight- Interaction- Improve.  NetBase is looking to provide an end to end service that helps companies to understand the reason around emotions, behaviors, likes and dislikes.   And, these are not the only game in town. Other social media analysis services announced in the last year (or earlier) include those from other text analytics vendors such as IBM, Clarabridge, and Lexalytics. And, to be fair, some of the listening posts are beginning to put this capability into their services.

This market is still in its early adoption phase, as companies try to put plans together around social media, including utilizing it for their own marketing purposes as well as analyzing it for reasons including and beyond marketing. It will be extremely important for users to determine what their needs and price points are and plan accordingly.

Syndicating Text Analytics

Over the past several weeks, I’ve been briefed by a number of text analytics vendors and companies in partnership with text analytics vendors about syndicated services that make use of text analytics. Of course, syndicated services such as brand monitoring and news services that make use this technology to some degree have been around for a while. But, how about some of the newer services?

 illumin8

An interesting example of this is illumin8, which is being offered by Elsevier, in partnership with Netbase. The service is targeted at R&D knowledge workers looking to solve technical and business problems. According to Elsevier, knowledge workers spend more time per week trying to discover relevant content relating to a particular problem area than analyzing that information (5.5 hours/week accessing vs. 4.7 hours/week analyzing). These workers are usually using a google-like search engine. I think everyone can agree that the google-like search engine is not ideal for research purposes, so I won’t belabor the point here. In the case of the R&D knowledge worker, often one goal is to gather information relating to a particular problem, finding products that solve that problem, as well as understanding the approach used to solve the problem.

Elsevier has aggregated 5 billion business sources, 3 million full text articles, 33 million scientific records, and 21 million patents as the source of information for this service. Using the Netbase semantic index, Elsevier crawls through the information and extracts solutions that solve a problem and the approaches used to address a specific issue. In this way, R&D can help answer the following questions:

  • Solutions that exist to solve a problem
  • New applications and processes that might exist to help solve a problem
  • Information about what competitors are doing in the particular problem space
  • What the experts are saying about a particular problem area

Here is a screen shot of what an end-user might see using this service. In this example, the user is interested in solving the problem of fuel efficiency in boats. He or she wants to see what products and approaches are out on the market to address this problem and what companies are providing these solutions. The user enters the topic (boats) and the benefit (fuel efficiency) in the search box and gets back information that is organized in a logical way. In this example, you can see that query returns information about products that address the problem as well as the companies that make the products, organizations that deal with energy, as well approaches to solving the problem (drag, stroke, etc). These are ranked. Users can then drill down on any of these areas to get snippets (and full text) associated with areas that he/she is interested in analyzing.

During the demo, I asked to see what would happen if we input “text analytics” as the problem space in the search box. I was actually impressed that what was returned was a good set of information about the players, organizations dealing with text analytics and other information about it. The service is not inexpensive, but it does cull a lot of information.

Syndicated Services

I believe that the number of syndicated services using text analytics will continue to grow. We’re certainly seeing action in the brand monitoring space on this front. Vendors are also getting into the act. Expert System, for example, has its own service that is targeted at the auto industry. I believe that other vendors may get into the act if they determine that the financial benefits of offering syndicated services (as opposed to SaaS offerings) makes sense.

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