Hear My Voice!

I’ve been writing a lot about text analytics because I think it is a critical technology for deriving insight from unstructured data.  Late last summer,  Hurwitz & Associates published a report on Text Analytics.  As part of our research, we surveyed companies that had deployed this technology, were planning to deploy the technology, or had no plans to deploy text analytics.  We asked companies planning to implement the technology as well as those that had already deployed it what kinds of applications they had deployed or were considering deploying.   

Voice of the Customer Rules

 The top response was “customer care” applications which include using text analytics to gather information about products, customer sentiment, customer satisfaction, retention and churn, or reputation and brand management. In fact, close to 70% of the respondents cited this application as one they had either already deployed or were planning to deploy in the next year.    

It was no surprise, then when I spoke with Michelle DeHaaff, VP of Marketing at Attensity, that Voice of the Customer (perhaps a broader and better term than customer care) is rapidly becoming a main focus area for the company, and an area where it has gotten a lot of traction.  For those of you who aren’t familiar with Attensity, Attensity’s flagship technology uses what it terms “exhaustive extraction™,” which automatically extracts facts from parsed text (who did what to whom, when, where, under what conditions) and organizes this information. Attensity believes that this technique sets its solutions apart from competitors’ products because it doesn’t require extensive knowledge engineering capabilities; there is no need to develop rules or taxonomies.  

What does this mean for Voice of the Customer applications? Attensity provides software to analyze “traditional” unstructured information such as call center notes, customer emails, and survey responses, as well as unstructured information in blogs and web forums – a rich new source of first person feedback.  Using exhaustive extraction, customer feedback is dissected to analyze sentiment, root cause, and what customers are talking about, in general.  

Voice of the Customer as a Service

 And Attensity just announced a new service directly addressing this market – Attensity Voice of the Customer on Demand – a Software as a Service model that will allow companies to supply Attensity with their unstructured information and get back analysis about the information via a web based application. The fee for the service is based on data source and the size of the data. The service provides:

  • Sentiment, root cause, and a set of analysis and reporting tools to dig deeper and ask more questions about the data.
  • Published “out of the box” reports on customer sentiment, top issues (by product, customer segment, date, region) and root cause.
  • The ability to validate issues discussed by customers online via blogs and web forums with data reported by customers via email into a service center.

Insight from both inside and outside the company

 Think about it.  Companies can now analyze both internal and external sources of unstructured information to gain a better insight about their market.  By tapping into external sources of customer voice, such as blogs and web forums  companies can understand how its competitors are faring as well as how its own brand is holding up. This is exciting stuff. 

Text Analytics Meets Enterprise Content Management

Late last summer, Hurwitz & Associates published a report on Text Analytics.  As part of the report we surveyed companies that had deployed the technology, were planning to deploy the technology, or had no plans to deploy text analytics.  We asked companies planning to deploy text analytics solutions whether they planned to integrate it with their BI solution and whether they planned to implement it with their content management solution.  It turns out that the majority of companies planned to use text analytics with both their BI and content management systems.  In fact, sixty-two percent stated that they plan to deploy the technology in conjunction with their content management systems. 

content-jpeg.jpg 

A few definitions are in order.  Enterprise Content Management (ECM) generally refers to a set of technologies that are used to acquire, manage, store, and serve up content.  Content management software usually has some sort of categorization capability to help classify content information and search to help access the information.  But, what about actually analyzing this unstructured content? That’s where text analytics comes in.  Hurwitz & Associates defines text analytics as the process of analyzing and extracting relevant unstructured text and transforming it into structured information that can then be mined and analyzed in various ways.  Currently, the most popular method of deploying text analytics is as part of a business intelligence solution.  This makes sense – it is a comfortable paradigm.   So, how would deploying text analytics with a content management system work?  Text analytics can be used as part of a content management solution in many ways including: 

·        To help feed the content repository.  Text analytics can help categorize or enrich content.  In life sciences as well as other industries, regulations are mandating that notes, etc. be put in electronic form.

·        To better categorize all documents related to each other.  As a vertical application on top of a repository.  For example, in the area of legal compliance, if one document is tagged as sensitive in a legal case, then it is necessary to find all other docs that relate to this.

·        To better analyze information in the repository. As a means to actually extract information from the content repository and use it for analysis purposes. This might include, for example, extracting information from email complaints, merging it with information found in other systems and using it for analysis.

·        As part of the workflow.  Here, as digital assets are coming into a content repository, information is extracted, merged with other enterprise information and fed it into the workflow process.  For instance, in the email example above, the idea would be to pull information out of the email and merge it with information about the customer, their invoices, etc. found in other systems, and feed this to a customer care agent. The text analytics software can sit on top of the content management repository.  It can access the content via a pre-built connector that acts as the gateway and retrieval of the documents. 

Once there, information can be extracted (terms, facts, etc.)  from the documents and then either stored within the text analytics vendor’s repository or, another data store, or within the content management system itself.   Hurwitz & Associates is seeing a small but growing number of companies looking to implement this type of model.  So far, only a handful of content management vendors are providing this functionality.  Let’s look at some of the top players: 

·        OpenText.  As part of its LiveLink ECM product, OpenText is building text analytics that will be delivered in a series of solutions.  These solutions will link ECM to other enterprise systems and provide algorithms to identify entities, relationships, etc. from the content and applications and use it, for example as part of a workflow process. The infrastructure will be built and in place in the next 12 to 18 months. 

·        IBM. Last summer, IBM announced the integration of its Omnifind Analytics Edition Product, which uses linguistic understanding and trend analysis to allow users to search, mine, and analyze the combined information from their unstructured content and structured data, with ECM solutions including its FileNet products. IBM provides the capability for exploration and mining of enterprise content, as well as services to add analytics-derived content insight to vertical content  applications.

·        EMC Documentum. EMC has a partnership with TEMIS, a provider of text analytics solutions that is particularly strong in the life sciences.  EMC’s Content Intelligence Services, an extension to its content management platform, automates this task by intelligently mining the pertinent information from the document and tagging and classifying the document.

I expect that in the next year, we will finally see some real action in text analytics on the content management front. 

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