When Telecom customers complain-Pt. 2

December 14, 2008
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On the previous post I explained the first steps in deploying Information Extraction, Text Mining and Computational Linguistics to capture the essence of Telecom customers complaints.
We have already discussed about the big picture: Retrieve data (essentially user messages from forums) and then use Information Extraction to transform unstructured information to a structured form. This transformation is done by building a set of matching rules for

On the previous post I explained the first steps in deploying Information Extraction, Text Mining and Computational Linguistics to capture the essence of Telecom customers complaints.
We have already discussed about the big picture: Retrieve data (essentially user messages from forums) and then use Information Extraction to transform unstructured information to a structured form. This transformation is done by building a set of matching rules for specific phrases or keywords, such as
-signal
-antenna
-customer care
and words of sentiment such as
-worse
-worst
-better
-best
-outraged
among many others.
So here comes the interesting part: Suppose a telecom company has in its possession an application that is able to search and extract sentiment from unstructured information. Having such a tool means that :
  • A user can query directly on user forums — for example, specific network problems — and break down those problems by area name.
  • A user can directly query for hot phrases such as “canceling my subscription” and cluster keywords around those messages. If the telecom company is also running (and most likely it is running) churn prediction models, then analysts have yet another source to cross-check and/or enhance the conclusions of their churning models with this new information.
  • Special matching rules can be applied to extract why users prefer company XYZ over company ABC.
  • This technology can be applied to e-mails and/or free text complaints to the customer care center, which means that analysts can further enhance their churning models with additional data.
  • Matching rules can be built that associate keywords to Telecom companies in terms of their co-occurrence. So telecom company XYZ has the phrase “good signal” associated with its brand whilst company ABC has the phrase “bargain” as the associated keyword.
  • Match billing plan keywords and then cluster them with sentiment keywords. In other words, how do customers perceived the new billing plan and what is the sentiment about it?
It is easy to realize that Information Extraction combined with Text Mining and linguistics is a powerful combination that can extract many “knowledge nuggets”. The fact that such an application cannot be 100% accurate may arise acceptance problems but its sure worth the effort in the end if potential problems are clearly presented before implementation of this application.
Let us not forget that a complaint given by a customer to the customer center remains there – between the boundaries of the company. A complaint posted on a forum can be seen by hundreds of thousands of others (and it will most likely stay there for a long time), influencing potential and existing customers in a non-positive way.
A Sentiment mining application may be also used for:
  • Banking
  • Pharmaceuticals
  • Insurance
  • Consumer Products (Customer Reviews)
…and of course for capturing the sentiment of citizens for politicians.

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