Wouldn’t it be great if you could analyze all customer interaction and learn which parts of our services or sales are better than others? Or analyze all of our service request textual descriptions and infer the call volume drivers? Understand the main topics of a chat session? Use the same data to understand how the customers are actually using our products? Or to go beyond customer interactions and help us identify the common bugs in our code by analyzing the text engineers type in a bug tracking system such as Jira or Bugzilla?
Liberating your data is not enough if a big chunk of it remains locked in human generated texts.
EMC’s Data Science as a Service team has created a highly-advanced text analytics technology which can help your organization unlock the value in human generated texts.
The first step we ask as data scientists when we approach a new project is what’s the customer’s available data? While some of the time the answer will be a table or file with lots of nice numbers just waiting to be ingested by a machine learning classifier, most of the time a big chunk of the information will be stored in free text columns or documents.
As a customer-facing organization we store information describing EMC’s interaction with clients: some of it structured such as time to close, problem codes, etc.— but also free text fields such as problem summary or comments from the customer satisfaction survey. These free text fields can be used for accurately routing service requests to the right support team to improve resolution times and customer satisfaction, identify burning issues in the customer satisfaction survey or identify emerging problems.
Similarly, Sales would like to use a potential customer’s web site in order to categorize that company’s needs and identify products sold to similar companies.
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