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ANALYZING CALL CENTER TEXT
A presentation by W H Inmon
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Lots of companies have call centers
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But do you know what is being said?
Can you examine 100% of your call center conversations? What is going on in your call centers? What is on our customers mind?
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When you ask a company about their call center, what do they tell you?
- how many calls a day they get - how long their calls are And that is all they know.
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Descriptive conversation
text
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With Textual ETL and visualization now you can easily
taxonomy relational data base textual ETL unstructured data With Textual ETL and visualization now you can easily and quickly capture and analyze ALL your call center conversations Now you can know what your customers are actually saying
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And once you have created a relational data
taxonomy relational data base textual ETL unstructured data And once you have created a relational data base with Textual ETL, you can do your analysis with visualization visualization
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A dashboard showing what is going on in the call center
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Once you have created your data base, you can
taxonomy relational data base textual ETL unstructured data Statistical Analysis visualization Once you have created your data base, you can analyze it in any way you want
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Building the relational data base -
taxonomy relational data base textual ETL unstructured data Building the relational data base -
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Language is complex
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So what do you need to do to text to turn it into a form
that can be analyzed?
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Proximity analysis Custom variable formatting Taxonomy/ontology resolution date standardization inline contextualization Not surprisingly, there are many facets to executing Textual disambiguation
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…the Dallas cowboys always play on Thanksgiving…..
Proximity analysis
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…she drove her Honda past the telephone booth…..
…he walked past the red Volkswagen in a hurry….. …the yellow Porsche ran well ahead of the traffic…… taxonomy Car Honda Ford Volkswagen Porsche Toyota …she drove her Honda/car past the telephone booth….. …he walked past the red Volkswagen/car in a hurry….. …the yellow Porsche/car ran well ahead of the traffic……
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Whereas, John Quincy as tenant in common has purchased…..
owner Whereas, John Quincy as tenant in common has purchased….. Beginning delimiter Ending delimiter Inline contextualization
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…remove OL-995-AT from the exhaust manifold…
CC-999-cc Custom variable formatting
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…on July 20, 1945 singer Kim Karnes came into this earth…..
Date: Date standardization
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A standard relational table
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Doc name value byte context
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Content of text is the easy part
Context of text is the hard part
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And where is text found? EVERYWHERE!!! Medical records Call center Contracts Warranty Insurance claims Human resources Letters And many, many more places
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+ = Textual ETL visualization Business Value
For more information about Textual disambiguation, see –
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Now you can unlock the text that is found in your corporation
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