A presentation by W H Inmon ANALYZING CALL CENTER TEXT.

transcript

A presentation byW H Inmon

ANALYZING CALL CENTER TEXT

Lots of companies have call centers

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?

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.

Descriptive conversation

Transportation autom obile m ake Honda Ford Porsche Saturn type SUV sedan sports sta tion wagon airp lane m ake Boeing ..........

textualETL

unstructureddata

taxonomy

relationaldata base

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

textualETL

unstructureddata

taxonomy

relationaldata base

And once you have created a relational database with Textual ETL, you can do your analysiswith visualization

visualization

A dashboard showing what is going on in the call center

textualETL

unstructureddata

taxonomy

relationaldata base

Once you have created your data base, you cananalyze it in any way you want

StatisticalAnalysis

visualization

textualETL

unstructureddata

taxonomy

relationaldata base

Building the relational data base -

Language is complex

So what do you need to do to text to turn it into a formthat can be analyzed?

Proximity analysis

inline contextualization

Taxonomy/ontology resolution

Custom variable formatting

date standardization

Not surprisingly, there are many facets to executingTextual disambiguation

…the Dallas cowboys always play on Thanksgiving…..

Proximity analysis

…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……

Car Honda Ford Volkswagen Porsche Toyota

taxonomy

…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……

Whereas, John Quincy as tenant in common has purchased…..

Beginning delimiter Ending delimiter

Inline contextualization

…remove OL-995-AT from the exhaust manifold…

…remove OL-995-at from the exhaust manifold…

CC-999-cc

Custom variable formatting

…on July 20, 1945 singer Kim Karnes came into this earth…..

Date:19450720

Date standardization

A standardrelationaltable

Doc name bytevalue context

Content of text is the easy part

Context of text is the hard part

And where is text found?

EVERYWHERE!!!

Medical recordsCall centerEmailContracts

WarrantyInsurance claimsHuman resourcesLetters

And many, many more places

+ TextualETL + visualization = Business Value

For more information about Textual disambiguation, see –

www.forestrimtech.com

Now you can unlock thetext that is found in yourcorporation

A presentation by W H Inmon ANALYZING CALL CENTER TEXT.

Documents