1. ANALYZING CALL CENTER TEXT
A presentation by
W H Inmon

2. Lots of companies have call centers

3. 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?

4. 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.

5. Descriptive conversation
text

6. 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

7. 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

8. A dashboard showing what is going on in the call center

9. 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

10. Building the relational data base -
taxonomy
relational
data base
textual
ETL
unstructured
data
Building the relational data base -

11. Language is complex

12. So what do you need to do to text to turn it into a form
that can be analyzed?

13. Proximity analysis
Custom variable formatting
Taxonomy/ontology resolution
date standardization
inline contextualization
Not surprisingly, there are many facets to executing
Textual disambiguation

14. …the Dallas cowboys always play on Thanksgiving…..
Proximity analysis

15. …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……

16. 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

17. …remove OL-995-AT from the exhaust manifold…
CC-999-cc
Custom variable formatting

18. …on July 20, 1945 singer Kim Karnes came into this earth…..
Date:
Date standardization

19. A standard
relational
table

20. Doc name
value
byte
context

22. Content of text is the easy part
Context of text is the hard part

23. And where is text found?
EVERYWHERE!!!
Medical records
Call center
Contracts
Warranty
Insurance claims
Human resources
Letters
And many, many more places

24. + = Textual ETL visualization Business Value
For more information about Textual disambiguation, see –

25. Now you can unlock the
text that is found in your
corporation