This document discusses how call centers can create a data warehouse to store and analyze their data. It outlines the steps to map data sources, clean the data, and export it to a central warehouse. This includes linking data together and creating support tables. Having a data warehouse allows call centers to determine customer patterns, perform data mining and statistics, and gain insights to improve performance. Properly analyzing the historical data in a warehouse can provide future benefits.

1. Growing Intelligence by Properly Storing and
Mining Call Center Data
AGENDA
• Today’s Data Challenge in the Call Center Environment
• The Difference Between Data Storage and Data Warehouse
• Steps to Create a Quality Warehouse
• Data Mapping
• Data Discovery
• Data Cleaning
• Export to Warehouse
• How to Determine Customer Base
• Future Benefits of Having a Warehouse
• Data Mining
• Statistics for Mortals
• Final Thoughts and Questions

2. Data Overflow
Corporate Sales
International
Switch (Avaya)
(Different Systems)
HR Data
Agent Surveys
(PeopleSoft)
Financials
Email (Kana)
(Accounting)
Forecasting &
Workforce
Planning
Management (IEX)
(CenterBridge)
External Data
(Benchmarking)

3. What is a Data Warehouse

4. What is a Data Warehouse
 Centralize all data that can be used for information
in one location.
 Data should be audited.
 Data should allow the same timespan.
 Data should have all calculations finalized or
defined.
 Data should be standardized or have support tables
that allow for standardization.
 It should be scalable.
 It Should address the users’ needs.

5. Steps to Create a Data Warehouse
Corporate Data Mapping
 Close Internal (Own Department)
 Distant Internal (Other Departments)
 Close External (Corporate)
 Distant External (Outsourced or International)
 External – Non-Generated (Benchmarking,
government)
 http://research.stlouisfed.org/fred2/

6. Steps to Create a Data Warehouse
Corporate Data Mapping: Close Internal
• What data sources (database, report from the web, excel)
• Attempt to get data from the first data source (avoid
pulling data from the web, excel spreadsheets etc.).
• What type of data (identification)
• How is it currently used (purpose)
• Who is currently using the data (audience)
• Who is currently owning the data (manager)

7. Steps to Create a Data Warehouse
Owner: John
Johnson,
Telecom,
Omaha
Purpose / Users: Call
IEX or
Definition of Center
CenterBridge
data Management
Stand-alone
or composite

8. Steps to Create a Data Warehouse
Data Mapping /Discovery
• Owner:
• get access to data
• ask questions about format and usage
• Users:
• how do they use the data
• what is missing (important!)
• timeframe needed
• Purpose / Definition:
• type of tables
• understand the fields
• Stand-alone or composite:
• is the data we need in one table, or
• do we need to combine tables to get the result

9. Steps to Create a Data Warehouse
 Data Cleaning
 Purpose of Data
 Weed Out “Waste”
 Determine Unique Links (Database Keys)
 Determine Time Frame
 Determine Calculated Fields
 Can be done at extraction
 Danger is that people may use different
formulas

10. Steps to Create a Data Warehouse
 Create Link (or Support) tables.
 Date
 Skill / VDN / Vector Dictionary
 Create Schema
 Determine Redundant Data
 Keep the table that is easiest to extract
 The table that has a stable extract
 Create Audit Tables

11. Steps to Create a Data Warehouse
Date Link Table Example
txtDate ntxtDate Date Year Month Week WeekDay Period PdWeek OTR
4/1/2012 41000 4/1/2012 2012 4 14 Sunday 4 2 2
4/2/2012 41001 4/2/2012 2012 4 14 Monday 4 2 2
4/3/2012 41002 4/3/2012 2012 4 14 Tuesday 4 2 2
4/4/2012 41003 4/4/2012 2012 4 14 Wednesday 4 2 2
4/5/2012 41004 4/5/2012 2012 4 14 Thursday 4 2 2
4/6/2012 41005 4/6/2012 2012 4 14 Friday 4 2 2
4/7/2012 41006 4/7/2012 2012 4 14 Saturday 4 2 2
4/8/2012 41007 4/8/2012 2012 4 15 Sunday 4 3 2
4/9/2012 41008 4/9/2012 2012 4 15 Monday 4 3 2
4/10/2012 41009 4/10/2012 2012 4 15 Tuesday 4 3 2
4/11/2012 41010 4/11/2012 2012 4 15 Wednesday 4 3 2
4/12/2012 41011 4/12/2012 2012 4 15 Thursday 4 3 2
4/13/2012 41012 4/13/2012 2012 4 15 Friday 4 3 2
4/14/2012 41013 4/14/2012 2012 4 15 Saturday 4 3 2

12. Steps to Create a Data Warehouse
Schema Example

13. Steps to Create a Data Warehouse
Exporting Data to Warehouse
 Server Size / Type:
 Tower (16TB)
 Rack (12 hard drives)
 Blade
 Database:
 SQL Server, Oracle, DB2, PostgreSQL
 Scope:
 Interval
 Daily
 Weekly
 Monthly

14. Benefits of a Data Warehouse
Who Should Have Access
 Traditional Reporting
 Direct Access
 Access via desktop database (ODBC etc.)
 Direct Access to Warehouse
 Interactive Reporting (Web “Cloud”)

15. Benefits of a Data Warehouse
 Consistent Numbers
 Easier to Audit / Problem Fixing.
 Quick Ad Hoc Reporting
 Knowledge of Data Available
 Data Mining

16. Data Mining
 What is it?
 Why do we not use it more often?

17. What Statistics Do (in a nutshell)
• Finding the Probability that Something Will Happen.
• Comparing two (or more) Groups of Data.
• Determines if Movements in one Type of Data Explains
Movement in a Different Data-set.

18. Getting Stats in Excel

19. Getting Stats in Excel

20. Getting Stats in Excel

21. Getting Stats in Excel

22. Getting Stats in Excel

23. Comparing Groups of Data
• Example: Which group of agents perform best?
• 480 agents chosen from sample.
• 160 agents worked up to 1 year
• 160 agents worked from 1 – 4 years.
• 160 agents worked more than 4 years.
• Do these agents perform differently in regards to conversion.
• We can use ANOVA to figure this out.

24. Comparing Groups of Data

25. Comparing Groups of Data
1-4
Agent 1 year years 4+
1 0.28 0.41 0.62
2 0.25 0.38 0.50
3 0.29 0.40 0.50
4 0.37 0.32 0.53
5 0.41 0.42 0.59
6 0.50 0.39 0.54
7 0.43 0.42 0.45
8 0.38 0.42 0.47
9 0.41 0.40 0.48
10 0.47 0.44 0.52
11 0.38 0.32 0.45
12 0.41 0.36 0.48
13 0.38 0.37 0.47

26. Comparing Groups of Data
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
1 year 160 61.82122445 0.386382653 0.003960592
1-4 years 160 76.16293624 0.476018351 0.004419634
4+ 160 81.91744414 0.511984026 0.00356321
ANOVA
Source of Variation SS df MS F P-value F crit
Between Groups 1.338868966 2 0.669434483 168.1512278 5.38904E-56 3.014625576
Within Groups 1.899006344 477 0.003981145
Total 3.23787531 479

27. Types of Regression Analysis
• Simple Linear Regression
• Multiple Regression
• Lagged Regression
• Stepwise Regression
• Logistic Regression

28. Simple Regression
• Example: Does the 2010 call volume explain the 2011 call
volume?
• Simple Regression comparing 2010 with 2011 by week.

29. Simple Regression
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.86
R Square 0.74
Adjusted R Square 0.74
Standard Error 9,790.76
Observations 52
ANOVA
df SS MS F Significance F
Regression 1 13,882,238,604.22 13,882,238,604.22 144.82 0.00
Residual 50 4,792,946,045.53 95,858,920.91
Total 51 18,675,184,649.75
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 53,227.69 10,198.69 5.22 0.00 32,743.02 73,712.37 32,743.02 73,712.37
X Variable 1 0.71 0.06 12.03 0.00 0.59 0.83 0.59 0.83

30. Growing Intelligence by Properly Storing
and Mining Call Center Data
Questions?
Comments?
Geir Rosoy
Manager of Resource Intelligence
geir.rosoy@hyatt.com
402-592-6469