growing intelligence by properly storing and mining call center data
DESCRIPTION
Geir Rosoy discusses the importance of moving from just data storage to data warehousing. This presentation includes how Hyatt Hotels & Resorts have created a data mapping system to determine all sources of data and created a centralized location where we as an organization can use the data to provide intelligent and strategic analysis. It will further show, as an example, how CenterBridge data can be combined with other departmental data, as well as, macroeconomic data, to provide more insight into customer and agent behavior. The final part will discuss some of the statistical methodologies that can be utilized to derive information.TRANSCRIPT
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
Data Overflow
Corporate Sales
Switch (Avaya)
HR Data (PeopleSoft)
Email (Kana)
Workforce Management (IEX)
External Data (Benchmarking)
Forecasting & Planning
(CenterBridge)
Financials (Accounting)
Agent Surveys
International (Different Systems)
What is a Data Warehouse
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.
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/
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)
Steps to Create a Data Warehouse
IEX or CenterBridge
Owner: John Johnson, Telecom,
Omaha
Users: Call Center Management
Stand-alone or composite
Purpose / Definition of data
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
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
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
Steps to Create a Data Warehouse
Date Link Table Example
txtDate ntxtDate Date Year Month Week WeekDay Period PdWeek OTR4/1/2012 41000 4/1/2012 2012 4 14 Sunday 4 2 24/2/2012 41001 4/2/2012 2012 4 14 Monday 4 2 24/3/2012 41002 4/3/2012 2012 4 14 Tuesday 4 2 24/4/2012 41003 4/4/2012 2012 4 14 Wednesday 4 2 24/5/2012 41004 4/5/2012 2012 4 14 Thursday 4 2 24/6/2012 41005 4/6/2012 2012 4 14 Friday 4 2 24/7/2012 41006 4/7/2012 2012 4 14 Saturday 4 2 24/8/2012 41007 4/8/2012 2012 4 15 Sunday 4 3 24/9/2012 41008 4/9/2012 2012 4 15 Monday 4 3 24/10/2012 41009 4/10/2012 2012 4 15 Tuesday 4 3 24/11/2012 41010 4/11/2012 2012 4 15 Wednesday 4 3 24/12/2012 41011 4/12/2012 2012 4 15 Thursday 4 3 24/13/2012 41012 4/13/2012 2012 4 15 Friday 4 3 24/14/2012 41013 4/14/2012 2012 4 15 Saturday 4 3 2
Steps to Create a Data Warehouse
Schema Example
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
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”)
Benefits of a Data Warehouse
Consistent Numbers Easier to Audit / Problem Fixing. Quick Ad Hoc Reporting Knowledge of Data Available Data Mining
Data Mining
What is it? Why do we not use it more often?
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.
Getting Stats in Excel
Getting Stats in Excel
Getting Stats in Excel
Getting Stats in Excel
Getting Stats in Excel
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.
Comparing Groups of Data
Comparing Groups of Data
Agent 1 year1-4 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
Comparing Groups of Data
Anova: Single Factor
SUMMARYGroups Count Sum Average Variance
1 year 160 61.82122445 0.386382653 0.0039605921-4 years 160 76.16293624 0.476018351 0.0044196344+ 160 81.91744414 0.511984026 0.00356321
ANOVASource of Variation SS df MS F P-value F crit
Between Groups 1.338868966 2 0.669434483 168.1512278 5.38904E-56 3.014625576Within Groups 1.899006344 477 0.003981145
Total 3.23787531 479
Types of Regression Analysis
• Simple Linear Regression• Multiple Regression• Lagged Regression• Stepwise Regression• Logistic Regression
Simple Regression
• Example: Does the 2010 call volume explain the 2011 call volume?
• Simple Regression comparing 2010 with 2011 by week.
Simple Regression
SUMMARY OUTPUT
Regression StatisticsMultiple R 0.86 R Square 0.74 Adjusted R Square 0.74 Standard Error 9,790.76 Observations 52
ANOVAdf 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
Growing Intelligence by Properly Storing and Mining Call Center Data
Questions?Comments?
Geir RosoyManager of Resource Intelligence