1. Professor Vijay Mehrotra vmehrotra@usfca.edu / drvijay@sfsu.edu
“If the Phone Doesn’t Ring, It’s Me”: An Services Science Success Story
Professor Vijay Mehrotra /

2. Presentation Roadmap Intro to Call Centers The Business Problem
The Statistical Problem
The Organizationational Problem
The Results!
Summary: Rich Area For Research

3. What is a Call Center?
Any facility or group of facilities which has the processing of telephone calls as its primary business purpose
EXAMPLES:
Technical Support
Airline Reservations
Catalog Sales
Financial Transactions Processing

4. Why Call Centers? Ubiquitous Growing Business Importance
Employ over 3% of US Population
Estimates: 78,000 centers (US), 28,000 (EUR),
Growing Business Importance
Front Door to Firm (Over 80% of Businesses)
Impact of Poor Service on Customer Loyalty
Co$tly to Operate

5. Why Call Centers? Data Rich High Complexity People Costs Dominate
Stochastic Demand, Variable Process Time
Mass Customization / Segmentation  Variety
, Chat, Web  Multiple Channels
People Costs Dominate
Labor Accounts For 60-75% of Overall Cost
Agent Turnover >>30% Annually Across Industry

6. The Complexities of the Technical Support Call Center
Management
And Organizational
Behavior
Operational
Economics
OR/ Statistics
Telecommunications/
Information
Systems
Business
Processes
I/O
Psychology
Engineering
Disciplines

7. Presentation Roadmap Intro to Call Centers The Business Problem
The Statistical Problem
The Organizational Problem
The Results!
Summary: Rich Area For Research

8. Business Problem Client Faced a Significant Problem:
Too Many Calls, Too Much $pent on Technical Support
Nearly 3x Industry Norm as a % of Revenues
What To Do?
Step 1: Move From HQ in PA to Places With Lower Labor Costs
Step 2: ????

9. “Tell Your Statistics to Shut Up”
Initial Engagement
Develop a Call Forecasting Model
Quantified the Relationship Between Software Units Shipped and Incoming Calls
Call Intensity Factor – TOO HIGH!!
Traffic
Forecasting FC
Model
Historical
Data
Business
Judgment

10. Our Solution: “More Statistics!”
Developed an Integrated “Call Stopping” Methodology
Solution is Obvious in Hindsight…
..The Story Behind the Solution is Instructive

11. Presentation Roadmap Intro to Call Centers The Business Problem
The Statistical Problem
The Organizational Problem
The Results!
Summary: Rich Area For Research

12. Early Discoveries
“We Use a ‘Wrap-up’ Coding System. It Has Been Working Just Fine For Us.”
Consistently Misused By TSRs
Applies to Known Issues or Generalities
No Drill Down to Root Cause or Solution
First Step: Paper Tracking
Small Group of Agents
Instructed on How to Track Call Content
Key Principle: Track What Caused the Customer to Pick Up the Phone

13. Early Discoveries

14. Early Discoveries Good News!
TSRs Could Track Effectively (When Taught)
With Support From Product Experts, We Could
Identify Recurring Issues
Classify Cases
Revelations!
Top 50 Issues > 30% of Calls
Top 100 Issues > 40% of Calls
Rarely Did an Individual Issue Comprise > 1% of Calls
Substantial Differences in AHT Across Different Top Issues

15. Major Implications…
Why Individual Agents Don’t “Know” Call Content Patterns:
Typical Agents Handles Approximately Calls Per Day, 150 Calls Per Week
Over 300 Agents
High Variance in Calls Handled
For an Issue with p=0.01:
P(“Average” Agent Sees Any Issue More Than 2 Times in a Week) = 19%
Lesson: Beware the “Anecdotal Data”

16. Modeling Challenge How to Understand Call Patterns?
Too Many Calls For Two Assigned Analysts
Solution: Use Random Sampling
How to Determine Sample Size?
No Shortage of Trials (45,000 Calls/Week)
Accuracy of Probability Estimates?
Limited Skilled Resources, Lots of Skeptics
“What Do These People Do All Day?”
Sample/Classify Enough to Ensure Major Issues Are Identified
Get Analysts’ Buy-In on Feasibility of Sample Size
>

17. Presentation Roadmap Intro to Call Centers The Business Problem
The Statistical Problem
The Organizational Problem
The Results!
Summary: Rich Area For Research

18. Organizational Challenges
Product
Mktg
Eng
& Doc
Sales
Technical Support

19. Organizational Challenge
Technical Support Holds Low Status in Business
Perceived Cost Center
Perceived Drain on Bottom Line
No Credibility Internally (Historical, Endemic)
Marketing and Sales Driving Product Direction
Typical For Software Industry
“More Features, More Features, More Features”
Puts Pressure on Engineering Resources
Historically, Crowded Out All But Most Egregious Fixes

20. Our Solution: Crosses Many Organizational Boundaries
Relied on Data Collected Through New CRM System
System Design Was Largely FUBAR
Cross-Functional Team with Active Representation From:
Product Marketing
Engineering and QA
Documentation and Web

21. Organizational Challenge
Why Did We Get Traction? Several Reasons:
Exploited Corporate Visibility of Problem
Perceived “Crisis”
Made our Cross-Functional Team Politically Attractive
Identified Champions at Leadership Positions
Engineering, Documentation Heads
Credibility of Process and Analysts
Spoke Language of PMs, Eng, Doc
Provided Detailed Information

22. Presentation Roadmap Intro to Call Centers The Business Problem
The Statistical Problem
The Organizational Problem
The Results!
Summary: Rich Area For Research

23. Results: Major Savings

24. Results: Major Savings5

25. Presentation Roadmap Intro to Call Centers The Business Problem
The Statistical Problem
The Organizational Problem
The Results!
Summary: Rich Area For Research

26. Vijay’s View on the Value of Services Science Research
“If you hold on tight to what you think is your thing,
You may find you’re missing all the rest.”
Dave Matthews

27. Research Opportunities
CRM Value Creation Success Stories
What are the Key Characteristics?
Process Improvement Accompanying Tech Innovation
Quantifiable Business Results
How Do We Study This?
Design for Supportability / Software QA
What are best practices? Who is doing this well?
Enterprise Software Firms
Internal IS Groups
Fair and Accurate Measurement Methods?

28. Research Opportunities
Ethnography: Technical Support Operations
Huge, Growing, and Barely Researched
Successful Research Requires Skills From
Strategy, Organizational Behavior
Information Systems
Decision Sciences
Automated Call Content Segmentation
Rich problems in AI / CS Realm
“Sexy” Solutions