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

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

3. 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 What is a Call Center?

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

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

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” TrafficForecasting FC Model Historical Data Business Judgment Initial Engagement Develop a Call Forecasting Model Quantified the Relationship Between Software Units Shipped and Incoming Calls Call Intensity Factor – TOO HIGH!!

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. “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 Early Discoveries

14. 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 Early Discoveries

15. Why Individual Agents Don’t “Know” Call Content Patterns: Typical Agents Handles Approximately 30 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” Major Implications…

16. 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 Modeling Challenge >

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. 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 Organizational Challenge

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. 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 Organizational Challenge

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

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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