“If the Phone Doesn’t Ring, It’s Me”: An Services Science Success Story

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

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

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

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

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

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

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

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: ????

FC Model Historical Data Business Judgment Traffic Forecasting “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!!

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

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

Early Discoveries

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

Major Implications… • 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”

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 >

Organizational Challenges Product Mktg Eng & Doc Sales Technical Support

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

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

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

Results: Major Savings

Results: Major Savings 5

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

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?

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

“If the Phone Doesn’t Ring, It’s Me”: An Services Science Success Story

“If the Phone Doesn’t Ring, It’s Me”: An Services Science Success Story

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