Routing to Manage Resolution Rates and Service Times in Call Centers with Heterogeneous Customers and Servers * Vijay Mehrotra, University of San Francisco Yong-Pin Zhou, University of Washington Kevin Ross, University of California-Santa Cruz Geoff Ryder, University of California-Santa Cruz, SAP Laboratories * Currently under revision for MSOM

Outline of Presentation  “Who is this Guy?”  Modeling Service Quality Traditional Paradigm, Recent Developments  Performance-Based Routing Framework Parameters, Key Business Questions  Experimental Framework Simulation Models, Routing Rules, Model Inputs Output Analysis  Conclusions and Future Research

About Vijay: Early Influences  Fascinated by Telephones at a Young Age  As a Child, Lay in Bed Dreaming About Call Centers  Accidently Detoured By Stanford OR Department {Mistakenly thought work on stochastic telecommunications networks involved talking on the phone at random times }

About Vijay: Academic Career  PhD in OR, Stanford University, 1992 Thesis: Performance Analysis of Multiclass Closed Queueing Networks  Assistant Professor (Fall 2003 – Spring 2009) Department of Decision Sciences College of Business, San Francisco State University  Visiting Scholar (June – December 2006) Dept of IEOR, UC-Berkeley  Associate Professor (Fall 2009 – Present) Department of Business Analytics University of San Francisco

About Vijay: Business Career  1993 – 1994: Associate, Decision Focus Inc. OR Consulting Firm Transportation, Electric Power, Revenue Optimization  : Co-Founder and CEO, Onward Inc. Analytics Consulting Firm Forecasting, Pricing, Scheduling, Process Improvement, Call Center Operations Management, Advertising Optimization  : Vice President of Solutions, Blue Pumpkin Software Inc. Managed Staff of 50 Scheduling and Software Consultants Executive Sponsor of Large-Scale Deployment of Call Center Scheduling Software for EDS (10,000+ agents)

About Vijay: Call Center Research  Theme #1: “Excess” Variability in Call Volumes  Staffing/Scheduling Models  Theme #2: Leveraging Mountains of Data for Performance Improvement

Outline of Presentation  “Who is this Guy?”  Service Quality for Call Center Operations Traditional Paradigm, Recent Developments  Performance-Based Routing Framework Parameters, Key Business Questions  Experimental Framework Simulation Models, Routing Rules, Model Inputs Output Analysis  Conclusions and Future Research

Classic Management Trade-Offs In Call Center Operations Service Quality & Customer Satisfaction Employee Satisfaction & Attrition Costs & Financial Results

Traditional OM/OR Paradigm For Call Center Resource Models  PROBLEM: Accurate and Believable Data About Customer Satisfaction Difficult to Measure, Predict  SOLUTION: Customer Waiting Time Distribution as Proxy for Service Quality  RATIONALE: Long Customer Waiting Times = Low Customer Satisfaction

10 Emerging Customer-Centric Metric First Call Resolution Rate (FCR): the percentage of customer phone calls that are resolved successfully during the first attempt at contacting the company by phone

New Discoveries/Developments  Recognition of the Importance of FCR Rates Huge Impact on Customer Satisfaction/Retention Customer Callbacks Also Create Congestion  New Information Systems Able to Accurately Measure FCR Rates at Agent-Queue Level  Data Reveals Significant Differences in Agent Performance For Different Queues Call Handling Times, Call Resolution Rates  ?? How to Capitalize on this Data ??

Outline of Presentation  “Who is this Guy?”  Service Quality for Call Center Operations Traditional Paradigm, Recent Developments  Performance-Based Routing Framework Parameters, Key Business Questions  Experimental Framework Simulation Models, Routing Rules, Model Inputs Output Analysis  Conclusions and Future Research

Traditional Model: Inbound Call Centers With a Single Queue Single Type of Phone Call Arriving Over Time

Our Framework: Heterogeneity in Handle Times Inbound Call Queue 2 Inbound Call Queue 1

Our Framework: Unresolved Calls, Heterogeneity in Resolution Rates Inbound Call Queue 2 Inbound Call Queue 1 Resolved Callback Resolved Callback

 Inbound call center with M “call types” Key parameters: arrival rates i  Each agent is belongs to one of N groups Key params: # agents n j, services rate  ij, resolution probabilities p ij,  Heterogeneity across all parameters Our Model Framework: Parameters

Business Question  Key Routing Questions BUSINESS QUESTION: Given the relative strengths and weaknesses of different agents, can we devise routing strategies that simultaneously INCREASE CR (Call Resolution Rates) DECREASE ASA (Avg Waiting Time)

Business Question  Key Routing Questions ROUTING LOGIC: BASED ON TWO QUESTIONS 1. Agent Selection: When a call arrives and finds agents from more than one agent group available to handle it, which agent should be selected to handle it? 2. Call Selection: When an agent finishes handling a call and finds more than one type of call waiting, which call type should the agent choose to serve?

Outline of Presentation  “Who is this Guy?”  Service Quality for Call Center Operations Traditional Paradigm, Recent Developments  Performance-Based Routing Framework Parameters, Key Business Questions  Experimental Framework Simulation Models, Routing Rules, Model Inputs Output Analysis  Conclusions and Future Research

Modeling Challenge: How to Evaluate Different Routing Strategies? System Complexity  No clear analytic models to evaluate dynamic routing strategies for ASA and CR results Our Approach: 1.Generate routing policies (from intuition, literature, and hypotheses) 2.Measure quality based on both CR & ASA 3.Use simulation models to compare rules

Myopic Policies: “Beauty is in the eye of the beholder…”  For a manager who prioritizes (min) ASA: ** ROUTING RULE: Max   For a manager who prioritizes (max) CR rate: ** ROUTING RULE: Max p  Alas, these myopic policies often fail to achieve even their myopic objectives…

Why Myopic Policies Fail: “Look Beyond the Obvious”  Myopic Waiting-Centric Routing Rule (Max  Does Not Always Minimize ASA  FREQUENT PROBLEM: Choosing the fastest servers may lead to higher % of unresolved calls (and thus more overall work and higher utilization)  ALTERNATIVE RULE * : Choose servers with fastest effective service rate ( Max p  ) * Originally introduced by de Vericourt and Zhou (2005)

Why Myopic Policies Fail: “Look Beyond the Obvious”  Myopic Resolution-Centric Routing Rule (Max p  Does Not Always Maximize CR Rate  SEVERAL POTENTIAL PROBLEMS  “Resolution vs. Speed”: What if highest RPs correspond to low service rates?  “Crowding out”: What if a particular agent group has highest RP for multiple groups?  WHAT TO DO?

Optimization Problem to Maximize Call Resolution Rates  Decision Variables: Proportion of calls of type i routed to agent group j ( x ij )  Additional Input Parameters: Minimum and Maximum Utilization Levels for Agent Groups ( ) and for Call Types ( )  Intermediate Values are (Quadratic) Function of DVs  Effective Arrival Rate  Agent Group Utilization  Utilization Allocated to Call Type

Optimization Problem to Maximize Call Resolution Rates

 For a manager who prioritizes (max) CR rate, our optimization suggests a new routing rule:  Randomly route calls of type i based on probabilities x ij  Once routed, calls wait in FCFS queues for chosen agent group (no jockeying between groups)  This rule “guarantees” maximum CR rate!  Alas, this rule has one (fatal) flaw:  Often results in calls waiting in queue while other agents are idle  long wait times! New Resolution-Centric Routing Rule!

“CallSwap(k)” Routing Rule – AGENT SELECTION 1.For call of type i, first assign calls based on optimal x ij 2.Once routed to queue for agent group j, check the queue length a)If queue length <= k, then stay in queue j b)If queue length > k and agents in groups other than j are free, then choose an agent from group g<>j with Max g<>j p ig  ig with at least one agent free c)If queue length > k and all agents in all groups are busy, stay in queue j. Queue j is considered “full” New Class of Hybrid Routing Rules

“CallSwap(k)” Routing Rule – CALL SELECTION 3.When an agent from some group j becomes free and calls are waiting in queue j, choose the oldest call 4.If no calls are in queue j, search for “full queues” for possible calls to serve 5.If one or more “full” queues exist, then choose the call that for which this agent group has the highest effective service rate (Max i p ij  ij ) New Class of Hybrid Routing Rules

??? “CallSwap(k)” Routing Rule ???? Note to self: if this seems totally confusing to attendees, then draw flowchart on board New Hybrid Routing Rule

Many virtues of CallSwap Policies  Respect for achieved CR rate  Initial routing decision based on optimal x ij  CallSwap( ∞ ) = OptXRand  Respect for achieved ASA  Work conserving policy  CallSwap(0) = Max p  New Hybrid Routing Rule: Balanced!

Numerical Experiments: Call Center Case Study  Large Financial Services Firm’s customer service call centers  M = 4 call types (subset of longer list)  N = 20 agent groups (clustered based on historical performance data)  Agents are fully cross-trained, heterogeneous: AHT values differ by call type & agent group FCR rates differ by call type & agent group

How To Interpret Results BEST Worst Depends on What You (and Your Customers) Value Depends on What You (and Your Customers) Value

Case Study: Efficient Frontier

Outline of Presentation  “Who is this Guy?”  Service Quality for Call Center Operations Traditional Paradigm, Recent Developments  Performance-Based Routing Framework Parameters, Key Business Questions  Experimental Framework Simulation Models, Routing Rules, Model Inputs Output Analysis  Conclusions and Future Research

Summary and Observations To Date  Dynamic Routing Rules Motivated By Availability of Detailed Agent-Queue Data For Both AHT and FCR Across “Call Types” Heterogeneity in Agent Performance  Real Opportunity to Create Value from Analytics All of Our Dynamic Strategies Dominate FIFO Benefits Very Likely to Be Even More Pronounced When Implemented at Individual Level

Current and Future Research  Attempt to Generalize From Initial Case Studies In the Midst of Executing Large-Scale Sim Study Varying Many Parameters  Arrival Rates  Within Group Correlations  Across Group Correlations  Other Dynamic Rules to Consider?  How to Jointly Optimize Staffing and Routing?

Questions?? Vijay Mehrotra Yong-Pin Zhou Kevin Ross Please Feel Free to Contact Us: