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Routing to Manage Resolution Rates and Service Times in Call Centers with
Heterogeneous Customers and Servers*
Vijay Mehrotra, University of San FranciscoYong-Pin Zhou, University of WashingtonKevin Ross, University of California-Santa CruzGeoff 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 SciencesCollege of Business, San Francisco State
University
Visiting Scholar (June – December 2006)Dept of IEOR, UC-Berkeley
Associate Professor (Fall 2009 – Present) Department of Business AnalyticsUniversity of San Francisco
About Vijay: Business Career
1993 – 1994: Associate, Decision Focus Inc. OR Consulting Firm Transportation, Electric Power, Revenue Optimization
1994 - 2002: Co-Founder and CEO, Onward Inc. Analytics Consulting Firm Forecasting, Pricing, Scheduling, Process Improvement, Call
Center Operations Management, Advertising Optimization
2002 - 2004: 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
EmployeeSatisfaction & 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 TimeSingle Type of Phone Call Arriving Over Time
Our Framework:Heterogeneity in Handle Times
Inbound Call Queue 2Inbound Call Queue 2
Inbound Call Queue 1Inbound Call Queue 1
Our Framework: Unresolved Calls, Heterogeneity in Resolution Rates
Inbound Call Queue 2Inbound Call Queue 2
Inbound Call Queue 1Inbound Call Queue 1
Resolved
Callback
Resolved
Callback
Inbound call center with M “call types”
Key parameters: arrival rates li
Each agent is belongs to one of N groups
Key params: # agents nj , services rate mij,
resolution probabilities pij ,
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 QUESTIONS1. 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 & ASA3. 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 m
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 ) m 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 pm )
* 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 (xij)
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
jj ,
ii ,
xi xj
xi
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 xij
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 xij
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 Maxg<>j pigmig 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 (Maxi pijmij )
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 xij
CallSwap(∞) = OptXRand
Respect for achieved ASA Work conserving policy CallSwap(0) = Max pm
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
Plot of CR Rates vs. ASA
0
5
10
15
20
25
0.934 0.936 0.938 0.94 0.942 0.944 0.946 0.948 0.95
CR Rate
AS
A (
Sec
on
ds)
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 [email protected]
Yong-Pin [email protected] Kevin [email protected]
Please Feel Free to Contact Us:Please Feel Free to Contact Us:
