Competence Management and Knowledge-Based Routing for Call Center Operations T. Rakotobe-Joel, J. Thomas, N. Dzhogleva Logistics and Operations Research laboratory Anisfield School of Business Ramapo College of New Jersey

Sprint Customers Getting the Boot June – July 2007: Sprint terminated the accounts of customers for “excessive use of their customer service lines” … Over the past year, a small number of our customers have made frequent calls regarding account information which we have been unable to resolve to their satisfaction, despite our best efforts. These individual are calling Care hundreds of times a month, for a period of 6-12 months on the same issues even after the issues appear to have been resolved …

Customer’s version of events – … We don’t enjoy calling them to get our billing errors (and the like) fixed; just as much as they hate us annoying them … – … they (Sprint) often told us to call back or transferred to another department, which would count as a new call to their customer care line …

Customer Prioritization Customer prioritization enhance firm performance (Reinartz, Kraft, and Hoyer 2004) Customer management strategies maximize individual customer life time value (Rust and Verhoef, 2005; Venkatesan and Kumar, 2004) Criteria: – Financial (Reinartz, Kraft, and Hoyer, 2004) – Customer related (Yim, Anderson, and Swaminathan, 2004) None addressed the issue of knowledge – Tacit vs Implicit Knowledge – Emergent and Self-Organizing Knowledge

Customer Loyalty vs. Satisfaction Completely satisfied are much more loyal (and profitable) than satisfied customers. Loyalty Satisfaction

The Research Project Key Questions – Do highly satisfied customers (Apostles) actually improve operational return? Approach – Model Call Center Operations as Profit Centers + Integrate Learning Process – Process Simulation Using ARENA Objective – Develop a self-learning system for competence management in a profit center operation Applications – Appropriate Call Center Assignment according to customer tacit knowledge/characteristics (Cultural, Technical, …) – Contextual Organization Development for Call Center Operations

The Model Basic Assumptions : Types and Number of Servers Interarrival Time Characteristics of Customers Net Present Value Level (NPV) Tacitreveal Satisfaction Stages of the Model Warm-up stages and their function Main (third) stage

Simulation Process Assign levels of NPV level, Tacit reveal, and Satisfaction for each customer entering the system based on the distributions formed by customers who have already been served Check if the current values of these variables are over certain thresholds to route the customers to the best available server. Procedure: Initial Satisfaction Decision: If terrorist, drop If apostle, send to High NPV Server If neither, continue

Simulation Process (Cont.) –Check for current customer high NPV level. If so, route to High NPV server –Check for current customer Tacit level. If so, route to High Tacit server –After processing, customer NPV, Tacit and Satisfaction are updated (delta is added to the current value of each variable).

Simulation Process (Cont.) – Each server type has a different mean and distribution of impact on customers. – We limit the line length in front of each server. – As the simulation progresses, the customer pool changes characteristics. It reveals its tacit preference, changes its NPV and changes its satisfaction – We run the simulation for a year.

Assignment of Deltas: When a customer is served by: High NPV server – Delta NPV = NORM(22,4) – Delta Satisfaction = TRIA( ,0.0001,0.0005) – TACIT does not change High TACIT server – Delta NPV = NORM(22,4) – Delta Satisfaction = TRIA( ,0.0006,0.0010) – Delta TACIT = NORM(0.05,0.01) Low TACIT server – Delta NPV = NORM(5,1) – Delta Satisfaction = TRIA( ,0.,0.0001) – TACIT does not change

Findings Customer NPV, Satisfaction and Tacitreveal increase over a year. This allows us to look at the customers as a profit source and the aggregate virtual server room as a profit center. Highly satisfied customers and Highly dissatisfied customers…. – BOTH yield a non-linear profit, and this is accounted in the model.

Experimental Design Setup HNPVTACITSATISFACTION High LowHighLow HighLowHigh Low High LowHighLow HighLowHigh Low with 2 replications for each combination

Preliminary Results Long Term Customer Value can be improved with prioritization of ‘Apostles’ customers Removing ‘Terrorist’ customers can positively affect long term profitability Using high-value servers for high-value customers can be justified by consistent positive changes in long-term profitability Low-value servers can be minimized and used as back-up

Where do we go from here? Business Applications – Transform customer support operations into profit centers – Unlock critical information from companies Customer Relationship Management (CRM) data Separate “highly satisfied” from “merely satisfied” customers to unlock hidden profits – Model can justify the need for first-rate servers Limitations – Model needs to ‘learn’ about customers It requires seed data – “Tacit” knowledge can be partitioned further for better results