- Simulation
The security alarms services market in the United States delivers hardware equipment and services to homeowners and businesses to help monitor and enhance personal property protection. Customer satisfaction via wait time reduction, first call resolution, and cost minimization are key drivers of success to players in this market. Most companies invest heavily in customer service systems including call centers. Our client, AlarmCo, a top provider of property protection, manages an inbound call center that supports a range of questions from customers who call with in thirty days from the alarm installation date. Often, security companies fail to utilize strategic solutions when managing inbound customer call traffic and default to reactive measures which unnecessarily increase customer wait times. The key question the team aims to address in this thesis is: “How can we improve the customer service experience for customers of a major security service provider in the United States?”
For this thesis, MIT partnered with OnProcessTechnology, a managed services provider specializing in complex, global service supply chain operations, to develop a robust framework to preemptively reduce the number of inbound customer calls, and thereby improve customer service. Using ABC segmentation, the team categorized customers by reason code and demographics. To simulate the client’s call center queue, the team calculated the key inputs for the queuing model including average wait time, interarrival rates and number of servers. The team then chose and developed the M/M/n stochastic queuing model for the simulation.The M/M/n queue reflects a simple system with parallel servers, arrivals with a Poisson distribution and service times that are exponentially distributed. Next, the customer segmentation was used to develop targeted preemptive solutions. Taking into account feasibility ratings, the team assigned success rates to each solution and adjusted the inbound call data accordingly. By analyzing the outputs of the simulation before and after adjusting the dataset, the team quantified the impact of preemptive solutions on the call center queue. Ultimately, narrowing to twelve strategic preemptive solutions led to the enhancement of the as-is queuing model by reducing average wait time by up to 35%.