Predicting Distributions of Waiting Times in Customer Service Systems using Mixture Density Networks

2019 ◽  
Author(s):  
Majid Raeis ◽  
Ali Tizghadam ◽  
Alberto Leon-Garcia
Keyword(s):  
Customer Service ◽  
Waiting Times ◽  
Service Systems ◽  
Mixture Density

Personalized Priority Policies in Call Centers Using Past Customer Interaction Information

2021 ◽  
Author(s):  
Brett Alan Hathaway ◽  
Seyed Morteza Emadi ◽  
Vinayak Deshpande
Keyword(s):  
Customer Service ◽  
Operations Management ◽  
Bayesian Learning ◽  
Structural Model ◽  
Call Centers ◽  
Waiting Times ◽  
Operational Performance ◽  
System Throughput ◽  
History Data ◽  
Individual Level

To increase revenue or improve customer service, companies are increasingly personalizing their product or service offerings based on their customers' history of interactions. In this paper, we show how call centers can improve customer service by implementing personalized priority policies. Under personalized priority policies, managers use customer contact history to predict individual-level caller abandonment and redialing behavior and prioritize them based on these predictions to improve operational performance. We provide a framework for how companies can use individual-level customer history data to capture the idiosyncratic preferences and beliefs that impact caller abandonment and redialing behavior and quantify the improvements to operational performance of these policies by applying our framework using caller history data from a real-world call center. We achieve this by formulating a structural model that uses a Bayesian learning framework to capture how callers’ past waiting times and abandonment/redialing decisions affect their current abandonment and redialing behavior and use our data to impute the callers’ underlying primitives such as their rewards for service, waiting costs, and redialing costs. These primitives allow us to simulate caller behavior under a variety of personalized priority policies and hence, collect relevant operational performance measures. We find that, relative to the first-come, first-served policy, our proposed personalized priority policies have the potential to decrease average waiting times by up to 29% or increase system throughput by reducing the percentage of service requests lost to abandonment by up to 6.3%. This paper was accepted by Vishaul Gaur, operations management.

Algorithmic analysis of the BMAP/D/k system in discrete time

2003 ◽  
Vol 35 (4) ◽  
pp. 1131-1152 ◽  
Author(s):  
Attahiru Sule Alfa
Keyword(s):  
Structural Properties ◽  
Customer Service ◽  
Discrete Time ◽  
Queue Length ◽  
Busy Period ◽  
Marginal Distribution ◽  
Waiting Times ◽  
Single Server ◽  
Waiting Time Distribution ◽  
Algorithmic Analysis

We exploit the structural properties of the BMAP/D/k system to carry out its algorithmic analysis. Specifically, we use these properties to develop algorithms for studying the distributions of waiting times in discrete time and the busy period. One of the structural properties used results from considering the system as having customers assigned in a cyclic order—which does not change the waiting-time distribution—and then studying only one arbitrary server. The busy period is defined as the busy period of an arbitrary single server based on this cyclic assignment of customers to servers. Finally, we study the marginal distribution of the joint queue length and phase of customer arrival. The structural property used for studying the queue length is based on the observation of the system every interval that is the length of one customer service time.

scholarly journals Customer Development Strategies and Performance of Start Up Carbon Projects; A Case of Study of Sustainable Agriculture Tanzania (SAT)

2021 ◽  
Vol 3 (1) ◽  
pp. 49-64
Author(s):  
Betty Jeruto Cheruiyot ◽  
Mary Ragui
Keyword(s):  
Sustainable Agriculture ◽  
Customer Service ◽  
Business Model ◽  
Development Strategies ◽  
Forest Carbon ◽  
Service Systems ◽  
Communication Process ◽  
Customer Base ◽  
Start Up ◽  
Customer Development

Present challenges experienced by a globalized and changing world with new forms of doing business has forced entrepreneurs to change their approach to customers especially given the prior traditional marketing theory. Current markets have a customer base with increasing in demand for more marginal products or services. Henceforth, they have created individual preferences. The general objective of this study was to establish the effect of customer development strategies on performance of start-up carbon projects; a case of study of sustainable agriculture Tanzania (SAT) Specifically, aimed at examining the effect of business model on Performance of forest carbon projects, examine the effect of customer service systems on Performance of forest carbon projects, the effect of communication process on Performance of forest carbon projects, including effect of competitive pricing on Performance of forest carbon projects. It is anchored on theory of product market fit, start-up marketing pyramid and cue utilization theory. The study is further supported by the following models; business model canvas, value proposition canvas and customer development model. The study used a survey design. The survey collected data and information aimed at identifying customer development strategies for performance of carbon projects in the start-up carbon market. The selected population were customers of Sustainable Agriculture Tanzania. They consisted of companies, partners and individuals who were involved with the carbon offsetting project or potential clients. Ten companies and fifty individual customers were selected for the survey. Individual customers were drawn from the current connections developed by Sustainable Agriculture Tanzania. Primary and secondary data was utilized from various secondary sources linked to the topic of study and gathered facts and figures from the questionnaires. The questionnaire comprised of questions relating to the carbon markets for organizations in the start-up carbon projects. The quantitative data retrieved from the study was analysed by use of descriptive statistics that included variability, frequency and central tendency measures. These help provide information regarding the distribution. Measures of frequency distribution on the other hand document the frequency of scores or records. The Statistical Package for Social Sciences (SPSS) program was used to analyse the data and output presented in form of tables, pie charts and bar graphs. The study found out that customer development strategies in start-up forest carbon projects are central to performance of forest carbon projects. Playing a part to this performance are particularly the company’s prices, enhanced innovation and creativity, business management system, reduction of the time required for decision-making, and improved planning of activities.  At the same time, use of IT data management systems has to a large extent made the decision-making process faster. These aspects generally lead to better management of budgets efficiency in service provision; consistent increase in revenue and number of customers has increased. Moreover, customer development strategies lead to customer satisfaction improvement. In conclusion to the presented findings, communication process and customer service systems have shown to influence the performance of forest carbon projects. Setting up a tailored business model that works well with the cost structure of the business and suits project goals has also been visibly altering the functioning of projects. The strategy implemented on pricing is also seen to promote customer growth. It is recommended that start-up projects develop relevant pricing strategies for their products; especially during their early stages of development in order to differentiate and grow a viable customer base. Managers should also encourage proper training and practices when it comes to inter-organizational communication and strategy implementation. This will ensure every employee works in cohesion towards achieving the projected goals thus improving performance.

scholarly journals Customer Service System Design Based on Big Data Machine Learning

2021 ◽  
Vol 2066 (1) ◽  
pp. 012017
Author(s):  
Yuqiang Kong ◽  
Yaoping He
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Customer Service ◽  
Service System ◽  
Rapid Development ◽  
Good Method ◽  
Service Systems ◽  
Labor Costs ◽  
High Turnover ◽  
Online Transactions

Abstract In recent years, with the rapid development of big data, traditional offline transactions have been moved to online in large numbers driven by the Internet. The virtual nature of online transactions has caused it to have problems such as difficulty in guaranteeing product quality and difficulty in user consultation. In addition, consumers are paying more and more attention to the quality of services, and the participation of customer service in the process of online transactions is very important. However, the current e-commerce market in our country is large and the number of online shopping users is extremely large. Customer service personnel are facing great work pressure. In addition, customer service has the characteristics of difficulty in recruiting, high labor costs, and high turnover rate. Such a dilemma is not conducive to our country. The sound development of e-commerce needs to be solved urgently. In order to solve these problems, it is a good method to apply related technologies to realize the automatic response of customer service. The purpose of this article is to design and research a customer service system based on big data machine learning. This article first through the understanding of the basic concepts of big data, and then extend the core technology of big data. Combining with the design ideas and concepts of contemporary customer service systems in our country, we will discuss the design and research of customer service systems based on big data machine learning. Research shows that traditional customer service in the era of big data can no longer meet people’s growing needs, and customer service systems based on big data machine learning are more efficient and convenient.

Structured approach to improving wait times and overall customer service in an oncology outpatient phlebotomy department.

2013 ◽  
Vol 31 (31_suppl) ◽  
pp. 77-77
Author(s):  
Patrick M. Forde ◽  
Kitty Violette ◽  
Karen Maylor ◽  
Barbara Kasecamp ◽  
Beth Rushworth ◽  
...  
Keyword(s):  
Customer Service ◽  
Waiting Times ◽  
Limited Resources ◽  
Wait Times ◽  
Staff Satisfaction ◽  
Work Schedule ◽  
Leadership Positions ◽  
The Past ◽  
Patient Satisfaction Scores ◽  
Structured Approach

77 Background: Demands on the Johns Hopkins phlebotomy service have increased exponentially over the past several years leading to increased patient (pt) waiting times and reduced patient and staff satisfaction. Methods: The goal of our project was to reduce waiting times for outpatient phlebotomy to <30 mins for 90% of pts by May 2013 using a multi-disciplinary lean sigma approach. The following interventions were implemented - Two weekly multidisciplinary lean sigma meetings; Move to set appointment times; Twice daily staff "huddles" to plan the work schedule; Ensure a minimum of 8 phlebotomists on duty daily; Expand training for clinical assistants (CAs) to access VADs; Introduce pager system for pts to reduce needless waiting; Introduce leadership positions among phlebotomy and CA staff to manage change. Results: Within 6 months of these concerted efforts wait times have reduced to <30 mins and patient satisfaction scores for >90% of pts are very good or excellent. Conclusions: Difficult problems such as increasing patient demand with constrained resources can be ameliorated with a multidisciplinary structured approach. The project as outlined may serve as a template for other oncology services dealing with the increasing demands of an aging population in a time of increasingly limited resources.

scholarly journals Pseudo-conservation laws in cyclic-service systems

1987 ◽  
Vol 24 (4) ◽  
pp. 949-964 ◽  
Author(s):  
O. J. Boxma ◽  
W. P. Groenendijk
Keyword(s):  
Conservation Laws ◽  
Conservation Law ◽  
Waiting Times ◽  
Service Systems ◽  
Stochastic Decomposition ◽  
Single Server ◽  
Weighted Sum ◽  
Cyclic Service ◽  
The Mean

This paper considers single-server, multi-queue systems with cyclic service. Non-zero switch-over times of the server between consecutive queues are assumed. A stochastic decomposition for the amount of work in such systems is obtained. This decomposition allows a short derivation of a ‘pseudo-conservation law' for a weighted sum of the mean waiting times at the various queues. Thus several recently proved conservation laws are generalised and explained.

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