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Bear, Inc. is a fictional company which is based on Grainger..docx
- 1. Bear, Inc. is a fictional company which is based on Grainger. Grainger is a Fortune 500 company founded in 1927, which sells maintenance, repair, and operating products to various companies worldwide. This paper will formulate research criteria from the perspective of a call center manager for Bear, Inc. Variables In an experiment, the independent variable is the variable that is varied or manipulated by the researcher, and the dependent variable is the response that is measured. An independent variable is the presumed cause, whereas the dependent variable is the presumed effect. For this research topic, the dependent variable will be the average talk/handle time of call center phone calls. The goal will be three minutes. The independent variable will be product training, measured by the ways individual customers are helped. In choosing these variables, Learning Team A seeks to determine why some call center staff have higher than average handle/talk times. Scenario Running head: Learning Team A 0 Some of the call center team members at Bear, Inc. have higher than average talk/handle time. The standard that performance is measured on is an average of 3 minutes per call. Lately, however, the measurement of that number has been skewed higher than anticipated. Manage wants to determine what is going on to cause higher than expected numbers. Bear, Inc. strives for outstanding customer service and has a service
- 2. guarantee to stand behind. Feedback indicates that customers are not receiving proper information to solve their issues, which is causing a huge problem for management. Learning Team A will be investigating the root of the problem by identifying both dependent and independent variables to test within the department and by developing a research question and hypothesis to test in regards to the current situation that Bear, Inc is facing. Research Question The research question is based on both the dependent and independent variables. The problem this study seeks to identify is, “Is there a difference talk/handle time based on the amount of training received?” Hypothesis According to dictionary.com, a hypothesis is a “proposition, or set of propositions, set forth as an explanation for the occurrence of some specified group of phenomena, either asserted merely as a provisional conjecture to guide investigation or accepted as highly probable in the light of established facts.” The hypothesis of the research by Learning Team A consists of two variables, the talk/handle time which is the dependent variable, and the training provided to the customer service representatives, which is the independent variable. The training hours per representative should be 40 hours minimum, which not all representatives have yet completed, though they are already trying to assist customers. These representatives take longer to resolve the customer's problems, which are affecting and extending the talk/handle time average variable of three minutes. Management should ensure that all representatives complete the training to resolve the issue and to maintain the three-minute talk/handle time average. Literature Review Introduction Currently, Bear Inc. is researching the idea that average handle time could be directly affected by the completion of 40 hours of product training by each customer service representative. The
- 3. team will research the idea and decide if this is still the best route for Bear, Inc. to evaluate or if we want to change the question and evaluate a better idea or option. Utilizing previous research will help determine the best course of action. Below, each team member has summarized articles relating to Average Handle Time. Article summaries The article offers a brief summary on some of the written material dealing with the management of call center operations. It looks at the traditional research and management challenges often created by the introduction of new technology. The research focuses on things like the behavior of employees working as customer service representatives. One key point is the idea of “call forecasting,” which the article defines as “(a) the specific queue or call type associated with the forecast; (b) the time between the creation of the forecast and the actual time period for which the forecast was created (often referred to as the forecasting ‘lead time’); and (c) the duration of the time periods for which the forecasts are created, which can range from monthly (to support resource acquisition decisions) to short time frames, such as 15-, 30-, or 60-minute periods (to support resource deployment decisions)” (Aksin, Armony, & Mehrotra, 2007, p. 667). The article indicated this is a fairly new area that is not frequently used. This article focused on employees at call centers and how the Call centers are often perceived to have a negative impact on employee wellbeing, mainly attributed to four factors: job design, performance monitoring, HR practices and team leader support. This article reports on a survey of 557 customer service representatives that examined the relationship of these factors to four measures of wellbeing: anxiety, depression, intrinsic job satisfaction, and extrinsic job satisfaction. Results demonstrated that the factors most highly associated with wellbeing were high control over work methods and procedures, a low level of monitoring and a supportive team leader. Evidence also indicates that the level of wellbeing in some call
- 4. centers is similar to that in other comparable forms of work. "The average handle time is a very important statistic that relates directly to staffing needs, employee performance, and average call cost" (Peterson, 2014). The average handling time AHT has three components—talk time, hold time, and after call work—which contribute to the total AHT divided by the total number of phone calls received. To improve AHT, it is important to emphasize product knowledge, multitasking, agent's training, technology used, and tools. A well created script which consolidates all types of problems a customer will call for with the answers of most common issues will be a helpful tool to improve AHT. Research Conclusion Training—and the subsequent product knowledge received from training—may not be the most significant variable affecting average handle time. As a result, it is important for Learning Team A to assess the various elements that can affect AHT. Depending on this assessment, the team can then revise the research question for this study. References Aksin, Z., Armony, M., & Mehrotra, Vijay. (2007, Fall). The Modern Call Center: A Multi-Disciplinary Perspective on Operations Management Research. Production and Operations Management, 16(6), 665-688. Cooper, D., & Schindler, P. (2011). Business Research Methods (11th ed.). New York, NY: McGraw-Hill Irwin. Dictionary.com. Retrieved from http://dictionary.reference.com/browse/hypothesis Efficiency Indicators – Average Handle Time (AHT). (). Customer Reach,(), . Retrieved from http://www.pearladvisorygroup.com/resource- centre/articles/average-handle-time/ Grainger. (1994-2014). Retrieved from http://pressroom.grainger.com/phoenix.zhtml?c=194987&p=irol -overview
- 5. Peterson, M. (2014). www.answers.com. Retrieved from http://www.answers.com/Q/What_is_the_importance_of_measur ing_the_average_handle_time 7 Descriptive Statistics and Interpretation Example Interpretation Phrases Central Tendency: Mean = average of a set of data Median = half or equal number of data is above and half or equal number of data is below. It is a midpoint in an ordered (sorted) set of data, a physical location Mode = most frequent value in a set of data Dispersion: Standard deviation = variation Interquartile range (IQR) = the middle 50% of the data Range = the difference between the largest and smallest value of the data Confidence Interval: (data must be normal) There is 95% confidence that the population average is between _____ and ____ units. Normal or significantly skewed data: MegaStat: Descriptive statistics Normal curve goodness of fit p- value · Normal, p-value > .05 · Significantly Skewed, p-value < .05 Histogram: Eyeball the histogram. · Normal data will have a symmetrical or slightly skewed shape. · Significantly Skewed shape will have extreme skewness Use phrase combinations: Normally distributed: Mean and
- 6. Standard Deviation, Not normally distributed: Median and IQR Descriptive Statistics Body Weight (Lbs.) Central Tendency: Mean = 149 Lbs. Dispersion: Standard deviation = 30 Lbs. Count: 100 Min/Max: 99 pounds and 234 Lbs. Confidence Interval: 144 to 155 Lbs. See the histogram in Appendix A, and descriptive statistics in Appendix B. Age Distribution is not normally distributed Central Tendency: Median = 36 years Dispersion: Interquartile Range = 20.5 years / 2 = ± 10 years Count: 100 Min/Max: 18 years and 74 years Confidence Interval:
- 7. Not applicable (data is not normally distributed) See the histogram in Appendix A, and descriptive statistics in Appendix B. A scatter plot is in Appendix C. Education Level Thirteen percent of the subjects have no high school degree while 44% have high school degree. Forty three percent have a college or college graduate degree. See the bar chart in Appendix D. Descriptive Statistics Interpretation Interpretation Body Weight One hundred subjects were randomly selected. Their body weight was observed between 99 and 234 pounds. Their average weight was 149 pounds, with a variation of plus or minus 30 pounds. One half or more were above 149 pounds. There is 95% confidence that the population body weight average is between 144 and 155 pounds. Age The data was significantly skewed. One hundred subjects were randomly selected. Their ages were between 18 and 74 years, with a variation of plus or minus 10 years. One half or more subjects were 36 years of age or older. The middle half of the subjects’ ages fell between 27 and 47 years. The most frequent age was 36 years. APPENDIX A
- 8. Body Weight and Age Histograms APPENDIX B Descriptive Statistics Body Weight and Age APPENDIX C Scatterplot Body Age versus Weight APPENDIX D Bar Chart Education Level Call-Center- DataAgentCallsAnsweredAbandonedAvgTalkDurationAgentTrai ning Received16330:04:011Intermediate5231850:01:112Intermediate 124130:00:003No training5194150:04:014No training3373340:05:075Advanced2238150:04:366Intermediate7 245190:04:397No training49450:03:458Advanced98260:03:459No training119810:05:4310Advanced103716210:03:4511Intermedia te123911280:04:5012Advanced4161330:04:0113Intermediate11 393270:01:1114Advanced129360:00:0015No training10221570:04:0116No training11228140:04:3117Advanced83300:05:0718Advanced514 1130:04:3619Intermediate12181260:04:3920Intermediate126188 0:04:2821Intermediate95500:03:4522Intermediate5355300:05:4 323Intermediate7214170:00:0024No training11121200:04:5025No training47430:04:0126No training10153120:04:0127No training39180:01:1128Advanced44220:00:0029Advanced514590 :04:0130Advanced103119120:04:3621Advanced2171340:04:393