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Managers need to make informed decisions. Using data, or research, to analyze your business is an important part of making decisions and evaluating business performance. An IT manager analyzes service levels, a marketing manager tries to predict the results of planned campaigns, and virtually any business manager needs data to identify relationships between relevant variables. Your skill in business research to analyze data and drive decision-making helps you to add confidence despite uncertainty, draw conclusions about organizational performance, and add value to your organization.
As the manager of a customer service call center, you are evaluating the quality of the call center’s operations. One of the most important metrics in a call center is time in queue (TiQ), the time a customer waits before a customer service representative (CSR) comes on the line to help. To provide the best customer experience, you want your customers’ wait to be less than the 2.5-minute (150 seconds) industry standard. You know that when they wait for too long, customers are more likely to have a negative experience or hang up before being helped.
Another metric you measure in the call center is the handling time, or service time (ST), the amount of time a CSR spends servicing the customer. Last month’s average ST was approximately 3.5 minutes (210 seconds). Your industry experience tells you that the average ST can be influenced by a CSR’s level of training, amount of experience, and whether the CSR can provide a resolution to the issue quickly. Recently, you tested a strategy to have callers identify the type of issue they are calling about and then route calls to CSRs with expertise in that issue.
The new protocol (PE) is being tested side-by-side with the current protocol (PT) to see if ST is improved with this strategy. The regional director was hesitant to test the PE protocol for two weeks, already concerned about the customer experience, but agreed to the test. After the first few days, the regional director is anxious to know the results and asks you to send a report. You ask the user experience (UX) team to pull a report for the TiQ and ST for both protocols to analyze.
Review the call time analysis provided by the UX team. You may also review the call time data in Excel if desired.
Write a 350- to 700-word email, or memo, about the PE and PT test results after the first few days. Address the following in your email:
Your memo should have two sections. In the first section, you should clearly identify the results of the time in queue test and your recommendation for the company. What should the company do as a result of your findings? And in the second section, the average service time test, you should clearly state your finding in a sense did the new protocol work and what you recommend for the company. Both test results are shown in the “call time analysis report.” The link is provided above. In case you cannot open the link, I also attached the document below. To help you clarify the concept, I also posted the week four assignment guidance video in the announcement section.
There should be 2 headings in your memo as follows:
There should be 2 headings in your memo as follows:
Heading 1: findings and recommendations for the time in the queue (TIQ) and the Impact of the new protocol.
Heading 2: findings and recommendations for the average service time test Impact of keeping current protocol.
Format references according to APA guidelines.
Submit your assessment.
Managers need to make informed decisions. Using data, or research, to analyze your business is an important part of making decisions and evaluating business performance. An IT manager analyzes service
DAT/565 v4 Call Time Analysis Time in Queue Test The team performed a test of hypothesis to determine whether the average TiQ is lower than the industry standard of 2.5 minutes (150 seconds). A significance level α=0.05 was used. This was a test of mean against a hypothesized value of 150 seconds. Because the sample size was large, we assumed knowledge of the population’s variance. The null and alternate hypotheses is: Ho : µ ≥ 150 H1 : µ < 150 This is a left-tailed test, and with a significance level of α=0.05 the critical value is z = -1.645. The decision rule becomes: Reject Ho if zcalc < -1.645. Figure 1: TiQ Rejection Region The test statistic is given by: The test statistic zcalc falls outside the rejection region, so we reject the null hypothesis in favor of the alternate hypothesis. We conclude the call center’s average TiQ is greater than the industry’s average of 150 seconds. Figure 2: Results of TiQ Hypothesis Test: Mean versus Hypothesized Value Average Service Time Test The team performed a test of hypothesis to determine whether the service time (ST) with new service protocol PE is lower than with the current PT protocol. A significance level of α=0.05 was used. This is a test of means for two independent samples with unknown variances assumed unequal. Sample 1 is the data from the PT (current) protocol. Sample 2 is the data from the PE (new) protocol. We tested whether the mean ST with protocol PE is smaller than the mean with protocol PT. The null and alternate hypotheses are: Ho : µ1 ≤ µ2 H1 : µ1 > µ2 This is a right-tailed test. Because of the very large samples, there is no real difference between finding the critical value with a normal distribution or the t distribution. The critical value with a significance level α=0.05 is t = 1.645. The decision rule becomes: Reject Ho if tcalc > 1.645. Figure 3: Hypothesis Test of Independent Groups (t-test, unequal variance) The tcalc = -6.8 falls in the rejection region. We conclude that the new protocol (PE) results in a shorter average service time than the traditional protocol (PT) based on available data. Copyright 2023 by University of Phoenix. All rights reserved.