Research Analysis Report

Torrens University Australia is a part of Laureate International Universities, the renowned global network with 70 accredited universities and higher education institutions and has students of about 1 million worldwide (Torrens University Australia, 2020). Torrens University has the major aim of bringing new, contemporary, career focused and international perspective to higher education (Torrens University Australia, 2019). The University design courses in a way it provides the graduates to develop internationally oriented skills which facilitate them to be a valuable employee to any organization. Torrens offers graduate and post graduate courses in different disciplines and campuses extend over various destinations such as Sydney, Melbourne, Adelaide, Brisbane, and NSW. This is a report prepared by the Customer service Manager at Torrens University.

In the current contemporary world, the educational universities and other related institutions are facing lot of issues and the institutions are adapting approaches to deal with the issues. Bruni, Carubbo, & Sarno, (2018) has stated that system thinking is the most common approach that most of the business leaders adapts to solve the major issues in a rapid manner. Particularly the service quality which is perceived to be the major issue in Universities is declining slowly and due to this many potential students are diverted to rival universities . The issue of customer dispersion due to the long waiting time to connect to the executives is the common issue in educational universities. This slowly aggravates and shared in social media which became a risk factor for university reputation. Resolving the issue of customer service is becoming very challenging and effective decisions has to be made using a range of decision making tools to improve the customer service and increase the enrollment of students

There is a perception that individual decision making is the simplest process. But while considering with group decision making, it becomes one of the complex issue for the customer service manager to determine which the better and effective one is . In certain small issues like sharing university promotional activities to a set of customers individual decision making is perceived to be more productive. While for admission procedures, discounts and other customer related issues, group decision making with different departments such as customer service, admission, and payment facility proved to be the wiser choice.

(Yan, Liu, & Skitmore, 2018) has suggested that in regard to the issue of strengthening the customer service in the Torrens University, group decision making will be effective as the issue involves different departments and employees . As a first step of resolving the issue, following ancillary decisions for the issue was developed

· Identify strategies to improve customer satisfaction

· Plan suitable training to the customer service staff

· Implementing necessary software and adapt social media techniques to digitalize the admission procedures

· Develop costing for bringing the above

has expressed that for identifying suitable decisions for an issue, it is very inevitable to collect necessary data pertaining to the issue and make an analysis and this will give better clarity for the issue and facilitate effective decision making. In this study, the customer service quality has to be monitored for improvement. Hence before planning a decision, relevant data has to be collected and following key points have to be noted:

· The data should be right and valid to the identified issue

· The data should facilitate accurate conclusions

· The data should inform the decision making process

Right data and appropriate analysis and tools will lead to a simple and clear decision making process . To improve the data analysis and subsequent decision making process, following steps have to be performed.

Data driven Decision Making process

Figure 1: Source: Adapted from

Defining the question: As stated by Identifying the sources of data commences with the right question. The question should be designed in a way it qualifies potential solutions to the issue . In this study, the question is “What are the tactics to improve the students’ enrollment?”

Set Measurement practices: Firstly, what kind of data to be measured has to be decided. In answering the above question, following data have to be collected

· Number of employees engaged in direct admission

· Number of enquiries and number of admission in the previous years

· Details of training and working hours of employees of the concerned department

· Efficiency and effectiveness of the computer department including the software details

· Feedback from existing customers about the service of the university

Secondly, suitable measurement technique for the collected data has to be identified with the following key questions

· What is the time frame?

· What will be the unit measure?

· What factors are to be included such as enrollments, admissions, fees etc

Collection of data

Suitable sources for the data have to be determined. In regard to the data pertaining to previous years, the archives in the records office of the university can be gathered. A questionnaire or direct discussion with the employees provides information pertaining to employee involvement in the customer service. For the customer feedback, a questionnaire with questions relevant to their experience with the customer service of the University can be developed and distributed through messages, emails and social media.

Analyzing the data

The data gathered from the archives will help to make comparison with the existing data. Responses collected from employees and customers are recorded and fed in Pivot table of MS Excel . In this, the data is sorted and filtered on the basis of certain variables and helps to calculate mean, median and standard deviation. On manipulating this, exact data is identified and based upon the requirement for further manipulation, data collection is extended.

Interpreting the results

After analyzing the data, the results are interpreted and following key questions are raised to measure the outcomes:

How is the collected data relevant to the research question?

Does the data support any objections?

Is the conclusion drawn properly or are there any limitations?

The results of the data analysis process helps for effective decision making

In the perspective of customer service in Torrens University, data analytics involves collecting and gathering different information pertaining to customer service in order to obtain actionable insights in order to assess the strategies and design efficient customer service. The term data analytics is divided into 4 different types and aligned to decision making process.

Descriptive analytics: As said by Lepenioti, Bousdekis, Apostolou, & Mentzas, (2020) this facilitates the analysis in a comprehensive perspective of key metrics within the University. The real time data collected through questionnaire and historical data from archives of records are analyzed to develop significant insights for the challenge in regard to customer service. This is the basic type of analytics to determine the actual reasons behind any major issues. Through this descriptive analytics, the university can gain understanding on the past performance, initiate strategies on the basis of observations for the future outcomes and impact of the strategies on the current performance.

Diagnostic analysis:

The next phase of understanding the ins and outs of data analytics subsequent to descriptive analytics is diagnostic analytics. After evaluating the descriptive data, the analysts get into the problem deeply. By using the technique of drilldowns and queries, the major cause of the issue is eliminated . In this, the data collected from archives are ensured against any other data to uncover answer to “Why the customer service has declined”. Using Diagnostic analysis, Torrens University develops the capability of developing new idea to identify dependencies and distinguish prototypes. With this, the university is able to obtain better perception in regard to the problems and issues in customer service. Conversely, the information gathered through diagnostic analysis has to be maintained else data collection in future for any other purpose will become time consuming. To implement diagnostic analytics, Torrens University should have to be equipped with efficient and integrated Business Information dashboard with data assimilation, participating filters and drilldown capabilities.

Predictive Analytics: A good decision making lies on right predictions. This comprises of analysis of past patterns and trends and predicts the future outcomes accurately. Since this analysis uses the exact trends of the University, it helps in ascertaining the realistic goals for the decision making process. With this, it becomes easy for determining propensity and expectations of different customer groups and hence considered as a valuable tool. Moses, (2016) has expressed that this analysis implements various algorithms and statistical methods for predicting the probability of future outcomes. Contrary to this, Fortunny & Martens, (2013) has stated that this analysis does not provide 100% accuracy as the assumptions drawn are based on predictions. Many large organizations implements this approach to understand the customer behavior.

Prescriptive analytics: This relates to Big Data and Artificial Intelligence and major objective of this analytics is to recommend suitable action for addressing future problems. This helps Torrens to gain adequate understanding of the basic reasons for the problem and develop suitable action plan. The analytics uses combination of mathematical models, collected data and business rules . The data here refers to the historical data from archives, employee data and customer feedback. Business rules are the preferences, practices, procedures and other restrictions of the University. Operational research, statistics, machine learning and language processing are some of the mathematical models. This seems to be quite complicated but have huge impact on the operations and resolving the issues and develop growth.

Data Analytics

Figure 2: Source: Adapted from

Julmi, (2019) expresses that decision making is a natural activity based on the acquisition of knowledge and the result of this might be either reasonable or illogical. There are different reasons that influence the decision making process such as personality and experience of the individuals. The bias of individuals can either be a hindrance or enabler to the decision making process. The bias of the individuals is measured from the psychological perspective in terms of set of needs and preferences. Abraham Maslow Theory is the most significant theory on the basis of motivation. According to the Maslow motivational theory, the basic needs of individual has to be satisfied before desires and higher level needs.

A cognitive bias is a thinking error that happens when an individual or individuals are processing data and information around them and affect the decision making process . Cognitive bias is of four types and they are discussed below:

Actor-observer bias: This is the most common bias that is developed with the tendency to characterize own actions to external causes while linking other people’s behavior to internal causes.

Anchoring bias: This relates to the tendency of trusting the very first information received regardless of the genuineness and quality of the information. This bias can be used to set the preferences of others by putting the first information on the top of the table.

Availability Heuristic: This is a type of bias where the information comes to the mind is given higher value than others. Higher credibility is given to this information and be likely to misjudge the probability of same type of things in the near future.

Halo effect: The overall impression of an individual influences the way the character is perceived. Mostly the physical appearance influences the way the other qualities are rated.

Apart from the above there are also certain factors that contributes into the biases such as Emotions, Motivations, Mind ability, and social pressure. Multiple biases influences the thinking and subsequently affects the decision making process.

This report clearly elucidates the customer service quality in universities and educational institutions are slowly declining which will bring severe downfall in the industry. In order to maintain the reputation and market share, it becomes essential for Torrens University to plan suitable decision making process and initiate the decision of increasing the number of student enrollment. Data driven decision making is the technique that will be adapted to improve the customer engagement. Finally, the data analytics was implemented to inspect, cleanse, transform and model the data with the major aim of implementing efficient decision making process.

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Defining the question

What are the tactics to improve the students’ enrollment

Set measurement practices

Decide what to measure

Decide how to measure

Collection of data

Archives of records

Employee Feedback

Customer Feedback

Analyzing the data

Pivot Table in MS Excel

Interpreting the results

Measuring the outcomes with the Past performance

Develop Suitable decision

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