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  • Subject area(s): Marketing
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  • Published on: 14th September 2019
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Business Understanding

Introduction

AIH is about focusing over the financial standards which includes how the students are able to handle without the use of the government sponsored programs. For this, there is a need to make sure of the financial programs which includes the different financial institutions and how AIH can work towards the different source of funds. For this, the university also focus on the forms which include the marketing image to the community about enrolling the people in different courses and the degrees at AIH. This can help the students with the increased revenue from the enrolments with the increased marketing image. This works over the data mining and the predictive analysis that is put to offer for the different courses. The forms are related to make sure of the enrolment where the risks are also associated for the acquiring of the bad debts from the scheme with the causing of the financial hardships for the students and the university. One need to provide with the avenue that includes the studying and working over at the time of academic semesters. The issues are related to whether the plan will be adapted by the different people or not.

What the company do?

Being the data miner, my focus is on determining the processes for the centralisation with the easy collection of the data from the different resources. It is mainly to make sure of the database which have been linked along with handling the responsibilities which are important to help the business professionals as well. My main work for the AIH university is to make the decisions for the plan that needs to be pursued with the focus on whether the data should be analysed or not. This is important for creation of the reports with the easy reviewing of the data patterns as well.

Determine the Business Objectives

AIH is considering about the current financial assistance where the major objective is about the handling of the low interest finance to encourage the people to enrol and work on the different courses and the degrees. AIH is focusing on providing the competitive advantage which includes the universities and the private institutions that tend to offer the similar courses. The focus is on offering the assistance for the student fees and then provide the avenue for the students to study more and work less depending upon the academic semester.

Problem Area

The problem is based on the business development, where there is a need to identify the compeititve advantage with the focus on how the data mining will help in handling and achieving the compeititve advantage. The designing and implementation is for the decision support system for the enrolment management which is set for the predictive model and the user-friendly interface that allows the university to dispense with the services of outside consultants with the operational gains. The two-phase implementation is provided for the growth to understand the process of admission and then handling the different development processes using predictive models. The data mining needs to be induced in AIH and make sure of the improvement in the business parameters as well. The report for the top management is required as AIH is considering that they can add the people of different age. They are encouraging the people like senior citizens, professionals and the other low-income earners for the improvement to study without any worrying about the costs. The methodologies are depending upon the deployment which contributes towards the operational success of the system at the time of designing and the implementation phase.

Business Objectives

The enrolment management tool is developed in the environment which is based on how the principles are recognized based on the decision support system literature. A proper development of the DSS systems works with the management with the implementations that includes the issues related to the planning, budgeting and the resource allocation problems. The use of the multiple linear, logistic and the profit regression models are considered important for the forecasting at this level. The lower fees are mainly to handle the system functioning with the identification that there is no loss of the customers at any level.

Business Success Criteria

We can make use of the predictive analysis which is for creating the models and then predicting the future trends with this. This type includes the statistics, modelling and the machine learning. The leverages are depending upon the increased capacity with the institutions that can understand about the success and the effectiveness system as well.

It includes the increased technological advancements with the powerful computers that will help in improving the storage of the data and proliferation of the larger sets of the data. through this, the access is possible with the stronger data manipulation tools which includes the examination of the data across the different points.

1.2 Assess the situation

Activities: Inventory Resources

There is a possibility of different projects which include the data experts and the business, data mining personnel who will be able to handle the operations of the data with the different data mining tools. Through the predictive analytics, there is a possibility to check the enrolment forecasting and the student elasticity studies very effectively. The predictors of the retention are based on how the central and the complex issues are set with the institutional planning depending upon the higher rates of attrition where the people suffer from the reputational costs.

Activities: Sources of data and knowledge

Considering the data sources, they are the online sources of the experienced people, documentation of people on the data mining tools and how the experts can handle the relevant backgrounds with the informal standards. The knowledge sources are depending upon the documentation process which includes the analytics related to the specific student tracking studies. The forecasting approach is also effective to handle the different forms which forecast the importance to the admission departments where the regression analysis will help in the modelling and the analysis of the different variables. The focus is on the dependent variable and the independent variable to understand about the variations and the conditional expectations. The estimate target is depending upon the prediction and the forecasting that helps in analysing about the ordinary least squares and the regression functions. It is also important to understand the independent variables with respect to the dependent variables where the performance is related to the data generating process and how it relates to the system setup which is based on the observational form of the data patterns. .0

Activities: Requirements, assumptions and constraints

The requirements are based on scheduling the completion process with the quality of results. It is based on:

The requirements are based on how the predictive analytics will help in encouraging the senior citizens, homemakers and the low-income earners to handle the costs which is associated with the studying for the degree. The regression model also includes the different parameters and the variables which are related to administer about the unbiased, consistent and the efficiency class of the linear estimators. It is important to focus on the measures of how the independent and the dependent variables can refer to the values which are measured at the different point locations. The variables also include the aggregation by the areas which includes the aggregation by the political boundaries. The categorical standards are based on examination of the predictor variables to work on the jobs with the use of the predictors that use the accounting for the variability with the changes depending upon the relationship that includes the causal analysis, forecasting and the trend forecasting. The regression could be for identifying the strength of the effects where the effects, sales and the marketing, expenditure, age and income. The forecasting effects are depending upon the impact of the changes with the forms related to the ordinal regression, multinomial and the discriminant analysis.

Assumptions

The assumptions are related to the selection of the model for the analysis which is considered to increase with the explained patterns. The addition of the values is mainly based on the variables and the random effects which works with under fitting. The regression analysis is biased that includes the different variables like the choice of the people to reduce the effective strength of the independent variables and then work on the empirical forms. The effectiveness of the model is mainly to take hold of the effective strength of the independent strength and then using the fitting methods that are set when the linear regression could be used for the cause-effect relationship. The regression is based on the analysis using the finance, investing and the other attempts which are based on the relationship between the dependent variables. The regression helps in the management of the value assets and then work with the prices and the stocks of the business. The linear regression and the multiple linear forms are set with the complicated data and the analysis. It includes the forms where the linear regression makes use of the independent variable mainly for the explanation and prediction of a better outcome.

Constraints

The regression can be helpful for the finance and the investments, where it is possible to predict the sales and work on the significant relationship with the major strength of impact. The analysis is also based on comparison of effects with the measurement on different scales that are important for check on the effect of the price change and the other promotional activities.

Figure: Contingency Plan

1.3 Determine data mining goals

Task: Determine data mining goals

The major focus is on the increment of the catalogue sales which are important for the category of the existing customers. This will help in improving the client number and assurance that the customers will enrol more and more in the university, with the relevant growth and the demographic information, depending upon the courses that are being offered to them. The marketing and the campaigning requires to measure the different segmentation process which is depending upon the levels and the size related to the specific patterns. The problem type is prediction as in the universities, we can only check depending upon how many people are coming and getting themselves registered.

 

References:

1. Jacobsen, J.P., Levin, L.M. and Tausanovitch, Z., 2016. Comparing Standard Regression Modeling to Ensemble Modeling: How Data Mining Software Can Improve Economists’ Predictions. Eastern Economic Journal, 42(3), pp.387-398.

2. Kempler, S. and Mathews, T., 2016. Earth Science Data Analytics: Bridging Tools and Techniques with the Co-Analysis of Large, Heterogeneous Datasets.

3. Manuel, J., Cordeiro, R. and Silva, C., 2017, September. Between Data Mining and Predictive Analytics Techniques to Cybersecurity Protection on eLearning Environments. In Proceedings of the Computational Methods in Systems and Software (pp. 185-194). Springer, Cham.

4. Martens, D., Provost, F., Clark, J. and de Fortuny, E.J., 2016. Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics. MIS quarterly, 40(4).

5. Roiger, R.J., 2017. Data mining: a tutorial-based primer. CRC Press.

6. Salehan, M. and Kim, D.J., 2016. Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, pp.30-40.

7. Shmueli, G. and Lichtendahl Jr, K.C., 2017. Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. John Wiley & Sons.

8. Witten, I.H., Frank, E., Hall, M.A. and Pal, C.J., 2016. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.

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