1. Providing an overall summary of two outcome (dependent) variables:
One variable captures the intention to repurchase Furphy beer. The other one indicates whether a client would recommend Furphy to others.
2.1. Identifying potential variables that may influence the intention to repurchase Furphy beer.
An appropriate statistical technique could be used here to identify a list of possible predictors.
2.2. Building and finalising a model (through a model building process) to predict the intention to repurchase Furphy Beer.
2.3. Todd has done a separate analysis and found that perception of beer quality is a significant predictor of repurchasing intentions. In line with his findings, prior research shows that the strength of this relationship may vary according to brand image. That is, customers tend to associate brand image with product quality. Therefore, Todd believes that the relationship between quality and repurchase intention should be stronger for those who have a more favorable perception of a brand.
Your task here is to test Todd’s assumption by modelling the interaction between the above-mentioned predictors and the target variable.
3.1. Building and finalising a model (through a model building process) to predict the likelihood of recommending Furphy to others. Todd would like to gain a deeper understanding of the likelihood of recommending the Furphy product to other customers. He is specifically interested in understanding the probability of clients who meet the following criteria to recommend Furphy to others.
a) Feel neutral (i.e. score of 5 on the relevant scale) towards Furphy’s speed of delivery;
b) With varying levels of perception towards product quality (i.e., scores from 1 to 10) and brand image (scores of 1=negative, 5=neutral, and 10=positive);
c) And across two customer segments: 1) those who purchase directly; and 2) those who purchase through a sales representative.
Todd believes that quality of the product and brand image define Furphy’s success in being recommended. Therefore, it is important for Furphy to know whether effort and money should be put in improving perceptions of product quality and brand image to increase the probability of being recommended.
Accordingly, your job is to visualise the predicted probability of being recommended to others by customers with attributes described above.
4. Developing a time-series model to forecast Furphy production of pale ale in the next four financial quarters.
5. Produce a written report detailing ALL aspects of your analysis. Your report should be as detailed as possible and should describe ALL key outputs of your analysis. Make sure to provide recommendations to Furphy’s management that will guide them to improve their customer relationships management. Your recommendations / insights should be driven by the results of your analyses.
To accomplish allocated tasks, you need to examine and analyse the dataset (Furphy.xlsx) thoroughly. Below are some guidelines to follow:
Task 1 – Summarising Dependent Variables
The purpose of this task is to analyse and explore key features of these two variables individually. At the very least, you should thoroughly investigate relevant summary measures of these two variables. Proper visualisations should be used to illustrate key features.
Your technical report should describe ALL key aspects of each variable.
Task 2.1. – Identifying relevant factors for predicting repurchasing intention
Analyse the relevant dependent variable against other variables included in the dataset. Your job is to decide which variables to include here. Use an appropriate technique to identify important relationships.
The outcome of this task is a list of variables that should be included in the subsequent analysis.
Your technical report should describe why some variables were selected while others were dropped from subsequent analyses.
Task 2.2. – Model building (predicting repurchasing intention)
You should follow a model building process. All steps of the model building process should be included in your analysis. You can have as many Excel worksheets (tabs) as you require to clearly demonstrate different iterations of your predictive model (i.e., 2.2.a., 2.2.b., 2.2.c. etc.).
Your technical report should clearly explain why the model may have undergone several iterations. Also, you must provide a detailed interpretation of ALL elements of the final model.
Task 2.3. – Interaction effect
To accomplish this task you need to develop a regression model using ONLY the factors discussed in the meeting (Task 2.3). In other words, this section of analysis is separate from the regression model constructed in Task 2.2.
Your technical report should clearly explain the role of each variable included in the model. A proper visualisation technique should be used. Make sure you interpret all relevant outputs in detail and provide managerial recommendations based on the results of your analysis.