About AusPaper
The “Aus Paper” is actually a subsidiary of the “Pinnon Paper Industries”. The Australian Company “Pinnon Paper Industries” possesses a long history in the local production. The industry is seen to produce paper only. The subsidiary sells products to two major industries including the two industries namely the newspaper and magazine industry. “Herald Sun” and “Australian Financial Review” are the two industries which receive the paper products. The magazine industries that get the paper products are “Homes Gardens” and “Men’s style magazine”. Paper products are sold to the customers either directly or indirectly.
Despite the fact that the subsidiary has achieved immense success over the last few centuries the organisation is eying a shift in the coming years with respect to the business environment. The management is of the opinion that the need for ensuring a stable customer base and solid strategic alliance with the consumers of the industries is essential.
In this report the contracted firm managers are reported and the gathered data is given along with certain amount of assembled information. The data is tabulated with respect to the sales of the AusPaper warehouse. The sales are manageable by means of the decision support system.
There are 200 perceptions along with the 18 factors which are included in the data file. It is seen that majorly two types of data are included in the required database. The first is concerned with the performance of the organisation and this is on the 13 characteristics which are measured with the help of the 0-10 scale. This is used to imply that 0 is poor and 10 is excellent. The other information is linked to include certain outcomes and business connections. This can include the number of consumers and the length of purchase association and also the quarterly turnover operations of the “AusPaper”.
“The analysis with respect to the assistance of the considered factors is concerned with the clarification of the consumer loyalty with the firm operations. The information is aimed at discovering the major factors helpful for the estimation of satisfaction of the customers. “AusPaper” customers building strategic associationwith the firm. The report analytically focuses to develop a predictive model to anticipate the turnover of AusPaper in the 2nd, 3rd and 4th quarters of 2017. For the forthcoming financial year 2017, the analysis would demonstrate whether “AusPaper” would be in a decent position or not. The researcher clarified his objectives from the analysis to summarise the major research questions.”
- “The average customer satisfaction with past purchases from “AusPaper “is found to be 6.95”.
- “The standard deviation indicates the spread of the distribution of customer satisfaction with purchases from “AusPaper” that provides the value 1.24”.
- “The median of the satisfaction data set is 7.05”.
- “The measure of location states that 25% of the bottom values are less than 6 and 25% of the top values are more than 7.9”.
- “The mode is 5.4 that indicates that the frequency is found highest at the time customer satisfaction rate 5.4”.
- “The minimum customer satisfaction of previous purchases from “AusPaper” is 4.7 and maximum customer satisfaction of previous purchases from “AusPaper” is 9.9”.
- “Hence, the customer satisfaction with previous purchases from “AusPaper” ranges between 5.2”.
- “The customer satisfaction level of slightly rightly and positively skewed. The graphical visualization indicates that its left tail is longer than its right tail”.
- “The frequency distribution and frequency table of the variable “Extent to which the customer/respondent perceives hisor her firm would engage in strategic alliance/partnership with AusPaper” determines that out of 200 samples, 114 samples incurred thestrategic alliance or strategic partnership”.
- “On the other hand, 86 people conveyed that they are engaged in strategic alliance or relationship. The percentage share of these two cases are 57% and 43% respectively”.
There are fifteen samples which are chosen for the purpose of analysis. The tabulated samples are attached in the last section namely the appendix at the end of the assignment. “These chosen variables are – Cstmr_Type, Strat_Alliance, Prdct_Qual, E_Comm, Tch_Supp, Cmplnt_Supp, Advert, Prdct_Line, Image, Pricing, Warranty, New_Prdct, Billing, Price_Flex and Delvry_Flex.“Sats” is assumed to be a dependent variable. All the variables except“Sats” are assumed to be independent variables.” The analyst in this case is interested in building a suitable predictive model with a single dependent and several independent variables.
It needs to be noted that in this specific analysis the value of R-square is not authenticated. This implies that there is a chance for it to contain a certain amount of multi-co linearity. Regression analysis is a powerful statistical tool which helps in the examination of the relationship between two or more variables which are to b understood and analysed. This analysis is important for providing detailed into the method of research.
Market Segments
It is known that the method of finding the association among the variables along with the goodness of fit comes under the adjusted R – square calculations and interpretation. It is clearly seen that there are certain variables which are significant out of all the variables as the values of the values of the variables are greater than 0.5. These variables are Cstmr_type, Strat_alliance, Prdct_quality, Cmplnt_res, prdct_line, billing and delivry_speed.
It is known that negative association is used to indicate that with the decrease in the values of the independent variables the values of the dependent variables increase and vice versa.
The analyst in the next part of the analysiscaused that the depth and breadth of “Product line” of “AusPaper” is a significant estimator of the variable “Customer Satisfaction”. The previous analysis referred that the strength of this association may vary according to the location of customers.
Among 200 consumers, 81 customers are from in Australia and New Zealand. 119 consumers are from outside Australia and New Zealand. Three multiple regression models are executed with the help of four variables that are product line, region, customer satisfaction and interaction effect of region and product line.
“The very first multiple linear regression model is build assuming“Product line” as predictor variable and “Customer satisfaction”as dependent variable. The analyst for the regression calculation transformed the variable “Region” as binary variable. Here, 0 = Outside ANZ and 1 = ANZ region. The second multiple regression model is concerned with two “Region” and “Product line” and there is one dependent variable, “Customer satisfaction’. The third multiple regression model has considered the interaction variable of “Region” and “Productline” as a new predictor variable. The interaction variable is calculated multiplying two predictor variables of model 2 “. The predictor variables are “Region”, “Product line” and “Interaction effect”. The dependent variable is as usual as “Customer satisfaction”. There is a help in the representation of the variables with the help of the multiple regression models.
“In the first predictive model product line is linearly, positively and significantly associated with customer satisfaction with co-efficient 0.608915 andsignificant p-value = 0.0.In the second predictive model, product line and Region both are linearly, positively and significantly related with customer satisfaction. Product line whose co-efficient is 0.713734 and significant p-value is 0.0 and Region whose co-efficient (-0.54615) andsignificant p-value is 0.0005. In the third multiple regression model, all the three variables Product line, Region and Interaction are linearly, positively and significantly associated with “Customer satisfaction” where product line has co-efficient is 0.865218 and p-value is 0.0. Here,“Region” has co-efficient 2.927282 andsignificant p-value is 0.0003 and “Interaction” has co-efficient has(-0.55504) and significant p-value is 0.0.Thus, among the three variables ‘Product line’, ‘Region’ and the ‘Interaction effect’, ‘Product line’ and ‘Region’ have linear significant influence on the dependent variable ‘Customer satisfaction’.”
“The first regression model which is simple regression model has maximum“AIC” value and least p-value among three models. Therefore, the model is best fitted.In this case, the effect of “Region” and“Interaction” effect is not present.”
The most important analysis is regarding the understanding of a predictive model which is advanced and this utilizes major variables which impact the chance of impacting a strategic relation to the partnership of the “AusPaper”. There are five factors which are taken into consideration in the advanced model building analysis namely “Product Quality”, “Product Line”, “Personnel Image”, “Flexibility” and “Competitive Pricing”.
“Personnel Image” and “Product Line” are the predictor variable and “Strategic Alliance” is dependent variable in the predicted logistic regression model. The predictive regression model could be stated as-
Prob. (Strategic Alliance)
The p-values of two predictor variables in this logistic regression model are “Product Line” and “Personnel Image” are both 0.0. The p-value is much less in comparison to the level of significance. Both the variables are significant. The level of significance in this connection is 5%.
“Product Quality” and “Price Flexibility” are predictor variables and “Strategic Alliance” is dependent variable in the predictive regression model. It is given as-
Prob. (Strategic Alliance)
The calculated p-values of two predictor variables in the logistic regression model “Product Quality” and “Price Flexibility” are 0.0 in both cases. Both the predictor variables are significant at 5% level of significance. It is seen that the calculated values are less than the level of significance.
The method of forecasting is used for making the predictions of the future on the basis of past and present data by method of trend analysis. This is suitable in statistical analysis and helps in the forecasting of data on the basis of the provided information.
To predict the future turnover amount, the analyst considered “Time” as explanatory variable and “Turnover($’000)” as response variable. The variable “Turnover ($’000)” indicates for total turnover amount within the time span. The variable “Time” isactually the chronological frequency of quarters starting from 1st quarter of 2008 to 1st quarter of 2017.
It is seen that the estimated turnover amounts of the 2nd, 3rd and 4th quarters of 2017 are $4531.955, $4991.969 and $5043.73.
It is seen that the customers of the subsidiary have a significantly high level of satisfaction. Certain variables namely the quality of the product, the region, the effect of interaction, the price flexibility have a noteworthy influence on the dependent variable which is that of the strategic alliance. The discussion is clearly indicative of the fact that there is a predicted possibility of the creation of a strategic alliance with differing levels of the product quality and product flexibility. The continuous proportion to the line of the product and the image of the people are also asserted. Thus, it can be stated that, “quarterly turnover amounts of 2nd, 3rd and 4th quarters are greater in 2017 than any other quartiles of previous year”. Similar to the 1st quarter, the turnover amounts of other quarters also have grown in a significant manner.
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