AusPaper: A Leading Australian Paper Product Manufacturer

AusPaper’s History and Operations

Pinnon Paper Industries is an Australian company famous for manufacturing paper products in the local area. AusPaper is a subsidiary to this Australian paper company. This company has a market span of local Australia as well as 75 other countries all over the globe especially in Europe, Africa, Latin America, USA, Asia, Middle East and also in the Indian Subcontinent (Box et al. 2015). The products of this company are exported to all these locations around the globe. A volume of 619,000 tonnes of paper products have been produced by AusPaper alone in the year 2013. They have sold more than 690,000 tonnes of their products to their overseas market as well as the local market in the year.

The company AusPaper has two different market segments. These are the newspaper industry and the magazine industry. Their contribution in the newspaper industry are with the highly notable newspapers such as The Australian Financial Review and the Herald Sun. On the other hand, the contribution of AusPaper in the magazine industry are with the big names such as Men’s Style Magazine, Homes and Gardens and many others. The products produced by the company are either sold directly to the customers or sold with the help of a broker. AusPaper is now interested to have a shift in the business from the paper industry to some other industries in the upcoming years. Though they are experiencing high financial turnovers and the business is running extremely successfully over the last 20 years, this thought of expanding the business have occurred to the owners of the company. As the preferences of the existing customers change from day to day, the company is expecting to earn even higher profits with this shift in the business. The increasing popularity for e-papers, instant news and magazines using social media as a medium have influenced the owners to believe that this change will be beneficial. This is the reason for which the management of AusPaper have expressed their interest towards building a strong customer base and strategic alliances with the existing clients of the magazine as well as the newspaper industry. Additionally, they are also interested to develop a model so that they can forecast the financial turnovers in the future quarters of the current financial year.

A survey has been conducted and the responses of the clients to the survey have been analyzed empirically so that the nature of the customers of AusPaper can be understood. Further, the perception of the customers towards the company and its products and also the likelihood with the help of which a long term strategic alliance can be developed with the company have been discussed in the following sections along with the findings of the analysis.

The Two Market Segments of AusPaper

In order to meet the requirements asked by AusPaper, a market research company named ANALYTICS7 have been hired. The analytics company have contacted with the existing clients of AusPaper and have asked them to fill up an online survey form. There was information about the key analyzing factors that will be necessary for the objectives of the analysis. Further, historical data on the annual sales turnover in four quarters have also been collected from the data warehouse of AusPaper in order to forecast the future sales.

Data have been collected from 200 customers on 18 variables or factors. This data is collected directly from the potential customers for the purpose of the research and thus this data is known as primary data. There are data on two different aspects in the primary dataset. The first aspect contained the perceptions of the clients on the performance of AusPaper. The performance of the company was rated on 13 different aspects by the clients. The performance was scaled on a ordinal scale of 0 – 10, with 0 representing “Poor Performance” and 10 indicating “Excellent Performance”. The second aspect of the data was the outcomes of the purchase and the relationship with the business. For example, satisfaction of the customers with the products of AusPaper and whether the responding firm of the client is interested in forming a strategic alliance with the company. This variable has responses with a binary scale of “Yes” or “No”. The other aspect involved the market size claimed by the client, the length of time the client has purchase relationship with the company and the sales turnover of the company recorded quarterly over the years and obtained from the data warehouse of AusPaper.

There are two dependent variables that has been considered in this study. These are customer satisfaction, which has been scored on an ordinal scale of 1 – 10 and the interest of the client whether to form a strategic alliance with AusPaper or not, which is binary variable recording two responses “Yes” and “No”.

The summary of the data has been illustrated in the following figures 1 and 2. From table 1, it can be seen that the average satisfaction rating has been given to be 6.952. The observed median of the data is 7.05, which indicates that 50 percent of the satisfaction ratings are above 7.05. The standard deviation of the ratings given by the customers is 1.2411 which is very less and indicates that the ratings given by the customers are close to the average ratings (Draper and Smith 2014). It can also be seen from figure 1 that most of the customers have given ratings in the range of 7 and 8. The frequency distribution table is attached in the appendix section (Table A1).

Table 1: Customer Satisfaction
Mean	6.952
Standard Error	0.088
Median	7.05
Mode	5.4
Standard Deviation	1.241
Sample Variance	1.540
Kurtosis	-0.769
Skewness	0.090
Range	5.2
Minimum	4.7
Maximum	9.9
Sum	1390.4
Count	200

Forecasting a Shift in Business Climate

From figure 2, it can be seen that 43 percent of the clients are interested to form strategic alliance with AusPaper and 57 percent of the clients are not interested to form strategic alliance. Thus, it can be said the interest of the clients to form strategic alliance with AusPaper is more negative than positive, though there is not much difference in the two percentages (Chatterjee and Hadi 2015).

There are a lot variables in the dataset that has been collected from the potential clients of AusPaper. In the prediction of customer satisfaction, all these variables might not be significant and the model will become extremely messy with all the variables included. Thus, in view of developing a relevant model, all the independent variables have been used to predict the customer satisfaction with the help of regression and the insignificant variables have been eliminated from the final model development. The independent variables for which the p-values are higher than the level of significance (0.05), are considered as the insignificant variables. From the analysis, it has been observed that the variables such as the extent to which technical support is offered to help solve product/service issues, extent to which any complaints are resolved in a timely and complete manner, Perceptions of AusPaper advertising campaigns in all types of media, Depth and breadth of AusPaper product line to meet customer needs, Extent to which AusPaper offers competitive pricing, Extend to which AusPaper stands behind its product/service warranties and claims, Extent to which AusPaper develops and sells new products, Perception that ordering and billing is handled efficiently and correctly, Perceived willingness of AusPaper sales reps to negotiate price on purchases of paper products and Amount of time it takes to deliver the paper products once an order has been confirmed are considered insignificant as the p-values are higher than 0.05 (Kleinbaum et al. 2013) . The remaining variables such as the product quality, Overall image of AusPaper’s website, especially user-friendliness and Overall image of AusPaper’s salesforce have been considered significant for the model.

A regression analysis has been conducted considering the variables identified as significant in the previous section. Thus, the prediction model with the three identified significant variables to predict the customer satisfaction is given as follows:

Customer satisfaction = -0.141 + (0.534 * Product Quality) + (- 0.295 * Ecommerce Activities) + (0.760 * Salesforce Image)

Table 2: Regression Coefficients Table for the Prediction Model

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	-0.141	0.462	-0.305	0.761	-1.051	0.769
Prdct_Qual	0.534	0.042	12.678	0.000	0.451	0.617
E_Comm	-0.295	0.122	-2.411	0.017	-0.536	-0.054
Image	0.760	0.084	9.064	0.000	0.594	0.925

Market Research Conducted by ANALYTICS7

It can be seen from the ANOVA table given in table 3 that, the significance value of the model is 0.000, which is less than the level of significance (0.05). This indicates that the prediction model is significant. Further, from the value of R Square given in table 4, it can be observed that the R Square value is 0.58, which indicates that 58 percent of the variability in the customer satisfaction ratings can be explained by the independent variables Product Quality, Ecommerce activities and Salesforce Image. A higher value of R Square indicates a better model fit. Thus, the model developed here can be considered as reasonably good (Montgomery, Jennings and Kulahci 2015).

Table 3: ANOVA Table Stating Significance of the Model

	df	SS	MS	F	Significance F
Regression	3	177.122	59.041	89.415	0.000
Residual	196	129.418	0.660
Total	199	306.539

Table 4: Regression Statistics
Multiple R	0.76
R Square	0.58
Adjusted R Square	0.57
Standard Error	0.81
Observations	200

In a separate analysis conducted by Hugo Barra, it has been determined that the depth and breadth of AusPaper’s product line has been a significant predictor variable to predict customer satisfaction. With support to his findings, prior research has been conducted and the research has shown that there has been variation in the relationship between product line and customer satisfaction on the basis of the location of the customer. This indicates that there are differences in the needs and preferences of the customers from the global market than from ones in the locality such as ANZ region. Thus, the interaction effect of product line and location of the customers have been incorporated in the model and the effect has been evaluated.

The R Square value from the developed regression model have been obtained to be 0.503. This indicates that 50.3 percent of the variability in the customer satisfaction can be explained by this model (Chatfield 2016). The regression model can be specified as:

Customer Satisfaction = 8.080 + (- 0.2448 * Product Line) + (- 2.927 * Region) + (0.555 * Product Line * Region)

The significance value obtained as a result of the ANONA is 0.000, which is less than the level of significance (0.05). Thus, it can be seen that the model developed is significant. The regression tables and the ANOVA table are given in tables 5, 6 and 7.

Table 5: Regression Statistics
Multiple R	0.71
R Square	0.50
Adjusted R Square	0.50
Standard Error	0.88
Observations	200

Table 6: ANOVA
	df	SS	MS	F	Significance F
Regression	3	154.179	51.393	66.113	0.000
Residual	196	152.360	0.777
Total	199	306.539

Table 7: Regression Coefficients

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	8.08	1.46	5.533	0.000	5.200	10.961
Prdct_Line	-0.24	0.22	-1.106	0.270	-0.682	0.192
Region	-2.93	0.79	-3.702	0.000	-4.487	-1.368
Region*Productline	0.56	0.12	4.471	0.000	0.310	0.800

It can be seen from table 7 that product line has been insignificant variable in the prediction model. On the other hand, the interaction variable has been found to be significant. Thus, the effect of product line has been considered through the interaction variable.

Variables Considered in the Prediction of Customer Satisfaction

In this section inference will be made on the likelihood of a client in choosing or not choosing strategic alliance with the company AusPaper. The clients who have rated neutral towards the image of the products of AusPaper have been have been given special attention (Cameron and Trivedi 2013). In order to simplify the analysis, the ratings given by the clients have been rounded to the nearest whole number.

Considering the ratings to be neutral on product line and image, it can be seen that customers giving product quality ratings less than 8 have not opted for strategic alliance. Opting for strategic alliance increases gradually as the ratings increase. This can be seen clearly from figure 3.

Again, considering the ratings to be neutral on product line and image, it can be seen that customers giving price flexibility ratings more than 7 have opted for strategic alliance. This can be seen clearly from figure 3.

From the available historical data on AusPaper available from the company warehouse, a time series analysis has been conducted to forecast the turnovers in the 2^nd, 3^rd and 4^th quarters of the financial year 2017. Two different methods have been used to forecast the future turnovers. One of them is the moving average method and the other is the exponential smoothing method (Brockwell and Davis 2013).  From the analysis, it can be seen that the exponential smoothing method have given a better fit to the data than the moving average method and thus, exponential smoothing will be used as the forecasting method. The predicted sales ($‘000) on the second quarter of 2017 is $6114.52, in the third quarter of 2017 is $5500.63 and in the fourth quarter of 2017 is $5807.58.

Conclusion

In this paper summary has been provided on customer perception ratings, company performance metrics, scopes for new alliances and sales turnover of the paper manufacturing company AusPaper. The delivery time of the product quality, brand image and product line has been identified as the key influencing factors for customer satisfaction. Any improvement of these factors lead to a positive change in the satisfaction level of the customers. In addition to this, region was also found to influence the effect of product line on the satisfaction of customers. Market diversification on a global scale is a key influencing factor affecting relationship between sales of the product and customer satisfaction. The result holds irrespective of whether the relation is done directly but the company (in that case, dummy variable = 1), or indirectly through intermediation of brokers. Finally, it has been found that the exponential smoothing method has been a better forecasting tool than the moving average method.

References

Box, G.E., Jenkins, G.M., Reinsel, G.C. and Ljung, G.M., 2015. Time series analysis: forecasting and control. John Wiley & Sons.

Brockwell, P.J. and Davis, R.A., 2013. Time series: theory and methods. Springer Science & Business Media.

Cameron, A.C. and Trivedi, P.K., 2013. Regression analysis of count data(Vol. 53). Cambridge university press.

Chatfield, C., 2016. The analysis of time series: an introduction. CRC press.

Chatterjee, S. and Hadi, A.S., 2015. Regression analysis by example. John Wiley & Sons.

Draper, N.R. and Smith, H., 2014. Applied regression analysis. John Wiley & Sons.

Kleinbaum, D., Kupper, L., Nizam, A. and Rosenberg, E., 2013. Applied regression analysis and other multivariable methods. Nelson Education.

Montgomery, D.C., Jennings, C.L. and Kulahci, M., 2015. Introduction to time series analysis and forecasting. John Wiley & Sons.