Impact of Factors on Purchasing Initiative

This assignment delves into the factors that influence consumer purchase decisions, focusing specifically on a hypothetical 'Purchasing Initiative'. Students analyze survey data related to consumers' ratings of the initiative and their willingness to recommend Furphy Beer. The analysis involves employing regression techniques to identify significant independent variables impacting purchasing behavior, exploring both statistically significant and manager-identified variables. The final output includes tables presenting regression statistics for different models incorporating various independent variables.

Running Head: DESCRIPTIVE ANALYTICS AND VISUALISATION
MIS771
Descriptive Analytics and Visualisation
Name of the Student
Name of the University
Author Note

1DESCRIPTIVE ANALYTICS AND VISUALISATION
Table of Contents
1.0 Introduction................................................................................................................................2
2.0 Description of the Data..............................................................................................................2
3.0 Data Analysis.............................................................................................................................3
3.1 Dependent Variable Analysis................................................................................................3
3.2 Variables Influencing Repurchase.........................................................................................5
3.2.1 Identification of Variables..............................................................................................5
3.2.2 Prediction with the Identified Variables.........................................................................6
3.2.3 Separate Analysis............................................................................................................6
3.2 Recommending Furphy.........................................................................................................7
3.3 Time Series Analysis.............................................................................................................8
4.0 Conclusion and Recommendations............................................................................................9
REFERENCES..............................................................................................................................10
APPENDICES...............................................................................................................................11

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1.0 Introduction
An Australia born micro – Brewery Company named Furphy Beer has an experience of
less than fifteen years in the brewing ale. The company has limited sales in Melbourne and
reginal Victoria but is fast expanding in the rest of the parts of Australia in terms of its
production and sales. This growth has been quite significant in the previous two years. The
company has met its increasing demand in its pale ale beer by increasing its brewing capacity to
3 million litres per year. Their products are usually sold in pubs, bars, restaurants and
bottleshops. The products are sold either directly or by a sales representative.
Though they have been successful in these operations and have experienced a good
amount of financial turnovers over the years, they want to change the business to a new direction
in the upcoming five years. This decision was made because of the increasing popularity of the
brand. Thus, the management now is in need of a good relationship with its customers. They are
also planning to forecast their production of beer in the next four quarters. With the help of this,
the company will be able to understand their future demand and supply. Thus, they can plan their
needs of production accordingly. To do this analysis, the company has asked a market research
company named BEAUTIFUL DATA. The main aim of the company is to analyze the sales of
the brewery company Furphy, understand the customers’ characteristics and their conditions for
going for a repurchase and to predict the future sales for the next four quarters. The analysis will
be done using the appropriate statistical techniques.
2.0 Description of the Data
To analyze the situation and conditions described in the previous section, the company
has made contact with another market research company named Beautiful-Data. This market

3DESCRIPTIVE ANALYTICS AND VISUALISATION
research company in order to conduct the analysis has collected data from the clients of Furphy.
The clients to fill up an online survey form. 200 valid responses were considered from all the
responses received for the analysis. The dataset consist of 9 characteristics of the perceptions of
the customers. The perceptions of the customers were recorded in a scale of 0-10. Other
variables include the information on purchase outcomes and business relationships. Furphy’s
data warehousing provided the market research company with another type of data. This data
includes information of the quarterly beer production. The analysis will be done using the
statistical software MS Excel.
3.0 Data Analysis
3.1 Dependent Variable Analysis
The two dependent variables considered in this case are the “customers’ intentions to
repurchase products from Furphy” and “whether a client would recommend Furphy to others”.
From the analysis it is clear that most of the clients of the company have given ratings between 7
and 8. Most of the clients have given high ratings to the product. Thus, it can be said that most of
the people go for repurchasing the product. Figure 3.1 shows the distribution of opinions of the
clients for repurchasing intensions of the clients.

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Figure 3.1: Distribution of ratings for repurchasing intensions of the clients
The second dependent variable that has been considered is recommendations. It can be
seen from the data collected that most of the clients recommend Furphy to others. From this data,
it can be seen that the percentage of clients who recommend Furphy is very close to the
percentage of people who do not recommend Furphy. Thus, it is not right to conclude anything
from this information derived in this section. The information has been expressed graphically in
figure 3.2.
Figure 3.2: Recommendations given by Clients

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3.2 Variables Influencing Repurchase
In this part, the identification of the potential variables influencing the intention to
repurchase Furphy Beer has been done. For the identification purpose, regression analysis has
been used.
3.2.1 Identification of Variables
In the given dataset on Furphy Beer, a lot of variables are included. It is important to
identify which variables are more important in influencing the intention to repurchase the brand
of beer. To check this, a regression analysis has been done considering all the other variables
included in the dataset predicting the dependent variable repurchasing intention (Draper and
Smith 2014). The regression analysis will show very clearly which of the variables are
significant in interpreting the dependent variable and which are not significant (Montgomery,
Peck and Vining 2015). The regression analysis will give the p-values (significance values) of all
the independent variables (Chatterjee and Hadi 2015). The test will be done at 95 percent level of
significance. Now the variables with p-values less than 0.05 (95 percent level of significance) are
termed as significant variables and with values greater than 0.05 are termed as insignificant
(Kleinbaum et al.2015).
Here, the variables such as Loyalty, type of customer purchasing the product, the
customer location and how the Furphy’s products are distributed have all been decoded to run
the regression analysis. For “customer loyalty”, less than one year have been denoted as 1,
between 1 to 5 years have been denoted as 2 and longer than 5 years have been denoted as 3. For
The variable “customer type” the response Bottle Shops have been recorded as 1 and Pubs, bars
and restaurants have been denoted as 2. For the variable “customer region”, Melbourne has been
denoted as 1 and Outside Melbourne has been denoted as 2. Then regression analysis has been

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done on all the 13 independent variables to predict the dependent variable repurchasing intention
of Furphy Beer.
From the analysis (Table 5 in Appendices) it can be seen that the P-values of the
variables Loyalty, Distribution Channel, Quality and Brand Image of the product are less than
0.05. Thus, these are the only significant variables in predicting the repurchasing intention of the
product.
3.2.2 Prediction with the Identified Variables
To predict the intention to repurchase Furphy Beer, regression analysis has been done
again with the significant variables only. From the analysis it is clear that the identified variables
can predict 44 percent (R Square value, Table 6, Appendices) of the repurchasing intention of the
customers to buy the beer (Cameron and Trivedi 2013). The predicted value or rating for the
repurchasing intention can be given by the following relationship. The relationship is obtained
from table 8 (Appendices).
Repurchasing Intention = 5.31 + (0.42 * Loyalty) – (0.29 * Distribution Channel) + (0.12 *
Quality) + (0.21 * Brand Image).
3.2.3 Separate Analysis
It has been said that the research team manager has done a separate analysis and found
out that the perception of beer quality is a significant predictor of repurchasing intentions. With
his findings, it has been noticed that customers has a tendency to relate brand image and product
quality. Thus, according to the manager, a good prediction can be made for the repurchasing
intention with these two variables. The results of the analysis show that brand image and
perception of beer quality can predict 34 percent (Table 9, Appendices) of the repurchasing

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intentions. It can be seen from the P-value (Table 11, Appendices) that both the variables quality
and brand image are significant to predict the repurchasing intentions. The relationship can be
given by
Repurchasing Intentions = 3.59 + (0.31 * Quality) + (0.31 * Brand Image)
3.2 Recommending Furphy
The research team manager now wants to predict the likelihood of recommending Furphy
to others. The specific interest of the manager is to understand the probability of clients who feel
neutral about Furphy’s speed of delivery, with varying levels of perception towards product
quality and brand image and clients who purchase directly and purchase via sales representative.
To understand this likelihood, the ratings given by the clients have been rounded off to
the next whole number. To understand the likelihood of recommending Furphy to others by the
clients who feel neutral about Furphy’s speed of delivery, the recommendation status of the
clients giving a rating of 5 to the speed of delivery of the product has been collected. From table
12 it can be seen that the likelihood of recommending Furphy to others by these customers who
feel neutral towards the speed of delivery of the product is 29 percent.
With varying levels of perception towards product quality, it can be seen clearly from
table 14 that the likelihood of people recommending Furphy to others is 0.505. It can be seen that
the lowest rating given by the clients is 6. Thus, it can be said that the product has quite a good
quality.
Table 16 shows clearly the likelihoods of recommending the product with negative brand
image, neutral brand image and positive brand image. It can be seen from the table that, 18.5
percent of the clients recommend the product with negative brand image, only 1 percent of the

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customers with neutral brand image recommend the product and around 31 percent of the
customers with a positive brand image give recommendation to others for purchasing the Furphy
brand of beer.
Table 18 shows the likelihoods of recommending the product to others by the customers
who purchase the product directly and by the customers who purchase the product via sales
representatives. It can be seen that 34 percent of the customers who purchase the product directly
recommend the product to others and around 16.5 percent of the customers purchasing the
product with the help of sales representatives recommend the product to others. Thus, from the
nature of these likelihoods it can be said that the product is quite popular among the people of the
country despite of the negative or positive ratings given by the clients. Irrespective of the ratings
given by the customers (positive or negative), it can be seen that the clients do recommend the
product to others.
3.3 Time Series Analysis
From the data collected by the market research company from Furphy’s datamart on the
sales of the product per quarter, it has been predicted that the sales of Furphy in the first quarter
of 2018 will be around 1699.40 litres pale ale. In the second quarter of 2018, the sales will be
around 1714 litres pale ale, in the third quarter of 2018, the sales will be somewhat around
1656.55 litres pale ale and the fourth or the last quarter the sales has been predicted to be around
1688.71 litres pale ale. The moving average method has been used to forecast the future sales is
because a lot of past data points are available (Brockwell and Davis 2016). This will help in
understanding the type of the trend clearly (Box et al. 2015). Other reasons for selecting this
method in this case is because it is very easy to compute and is understood very easily

9DESCRIPTIVE ANALYTICS AND VISUALISATION
(Montgomery, Jennings and Kulahci 2015). This method also provides stable forecasts to the
data (Granger and Newbold 2014).
4.0 Conclusion and Recommendations
The analysis shows that most of the people have given high ratings to the product in
terms of repurchase unit. Most people have recommended the product to others. From the
regression analysis, it has been identified that the most significant variables to predict the
repurchase intensity are loyalty towards the product, distribution channel, perceived quality of
the product and the brand image of the product. These characteristics can predict the
repurchasing intensity of the customers 44 percent correctly. From the significant variables
identified by the manager of the market research company to predict the repurchasing intensity
of the product, it has been observed that the variables quality and brand image can predict the
repurchasing intensity 34 percent correctly. From the distribution of different variations of the
ratings given by the customers it is evident that the product is quite common among the people
of the country and is highly recommended. From the prediction of the future sales, it is evident
that the sales of the product will increase in the year 2018.
Thus, it can be said that the significant variables such as customer loyalty, distribution
channel, perceived quality of the product and the brand image of the product are responsible in
the increase in the future sales of the product of the company. Thus, the company should take
specific measures to develop these four conditions in order to increase the sales of Furphy beer
more.

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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.
Granger, C.W.J. and Newbold, P., 2014. Forecasting economic time series. Academic Press.
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.
Montgomery, D.C., Peck, E.A. and Vining, G.G., 2015. Introduction to linear regression
analysis. John Wiley & Sons.
Nelson, E., 2016. Radically Elementary Probability Theory.(AM-117) (Vol. 117). Princeton
University Press.

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APPENDICES
Table 1: Ratings for purchasing Initiative
Table 2: Recommending Furphy Beer
Table 3: Regression Statistics Involving
all independent variables
Multiple R 0.685526154
R Square 0.469946108
Adjusted R Square 0.43289933
Standard Error 0.672658451
Observations 200

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Table 6: Regression Statistics of the significant
independent variables
Multiple R 0.66411018
R Square 0.441042331
Adjusted R Square 0.429576533
Standard Error 0.674626217
Observations 200

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Table 9: Regression Statistics of Manager Identified
Independent Variables
Multiple R 0.583664866
R Square 0.340664676
Adjusted R Square 0.333970917
Standard Error 0.728972457
Observations 200

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