Factors Influencing Repurchasing Intention For Furphy Beer – Beautiful Data

Collection of Data

Furphy Beer is a micro-brewery company born in Australia. The company is a new company with less than 15 years of experience in the brewing ale. The production and sales of the company is limited in the city of Melbourne and reginal Victoria. The sales and production is increasing rapidly in all the other parts of Australia. In the last two years significant growth in the production and sales have been noticed. The demand of the company has been increasing so much that in the year 2016 they decided to increase the production to 3 million litres per year in order to cope up with the increasing demand of the product.

There are two market segments, which buy the Furphy pale ale. One is pubs, bars and restaurants and the other is bottleshops. The beer produced by Furphy are sold in these two markets either directly or with the help of sales representatives.

The company has experienced huge success in their operations and financial turnovers in the previous two years. Then also, the company can sense a change in the business climate in the upcoming five years. This can be the outcome of the high popularity of craft beer and microbrewery culture in Victoria and its surrounding regions. Thus, with the increase in the competition, the company has felt the necessity of building a strong relationship with the customers. Thus, to understand and identify the factors that are responsible to build a strong customer relationship the company has appointed a market research company named Beautiful Data. The have been asked to conduct a large scale survey of the clients of the company to have a clear understanding of the clients characteristics and their repurchasing intentions.

To meet the criteria provided by Furphy, the market research company Beautiful Data made contacts with Furphy’s clients and asked them to fill up an online survey. The survey questionnaire contained various factors necessary for the calculation. The data on the past years sales in the four quarters per year have been collected from the information stored and compiled through Furphy’s datamart.

The data collected from 200 clients of the company has 9 different perceptions of the customers or clients. These perceptions have been recorded in a scale of 1 – 10. Other variables give qualitative information about the outcomes of the purchases by the clients and their business relationship with the respective clients. The analysis has been done using the MS Excel software.

Description of the Data

The dependent variables that has to be analyzed and predicted with the data collected are intention of the customers in repurchasing Furphy’s products and recommending the products of the company Furphy to others. The test results show that most of the ratings are between 7 and 8. Thus, the product from the customers has received high ratings. Hence, the product is repurchased by the clients. The clients have a high probability of repurchasing the product. Table 1 gives the repurchasing intentions of the clients. 

Table 1: Repurchasing intentions of Clients

Another dependent that has been considered is the recommendation given by the clients towards purchasing the product. The table 2 shows the results of the opinions given by the customers. It shows clearly that the beer from the brand Furphy is highly recommended by the customers. However, it can also be observed that the percentages of recommending the brand and not recommending the brand are close to each other. Thus, it is clear from here that there are a lot of clients of the company who do not recommend the brand. Hence, if Furphy wants to survive in the market of competition with the increasing market of craft beer and another brewery rising in the locality, it is necessary for Furphy to identify the factors that are holding its prime clients from recommending the brand to others. 

Table 2: Recommendations given by Clients

It is important to identify the potential variables that are responsible in influencing the repurchasing intention of Furphy beer. To identify the variable regression analysis has been done on the complete dataset.

The data collected by the market research company from the clients of Furphy beer involves a lot of variables. All of the variables might not be significant to predict the repurchasing intentions of the product. In order to identify which are the significant variables the regression is necessary to run and check the p-values of the variables involved (Draper and Smith 2014). The regression has been run at ninety-five percent level of significance.  The variables with the p-value higher than 0.05 (95 percent level of significance) are termed as insignificant variables and the variables with the p-value lower than 0.05 are termed as significant variables (Montgomery, Peck and Vining 2015). Regression analysis is the most important analysis in this case as with the help of this analysis only the significant variables can be identified (Chatterjee and Hadi 2015). Analysis of variance (ANOVA) technique can be used to check whether all the independent variables overall signify the dependent variable (Kleinbaum et al.2015). The ANOVA table (Table 3) shows that the significance value is less than 0.05. Thus, the independent variables that have been taken into consideration for this regression analysis has an overall impact in predicting the dependent variable repurchasing intention of the clients.

Analysis of the Data

Table 5 shows the regression coefficients and the p-values of the independent variables individually. From the table, it can be seen that the variables that mostly influence the purchasing intention of the customers are

• Loyalty of the customers that is the length of the time the particular customer has been buying from Furphy
• Distribution Channel that is how the Furphy’s products are sold to the customers.
• Perceived level of quality of Furphy’s products (Quality).
• Overall image of Furphy’s brand (Brand_Image).

Considering all the variables it can be seen from table of regression summary statistics (Table 5) that the independent variables can predict the repurchasing intentions of the customers 47 percent correctly. The variables, which has been identified as insignificant also, has very little impact on the repurchasing intentions of the clients. With the exclusion of those variables, the correctness of the prediction will be affected but not to a huge extent. This difference will not be a problem.

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) 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 7.

Repurchasing Intention = 5.31 + (0.42 * Loyalty) – (0.29 * Distribution Channel) + (0.12 * Quality) + (0.21 * Brand Image).

The manager of the research team has conducted a different analysis. From the analysis, it is observed that repurchasing units significantly depend on the perception about beer quality. Another related findings is that there is a common tendency among the customers to link  brand image with quality of the product. Therefore, these two variables should be used for a good prediction of repurchasing intention. The regression analysis shows that perception about quality of beer and brand image explained 34% variation in repurchasing intention (Table 8). As explanatory variables both quality and brand image are statistically significant. There P values are less than 0.05 (Table 9). The estimated relationship of repurchasing intention with that of quality and brand image are given as-

Repurchasing Intentions = 3.59 + (0.31 * Quality) + (0.31 * Brand Image)

Next agenda of the tram manager is to recommend Furphy. There are clients who are neutral about delivery speed of Furphy depending on the quality and images of the brand. There are also clients who make purchase either directly or through sales representatives. Special attention has  been given to these groups of clients.

Factors Influencing Repurchasing Intention

An important determinant of this is the ratings given by clients. To make the analysis simple, ratings are rounded off to get the nearest whole number. Clients who have given a 5 ratings are collected to confirm the likely for Furphy recommendation. The likely hood of recommendation is 29 percent for the clients who are neutral about Furphy’s delivery speed as shown from table 12.

Perception of clients varied with different levels of product quality. With this varying level, the likely hood for recommending Furphy to other clients is 0.505, as stated in table 14. From the table it is seen that the lowest recorded rating is 6. Therefore, from the rating statistics it is clearly evident that the product delivered by Furphy is quite satisfactory and is of a good quality product.

The likely hood for different brand images is represented in tale 16. The brand images are categorized in three groups positive brand image, neutral brand image and negative brand image. 1 percent of the clients having neutral brand preference recommend Furphy’s product.  Clients with positive and negative brand image constitutes likely hood of 31% and 18.5% respectively. These are the recommendation statistics for clients influencing others toward making purchase of Furphy’s beer brand.

The tendency for recommendation by customers who directly purchase the product or purchase with intermediation by the sales representatives is shown in table 18. It is seen that those who directly purchase the product have a higher tendency for recommendation as compared to those purchase the product with sales representative. Percentage of customers recommending Furphy to others in directly purchasing group is 34% while that for buyers with sales representative is 16.5 percent.

Furphy’s beer brand is quite popular in the state. Mixed responses are obtained from the clients purchasing the products.  There are both positive and negative ratings obtained from the clients. Despite negative and neutral return from customers, there is an overall good image for the product. Recommendations come from all the groups of customers. The Customers recommend the product to others irrespective of their ratings

A time series analysis is made for foresting sales of Furphy in the next quarter. Using sales data until 2017, prediction is made for the tear 2018. Quarterly moving average method (Granger and Newbold 2014) has been used to predict quarterly sales in 2018. When past data points are available then moving average methods are suitable for making forecast (Brockwell and Davis 2016). The trend obtained from the data is likely to depict a clear trend for forecast (Box et al. 2015). Moreover, moving average method is widely used because of simplicity in calculation and easy interpretation (Montgomery, Jennings and Kulahci 2015). The predicted sale in first quarter of 2018 is 1699.40 litres per ale. An increase in quarterly sales prediction is found for the next quarter. In the second quarter of 2018, the expected sale is 1714 litres per ale. This trend declines in the third and fourth quarter of 2018. Forecasted sales for the third and fourth quarter of 2018 are 1656.66 litres per ale and 1688.71 litres per ale.

Recommendations and Conclusion

The paper summarizes sales and ratings of the Furohy product with reference to repurchase unit. Most people using the product have given high rating for the product. The significant variables affecting repurchase units are distribution channel, loyalty of the customers towards product, brand image and perceived product quality. The adjusted R square for the regression analysis is 0.44. This implies the explanatory variables taken in the regression analysis explained nearly 44% variation in repurchase unit. Again, brand quality and image predicts 34% of the repurchasing intensity. As per recommendation is concerned, people with positive ratings recommends the product most. Finally, forecast is made for future sales. Predicted sales is higher for first two quarters of 2018 while for later two quarters sales is predicted to be declined slightly.

Furphy should consider some policy changes to increase its sales and improve ratings. For repurchasing units the significant variables are distribution channel, brand image and quality and customer loyalty. Improvement of these aspects can lead to an improvement in sales of the concerned company. Once the company improves distribution channels or customer loyalty those giving a neutral rating or negative ratings currently might give a positive response. With increase in positive responses, more people will recommend the product to others. This will further enhance sales.  

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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