MKTG 202 Marketing : Local and International Markets

’s services and products. Therefore, this research will explore and address the issues and opportunities in the markets by adopting the customer feedback approach.

The research study examines the effectiveness of the customer feedback through the analysis and the interpretation of the collected data. Additionally, the factors of improvement in services and the products will be determined from the obtained results to draw the conclusions and the recommendations.

The methodology

From the proposal, the research will deploy the use of mixed techniques in the data collection where the qualitative data technique will be utilized in the collection of non-numerical data from the customers across different regions of the New Zealand (Richards, 2014). Additionally, this technique will be vital in examining the effectiveness of the customer feedback and the factors required for the improvement of the services and the products of the Dominos, New Zealand. However, the quantitative analysis will be utilized to collect the secondary data from the Dominos websites and among other sources (Fabijan, Olsson, and Bosch, 2015, pp.139-153). These quantities of the data will be a help to map the effectiveness of the customer feedback with other factors such as the revenue growth and the network sales in determining the marketing strategies of the company. Additionally, the quantitative data will enable the research to use the reliable sources of information such as the document secondary sources that are viable thus making the results of the study more accurate (Johnston, 2017, pp.619-626).

Data sampling and collection 

The collection of the data was conducted through the survey questionnaire within the allocated period of two months (Rea and Louis, 2014). The researchers were divided into different teams of three and visited different stores on different days. The main objective was to attain a completion of 6418 questionnaires across the country in pizza stores. During the collection of the quantitative data, the researchers made it voluntary for respondent wishing to the participant and they were freely allowed to stop or quit in the middle of the interview (Mockler, 2014, pp.146-158). The researchers, however, requested the customers who made a purchase and would approach them to inquire if they were willing to participate in the interview as they waited for their order. On the delivery of the order, the respondents could choose to either stay and finish the interview or leave without explanation of the reasons for haste leaving. The quantitative data collected from the secondary sources such as the Domino’s pizza website was used to draw the relationships between different trends in the data.

The Dominos having 105 stores of pizza in New Zealand, the use of probability sampling was applied to identify the stores where the data collection would take place. The research categorically applied in order to identify 35 samples out of the total stores where the researcher collected the data. Cluster sampling refers to the technique in which the clusters of the sample of participants representing the entire population are identified and included in a sample. This technique was identified to be the most effective sampling method for conducting market research according to (Feng, Liang, and Zhang, 2016, pp.204-219). Moreover, the geographical sampling type of cluster sampling was suitable for this research as the clusters were identified based on the geographical location of the pizza stores that were divided into samples upon which the clusters were obtained by randomly picking the samples. The researcher selected a cluster grouping as a sample frame where 35 samples were identified and each sample marked a unique number, and finally chose a sample of clusters by applying the probability sampling.

Questionnaires 

The research targeted to interview 6418 customers across the sampled pizza stores. However, 6236 customers were interviewed within the allocated time from the proposal. Out of the interviewed customers, 288 questionnaires were uncompleted as the customers left before the completion of the interview. Such questionnaires were sidelined and separated from the rest of the questionnaires thus not considered in the analysis. From these figures, the completion rate of the questionnaires is calculated to be 95.4 per cent. From the conducted survey questionnaires, saturation of the qualitative data was achieved where the researchers observed the repetition of the answers in the precedent questionnaires (Fusch and Ness, 2015, pp.1408-1416). This was a clear indication of the correct sample size implying that it could be replicated to a larger population.

Data analysis

From the quantitative data collected from the websites and the other secondary sources was analyzed using the descriptive analysis as described in the following section (Nardi, 2018). This includes the use of the bar graphs, line graphs and the pie charts. From the data collected the growth rate of the consumption of the fast foods from 2016 to 2018 in New Zealand.

The graph above indicates the growth of first foods across New Zealand from 2016 to 2018. The Domino’s pizza is observed to have a higher percentage of the growth followed by the Sushi. The Burger King is recorded to have depreciated in the market with the least percentage in terms of the growth. Moreover, the quantitative method was also applied in the collection of the data regarding the priorities of the consumers when they are making the purchases for the Domino’s pizza. This was abstracted from the product innovation survey and the results are indicated as shown in the graph below.

From the graph, the affordability in prices is the most considered factor by most customers when making a purchase followed the novel products factor. The degree of convenient was considered as the least priority of the customers during the purchase of the Domino’s products (Zemack, Rabino, Cavanaugh, and Fitzsimons, 2016, pp.213-230). The quantitative sources of data also covered the key financial indicators of the Domino’s enterprise from the financial reports of 2005 to 2010. The data focused on the network sales made versus the revenue generated. Furthermore, the data was varied basing on the revenue growth rate versus the revenue growth rate. These are represented below.

Additionally, the customer satisfaction index for Domino’s was obtained from the national bureau of statistics from 2000 to 2018 as tabulated in the figure. The line graph indicates an improvement in the degree of satisfaction from 69 per cent in 2000 to 77 per cent in 2012. The graph is observed to incur a sudden increase in 2013 to 81 percent then followed by a sharp decrease to 75 in the following year. The index rose gradually to 79 per cent in the 2018 as shown on the line graph.

After the collection of the survey data through the questionnaires, the data which was categorized in different frames from which the scores of the customer satisfaction index were determined (Gunawan and Akbar, 2018, pp.11-19). These indices were used to determine and address the weaknesses that were associated with the measurement of the standard customer satisfaction. The research uses this as the basis to quantify the performance of a specific pizza outlet from the samples given in the precedent section. Moreover, 80 questionnaires were used in determining the satisfaction index of a single outlet whereby the saturation was attained in the set of responses given by the participants. However, 35 different satisfaction indices were obtained from the available samples each representing a single pizza outlet. The obtained data for the 35 for the CSI is given below.  

Pizza outlet/store	Customer Satisfaction Index (CSI) score
1.      Auckland NZ	70
2.      Blenheim	78
3.      Rotorua	82
4.      Ashburton NZ	80
5.      Albany	74
6.      Highland park NZ	86
7.      Addington NZ	76
8.      Chrischurch NZ	74
9.      New Brighton NZ	76
10.  Hamilton NZ	78
11.  Hastings NZ	80
12.  Nelson NZ	80
13.  Wellington city	70
14.  Tauranga	72
15.  Upper Hutt NZ	74
16.  Mount Maunguni	48
17.  Rangiora NZ	86
18.  Papakura NZ	72
19.  Onehunga	68
20.  Nelson	78
21.  Bishopdale	62
22.  Woolston	76
23.  Manukau NZ	68
24.  Napier city NZ	78
25.  Manakau central NZ	68
26.  New Plymouth NZ	78
27.  Howick NZ	76
28.  Greymouth	82
29.  Rolleston NZ	76
30.  Mangere NZ	66
31.  Cambridge NZ waikato	80
32.  Belmont NZ	74
33.  Kingsland	74
34.  Dudeni anderson’s bay	74
35.  Bethlehem NZ	68

Table 2: CSI scores for sample NZ pizza outlets

From the data, it was possible to perform univariate analysis, which defines important statistical characteristics such as distribution, dispersion, and the measures of central tendencies (Jung, 2018, pp.1-15). These characteristics are defined by the mean, mode, median, and the standard deviation.

The univariate analysis 

This was used to determine the central tendency of the satisfaction index score of the customer feedback. From the data, mean or average computed from the Excel sheet was given by;

This gives the most frequent value in the set of the scores. From the data, it was given by 74 from the MS Excel sheet.

The median was used to determine the value that is found at the exact middle of the set of data. From the MS excel calculations, the median was found to be 76.

  =MEDIAN (A1: A35)” then “Enter” = 76

This is used in statistics to breakdown the data into smaller units in order to provide the comparison among the different pieces of data (Bosman and Peter, 2018, pp.1209-1223). From the MS Excel, the Kth percentile is given by the formula given below.

       "=PERCENTILE.EXC (A1: AX, k)"

Whereby "k" is the percentile value, you are looking for while X is the last entry of the data set in the column. For instance, the percentile value of the CSI score for the70th percentile is given by;

"=PERCENTILE.EXC (A1:A35, 0.7)" then "Enter" = 78

This gives a more detailed estimate of the dispersion among the data sets since an outliner is more likely to exaggerate the range (Rezaei, Van, andTavasszy, 2018, pp.158-169). The standard deviation is reliable in showing the relation that the data set has to the mean value. This can be calculated from the MS Excel using the formula outlined below.

                                        “=STDEV (A1:A35)” then “Enter” = 7.12057

Range of scores	Count	Cumulative count	Cumulative %
48-54	1
55-61	0
62-68	6
69-75	10
76-82	16
83-89	2

This is used by the researcher to determine the number of observations that a particular score will fall under in a given data set. This is calculated using MS Excel by first clustering the scores into different ranges, as shown, for instance.

The count or the frequency of occurrence is sort in a descending order in the MS Excel and the results tabulated as given below.

Range of scores	Count	Cumulative count	Cumulative %
76-82	16	16	45.71
69-75	10	26	74.29
62-68	6	32	91.43
83-89	2	34	97.14
48-54	1	35	100

Table 3: Cumulative percentage

The results can be represented using a Pareto chart from the MS Excel as shown below.

The primary axis is the frequency/ occurrence of the scores while the secondary axis is the cumulative percentage demarcations (Hamann and Michael, 2018, pp.1-2). From the Pareto analysis, the results indicate that the customer satisfaction index scores contributing to the reported good performance of the Domino’s pizza outlets fall in the range of 76-82 and 69-75. This calls for the outlets to improve their scores to range above 83 to give a significant contribution to the performance of the enterprise at large.

Data interpretation and findings

The Domino’s pizza is observed to be the leading in terms of the growth rate of the fast foods in New Zealand from 2016 to 2018 as evidenced in figure 1. This is linked to the customer satisfaction index that was reported to have risen from the year 2012 as shown in figure 3. This increment in the CSI in this year is believed to be the reason for the increase in the purchase of the Domino’s pizza, which is attributed to the improved service delivery in the year. This strategy could be the possible reason since there is observed an increase in purchases from the outlets. Additionally, the high growth rate is also linked to the subsidized prices of the Domino’s pizza as the study indicated that the consumers prioritized the prices of the products before any other factors as evidenced in figure 2.

Moreover, the key financial indicators of the Domino’s enterprise data from 2005 to 2010 indicate an increase in network sales and the revenue for the organization (Dumay and John, 2018). These increments are however attributed to the improved delivery of services and the products offered at the outlets. The data further indicated cumulative increments in the rate of growth of the network sales and the revenues as indicated above. The higher number of the network sales indicates that there is an increase of the outlets set up in different regions for to quench the customer demands. (Boo and Soyoung, 2018, pp.287-301)  explains that for an enterprise to set up an additional branch for any form of product or service offered, there must have been a thorough review of the customer feedback that was the basis of identification of the strengthens and the weaknesses of the organization. It is only from these reviews that the organization can make marketing and other strategic decisions such as increasing the number of the outlets by the firm thus facilitating the growth of the enterprise in general (Chernev, 2018).

Interpretation of the univariate analysis 

With mean, median, and mode being 74.34286, 76, and 74 respectively, it implies that the customer satisfaction index across all the outlets is stable. Taking the value of the 70^th percentile gives a score value of 78 meaning the breakdown of the data into smaller parts would still lead to a high SCI score value. The standard deviation allows the researcher to arrive at some specific conclusions about the distribution of the scores. For instance, the distribution of the scores from the analysis of the data set which makes us draw the following conclusions about the data (Mendes and BVM, 2018, pp.1244-1249). First, approximately 68 per cent of the scores fall within a single standard deviation of the mean. Additionally, nearly 95 per cent of the scores fall within two standard deviations of the mean whereas 99 per cent of the scores lies within three standard deviations of the mean. From the analysis, since our mean is 74.34286 while the standard deviation is 7.12057, 95 per cent of the customer satisfaction scores will fall within the range of 74.35286-(2*7.12057) to 74.34286+ (2* 7.12057) or between 60.1117 and 88.584. Such information is vital in predicting the future trends of the data or projecting the findings to the larger population. According to (Appiah-Adu, Kwaku, and Bernard, 2018, pp.86-104), having such information about the customer satisfaction index is crucial for making future decisions in marketing strategies aimed at improving the market share and sustaining a stiff competition.

Conclusions

From the analysis and interpretation of the data, it is evident that customer feedback is an essential approach in evaluating the customers’ perception about the products and services. Additionally, the client feedback has also been proven the bedrock of an effective decision-making concerning the wide range of the customers’ needs and expectations (Jaworski, 2018, pp.75-79). The Domino’s New Zealand is one of the leading fast foods industry, therefore, should continue investing in the customer feedback reviews which would enable the company to identify the opportunities in the market and the weaknesses leading to the lag in the delivery of the goods and services. From the reviews of the customer feedback, the company is also able to get the views concerning their competitors (Bernanke, Laubach, Mishkin, and Posen, 2018). This information can be harness the operational strategies laid in place by the competitors thus enabling the organization to gain a competitive advantage in the market.

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