Answer

Shopping Data Analytics

There are various factors that influence individual and communal shopping behaviors. Both internet and in-store shopping attitudes are influenced by certain attributes, as argued out by Hsin Chang and Wang (2011). Therefore, it necessitates that management of stores ascertain the influencing factors for shoppers in their market scope. Such kind of assessment is a leverage in the increasingly competitive environment (Berry, et al., 2010). The major shopping areas in the community of Springdale include Springdale Mall, West Mall, and the downtown area on Main Street. A telephone survey has been conducted to identify strengths and weaknesses of these areas and to find out how they fit into the shopping activities of local residents. The 150 respondents were also asked to provide information about themselves and their shopping habits. The study is intended to find out the customers’ rating of certain influential shopping attributes, a phenomenon that can aid managerial strategies in improving sales.

Descriptive Statistics

1. Easy to return/exchange goods

The mean of customer’s rating for importance of this attribute is 4.97 (SD = 2.00). This means that customers generally rate this attribute of shopping as moderately important (5). The mode was 7 (highest number of customers rated the attribute as very important among their shopping influences) and the Median was 5. With the moderate negative sleekness (-0.68), it is evident that a majority of the shoppers included in the dataset rated the attribute above the median.

Fig 1: Bar graph for “Easy to return/exchange goods”

2. High quality of goods

The mean of customer’s rating for importance of this attribute is 5.51 (SD = 2.03). This means that customers generally rate this attribute of shopping as important (6). The mode was 7 (highest number of customers rated the attribute as very important among their shopping influences) and the Median was 7. The data is highly skewed to the left (-1.13), evidencing that a majority of the shoppers included in the dataset rated the attribute above the median.

3. Low prices

The mean of customer’s rating for importance of this attribute is 5.65 (SD = 1.86). This means that customers generally rate this attribute of shopping as important (6). The mode was 7 (highest number of customers rated the attribute as very important among their shopping influences) and the Median was 7. The data is highly skewed to the left (-1.25), pointing out that a majority of the shoppers included in the dataset rated the attribute above the median (54% of the respondents rated the attribute as very important).

Fig 2: Bar graph for “Low prices” frequencies.

4. Good Variety of Sizes/Styles

The mean of customer’s rating for importance of this attribute is 5.26 (SD = 1.72). This means that customers generally rate this attribute of shopping as moderately important (5). The mode was 7 (highest number of customers rated the attribute as very important among their shopping influences) and the Median was 6. The data is highly skewed to the left (-1.04), it is evident that a majority of the shoppers included in the dataset rated the attribute above 6 (with a total of 52% of the respondents rating the attribute as important and very important).

5. Sales Staff Helpful/Friendly

The mean of customer’s rating for importance of this attribute is 4.87 (SD = 1.80). This means that customers generally rate this attribute of shopping as moderately important (5). The mode was 7 (highest number of customers rated the attribute as very important among their shopping influences) and the Median was 5. The data is moderately skewed to the left (-0.55), indicating that a majority of the shoppers included in the dataset rated the attribute above the median (a total of 60.67% of the participant customers rates the attribute of helpful/friendly staff as between moderately important to very important).

6. Convenient Shopping Hours

The mean of customer’s rating for importance of this attribute is 4.92 (SD = 1.92). This means that customers generally rate this attribute of shopping as moderately important (5). The mode was 6 (highest number of customers rated the attribute as important) and the Median was 6. The data is moderately skewed to the left (-0.74), indicating that a majority of the shoppers included in the dataset rated the attribute above the median (a total of 51% of the participant customers rates the attribute as between important to very important).

7. Clean Stores and Surroundings

The mean of customer’s rating for importance of this attribute is 4.68 (SD = 1.94). This means that customers generally rate this attribute of shopping as moderately important (5). The mode was 6 and 7 (highest number of customers rated the attribute as important and very important) and the Median was 5. There is a weak negative skewness for customers attributing cleanliness of the stores and surrounding as important (-0.48), indicating that not as much customers rated this attribute above the median of important as those that rated it below it.

Fig 2: Bar graph for “Clean Stores and Surroundings” frequencies.

8. A lot of bargain sales

The mean of customer’s rating for importance of this attribute is 5.25 (SD = 1.68). This means that customers generally rate this attribute of shopping as moderately important (5). The mode was 6 (highest number of customers rated the attribute as important) and the Median was 6. There is a negative skewness for customers attributing bargain sales as important (-1.02), indicating that a majority of the shoppers rated this attribute above its median (moderately important).

Outliers

There is no outlier in the dataset. The variables of concern do not have any outlier entry recorded.

The quartiles for the specific variables are reported in the table below

Table 1: Table for variable quartiles (First and Third Quartiles)

The attribute that seems to be the most important in respondents’ choice of a shopping area is “Low prices”. While the least important seems to be “Clean stores and surroundings”.

The mean, quartiles, frequency distribution and skewness of the data were used to determine respondents’ rating of importance in deciding the least and most important attributes in their view. The “Low prices” attribute had the highest mean, lower quartile of 5 and upper quartile of 7, a strong negative skewness and it also posted the highest proportion of respondents (54%) who rated it as very important. Relatively contrariwise can be reported of the “Clean stores and surroundings” attribute generally.

Correlation

Table 2: Correlation table

There is a moderate positive correlation between “High quality of goods” and “Good variety of sizes/styles” attributes (0.4). This indicates that as shopper’s influence by “High quality of goods”, the influence by “Good variety of sizes/styles” also increases.

Further, the correlation between “High quality of goods” and “Sales staff helpful/friendly” is 0.2. This indicates that there is a weak positive linear relationship between the attributes – as one increases in influence of shopping, so does the other in a considerable degree.

The correlation coefficient between “High quality of goods” and “Convenient shopping hours” is 0.3. This is a moderate positive linear association. Therefore, as one the influence of one attribute increases, so does the other’s in the case of shopping.

There is a moderately strong positive correlation between “High quality of goods” and “Clean stores and surroundings” (0.4). The kind of linear relationship between the attributes point out that a customer who is influenced to shopping by high quality of goods is also likely to be influenced by the cleanliness of the environment of the shop from which they are to obtain the goods.

The degree of linear association between “High quality of goods” and “A lot of bargain sales” attributes is 0.3, indicating a moderate positive linear correlation between the two attributes. This means that a shopper who is appealed by high quality of goods will also most likely like that a bargaining space be attached to the good in the shopping process. Both attributes moderately influence an individual’s shopping characters positively.

The linear relationship between “High quality of goods” and other attributes indicate that a shopper would be more influenced to shop in a store when the attributes are collectively impressing their impression. This is consistent with previous research expeditions on customer shopping attitude and behaviors that indicate that the attributes enhance each other (Kacen, Hess, & Walker, 2012).

Conclusion

The highlighted attributes were rated as important by the participant shoppers in the survey. Further, the attributes indicated a likelihood of collective influence on shopping on the progressive phase – this is based on the correlation between “High quality of goods” and the other attributes examined. The positive linear relationship indicate that a collective influence of the attributes on shopping is highly probable, and can be plausibly argued to enhance shopping. Therefore, a strategy that is inclusive of all the strategies examined in this report has high odds of enhancing shopping in the stores. Nevertheless, the nature of these association are subject to more conclusive assessments as correlation coefficient alone is not sufficient for explaining the relationships.