The purpose of the study is to explore the impact on the sales of hard window covering and by looking at three different factors, to be able to identify how each effect the sales in ups or downs. Using descriptive analysis, correlation coefficient and multiple regression analysis, the data can be identified if the factors have a weak, moderate or strong relationship between the dependent and independent variables. It is important to look at the detail of each factor to identify the key factors.
5.1 Major Findings of the Study 5.1.1 Environmental Factor
After examining the summarized data, these three states; California, Florida and Texas have larger window sizes than other states. But it is important to further examine the data by looking at the regions. The following table 17 shows the best-selling region is in the South, second place is in the Northeast region, then the Midwest, and lastly in the West region. From the average dollars per unit in four regions, the data suggest and support that both South and West have larger window sizes as they are the two highest in ranking.
Table 17: Four Regional Data in USA
The results of the four regional data show that the South has the highest population and highest average temperature which represent the best place to push for the sales. Focus on these two regions with marketing promotions and advertisings are
Northeast Midwest South West
62
important to boost their sales. However, from the original data, the top five sales states are New York, Pennsylvania, Florida, Massachusetts and New Jersey. Three of them are located in the Northeast region. These three states would be the best places for testing new products to see the potential of its sales in the market. The West Region has the worst sales but with the second highest population. This suggests that perhaps the product has not been introduced into the market well. There should be some type of advertising in the west region.
It is often thought that the weather would have impact on the sales of hard window coverings. However, the data suggest otherwise. The higher the average temperature, the higher the sales; but a higher the number of clear days results in lower sales. This is assuming that as the average temperature rises, the room temperature increases, and to maintain a comfortable room temperature, hard window coverings are used to insulate the room temperature. But, with a higher number of clear days, people would rather be out instead of shopping. As the weather is nice and sunny, people usually go out for fun instead of staying home or visiting the retail store.
If the weather is bad like raining or snowing, people tend to visit retail stores or shop online. Thus, whenever there is a forecast of bad weather, there should be some sales promotion. It is noted that the average temperature only effects Light Blocking and not Light Filtering and Room Darkening. This may due to the product design and function, which refers back to table 1, it clearly says that the Light Blocking product has strong insulation and it can maintain the interior room temperature. This shows significant relationship between average temperature and Light Blocking products.
Perhaps global warming is a positive thing for the sales of hard window covering!
5.1.2 Population Factor
From the initial data analysis, there is a direct and strong linear relationship between population and the sales of hard window coverings. It seems to be true that the higher the population, the better the sales of window coverings. With further analysis on different races in United States, there are significant relationships with Non-Hispanic White, Black and Asian groups. The Asian group has the highest impact with the sales of hard window coverings, and this suggests that the product should be more oriental in design to attract more of the Asian market. The Regression Analysis has suggested that the Asian group has a preference for Light Filtering and Room Darkening, but not Light Blocking. This may due to the function or design of the product in Light Blocking being less attractive than the products in Light Filtering and Room Darkening. The data also suggest that the Black group has a very high preference for products in Light Blocking.
63
5.1.3 Economic Factor
Economics is the fundamental factor that would effects anything that has a retail price on it. Needless to say it is the study of how people use resources. In looking at the economic impact on the sales of hard window coverings, one should look at the Median Household Income in each state. After all, if there is no income people wouldn’t shop at all. Based on the Median Household Income analysis, we find that if there is a higher median household income, the more people would spend on hard
The purpose of this study is to identify the key factors that affect the overall sales of different types of product categories. By looking at the online sales, which is still a fairly new type of business model, we can determine the key factors and how they impact the sales. This information is valuable for retailers, merchandisers, buyers, analysists, sales, marketing and manufacturers to maximize the sales and gain higher profit margin. Through descriptive statistics, correlation coefficient and multiple regression analysis, it properly measured the impacts of these key factors on the sales and made improvement measures accordingly. These improvement measures may not only apply to online business, but also their store sales as well.
This study has identified the importance of these factors by statistical analysis through a case study of Company A. As a result, we see a significant impact from the average temperature not only in the total of the three product categories but also a strong impact in the light blocking category. We also identify that different races have a different impact on different product categories; but overall, both Non-Hispanic white and black are the most significant. What is surprising is that the Asian population has a significant impact on both Light Filtering and Room Darkening while non-Hispanic white has a significant impact on the light blocking product category. This suggested that perhaps products should incorporate some oriental design. And last but not least is the median household income has significant impact on all three different categories and the total of the three categories.
In conclusion this study finds that:
1. The online sale is very important as it is a benchmark for the sales of window coverings.
2. The average temperature has direct impacts on the sales of window coverings
64
while the number of clear days has opposite impacts.
3. Racial groups including Non-Hispanic White, Black and Asian have a significant impact on the sales of window coverings.
4. The median household income has a significant impact on the sales of window coverings.
5.3 Limitations and Recommendations
This study is focusing on the exploratory factors as there are limited time and people. The variables studied are in original scale in order to study the original impact, but this treatment of data may influence the statistical precision, however, it may help to understand the meaning of the data. The readers are notified.
The frame of this study is the sole data provided by one single company in one single year. This study is also limited by the sampling size by 50 states. One single year of data may not tell the complete story of the product and its sales in a different year. Example like extreme weathers, such as heat wave in California, the hurricane in Florida, tornadoes in the Midwest states may affect the sales. These may limit the sales data of each state. This is beyond the purpose of this study as it is an exploratory research.
It is recommended for further study of the data should include the number of online users in a given area or state. The data is based on online sales and it is related to the number of online users. Addition to the online user would be their age. What age groups shop online and which age groups don’t? Does the age have influence for online shopping? These are suggestions for future researchers to study as part of possible follow-ups in this data collection.
Perhaps if possible to have more extended data not from just one company but two or three for the comparison. One company may have limited data because of its customer base. There are other online companies who specialize in online shopping without a physical retail store, for example, Amazon.com, Blinds.com, etc. These are suggestions for future researchers to study as part of a possible follow up in this data collection. Data compared in the city versus urban area or town versus suburban area.
Do different areas affect the sales of hard window coverings? These are suggestions for future researchers to study as part of a possible follow up in the data collection.
65
Reference:
張元蓉 (2006)。縱橫經緯:從開創到傳承. 台北市:知文企業(股)公 司
吳宗諭 (2009)。市場導向，關係行銷策略和新產品經營績效之相關 研究-以台灣窗簾產業為例。國立中正大學未出版之碩士論文。
林建良 (2010)。高階市場定位的反向選擇問題與策略-以台灣窗簾產 業為例。中原大學碩士班研究生未出版之碩士論文。
張元蓉 (2009)。法鼓山:「心五四運動」管理意涵-以窗簾窗是產業 為範例。國立台灣科技大學未出版之碩士論文。
林暐舜 (2004)。建築開窗與窗簾對室內熱環境舒適度之影響研究。
中國醫學大學未出版之碩士論文。
紀康寶 (2010)。驚人的生活醫學常識-沒想到除了床，窗簾也能影響
健康 P163~P165。台北市:高寶國際出版。
66