Research Paper on Importance of Using Social Media to Drive Sales for a Clothing Brand

Introduction

This academic project strives to investigate the importance of using social media to drive sales for a clothing brand. The theoretical concepts used to examine this research will be a combination of exploring brand theory, consumer behavior and how customers react to social media. Quantitative and qualitative data will be used to examine this research, the qualitative data will be one brand out of six brands I chose to analyze: ASOS, Burberry, Chanel, Loro Piana, Uniqlo, Ermenegildo Zegna. I chose ASOS to conduct my research. I also chose the United Kingdom as the chosen country where ASOS conducted its business from the year 2013 to 2014. I am interested in examining if the countries temperature also affects sales in those chosen years. The reason why I choose this topic is because I recently opened my own cashmere clothing brand and I believe conducting this study will greatly enhance my knowledge on this area. I used social media websites such as Facebook, Instagram and Twitter. I became a member of three top luxury fashion institutes to access the data I collected for this academic study. Without all their help I would not have the privilege to access this data and insight into this market. The analytical data used complies with the International Financial Reporting Standards (IFRS) and in particular with IAS 21 "The Effects of Changes in Foreign Exchange Rates". The hypothesis of the study is that uploading social media activities has a competitive advantage over offline competitors. A model was created using an econometric software program called SPSS by IBM to connect all the theoretical concepts, statistical data contributed to answering the academic question.

Literature ReviewSocial media has changed the way companies do business. Ideally, it has helped businesses raise standards including promotion of the means by which enterprises interact with costumers and at the same time deal with stakeholders (Kaplan & Haenlein, 2010). Social media is a relatively recent phenomenon therefore there is a limited amount of research and information available online and offline based on my research topic. However I did manage to find some which were close enough to my hypothesis and research topic. One study caught that caught my attention was how sales people perform compared to their peers by using social media by Jim Keenen. Util he came into the academic picture there was no real research done to support his hypothesis which was the rise of social sales people in using social media to sell to increase profits (Jim Keenenm 2012). He was one of the first to actually test this theory and put it into practice. From his analysis in 2012 he came to a conclusion that an astonishing 78.6% of sales people using social media to sell out performed their peers who did not use social media. Keenen also found that when it came to sales quota of 10% he came to a conclusion that social media users were 23% more successful than their non-social media peers.

Through analyzing his data I can see there is a realization that there is sufficient evidence to support his hypothesis. Consider that over half of the respondents in his research, 54% who used social media could tracked their closed deal through social media. Over 40% said theyve closed between two and five deals as a result of social media and more than 10% of the respondents said; Yes, It directly contributes to my closes. Respondents were very clear. Social media was a leading factor in their closed deals. Other research materials that caught my interest and was strongly related to my research topic were these academic journals from New York University, Forbes and Indianas Institute of Management.

Model and DataIn analyzing the data, I will provide a detailed analysis of ASOS and the influence of Social media activity on company sales. I collected data on weekly sales figures for this company from the period of 2013 to 2014: Also, I have collected data about ASOS and its social website activities from 1st of January 2013 to the 1st of January 2014, I have included actions from Facebook, Instagram, and Twitter and combined the figures into one and called it activities as below.

Social Media Platform Actions (in 2014)

ASOS 7,508

Facebook 5,546

Instagram 1,896

Twitter 66

Given this data, a regression analysis can be made. In this case, the weekly sales figures of 2013 was the dependent variable. As independent variables, we include weekly sales of 2014 to account for the influence of seasons and weekly numbers of actions in 2013 and 2014. I will use IBMs econometrics and statistics software called SPSS for this regression analysis.

Sales =b0+ b1Activity2013+ b2Weeklytemp+ b3Activity2014+ut

Where

Sales weekly sales (2014)

Weekly average temperature (in degrees Celsius)

Analysis and Results

Regression Results using SPSS by IBM

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .625a .390 .352 2353.887

a. Predictors: (Constant), activity2013, Weekly Average Temperature, activity2014

CoefficientsaModel Unstandardized Coefficients Standardized Coefficients t Sig.

B Std. Error Beta 1 (Constant) 17803.738 2441.992 7.291 .000

Weekly Average Temperature 43.174 54.434 .100 .793 .432

activity2014 96.824 26.725 .672 3.623 .001

activity2013 -12.063 31.718 -.074 -.380 .705

a. Dependent Variable: sales2014

Sales2014=11573.772***-8.610Activity2013+31.484Weeklytemp+81.888***Activity2014

(2233.249) (29.006) (49.781) (24.440)

n=52, R2=0.39, F=10.236

Economic theory behind the variable selection plus the hypothesized signs of coefficients

Bo is the intercept. It is the value of the activity of 2014 sales irrespective of 2013 activity and temperature.

Activity represents the action of 1 post in a given social media platform.

Significance level

The significance level used in the study was 1%. Ideally, this was because the researcher aimed at acquiring accurate results from the data being investigated.

Sample size

The researched utilized a sample size of 52 observations. The required amount of data variables to be investigated for the statistics to be valid is 30 and hence the use of 52 observations was acceptable.

Discussion

Sales 2014 stands at 11573.772 when we do not take in to consideration activity 2013, temperature and activities 2014. Activity 2013 has a negative and a statically insignificant effect on sales 2014. Its coefficient shows that when activity increases by 1 post on the chosen social media sales 2014 falls by 8.610 dollars. The weekly temperature has a positive and an insignificant effect on sales 2014. Activity 2014 has a positive and very significant effect on sales 2014. Its coefficient shows that 1 post on a chosen social media increases sales by 81.9 dollars and this result is significant at the 1% level. The significance of the weekly average temperature was 0.432. Additionally, the significance of activity 2014 and activity2013 was 0.001 and 0.705 respectively. From these results, it is clear to note that activity2014 was equivalent to the critical value 1percent while the rest were more than the 1percent significance level implying that they were insignificant.

Coefficient of determination (r2)

Turning to the coefficient determination (R2) it is evident that 39% of the total variation in sales 2014 is explained by our model.

Conclusions

From the results obtained from the study which aimed at examining the importance of using social media to drive sales for a clothing brand that establishing a clear strategy to use on social media to gain a competitive edge in the market is crucial. For any social media, the functionality of uploading items to the website was paramount especially if it was conducted at the right season. The study analyzed the 2014 online social media activity online of ASOS and from the results, it can be seen that it performed better in 2014 as compared to 2013. Comparing the results of the study to those of Jim keenens research studied in the literature review section, it is clear that the two research studies resonate as the results give the same inferences of positive outcomes. Ideally, the results of the study support the initial hypothesis of the study which posit that uploading social media activities has a competitive advantage over offline competitors. Thus, I would like to recommend the use of the internet in advancing social media activities especially for businesses that aim at reaching out to the target market located in areas out of reach. In this case, I would like to urge businesses using social media to conduct business to embrace the functionality of uploading as this will help them attract more traffic. Telecommunication companies as well as technology companies should focus their effort in creating functional social media websites with functionality of uploading including other customized functions which will improve interactivity of the customers with the social media websites. For further research, I would like research to study other social media companies over a longer period of time such as 10 years so as to get a better overview of the trend.

Bibliography

Bullas, J. (2012). 12 Key Findings on social Medias Impact on business and decision making by CEOs and Managers. Retrieved from http://www.jeffbullas.com/2010/04/05/12-key-findings-on-social-media%e2%80%99s-impact-on-business-and-decision-making-by-ceos-and-managers/

Fidelman, M. (2013). Study: 78% of sales people using social media Outsell Their Peers. Retrieved from http://www.forbes.com/sites/markfidelman/2013/05/19/study-78-of-salespeople-using-social-media-outsell-their-peers/#80dcd277c321

Forbes. (January 18th 2015). Measuring the impact of social Media on your business. Retrieved from http://www.forbes.com/sites/christinemoorman/2015/01/18/measuring-the-impact-of-social-media-on-your-business/#4c54a23b46e5

Jackson, O. M. (January 2007). The Study of Social Networks in Economics. University of Stanford

Panagiontis (2014). The effectiveness of promotional events in Social Media. New York University.

Vilma Todri,V. (2014). Social Commerce: An Empirical Examination of the Antecedents and Consequences of Commerce in Social Network Platforms. University of New York, 2014

DR Sandeep Vij. (2013). An Empirical Study on Social Media Behavior of Consumers and Social Media Marketing Practices of Marketers. Indiana Institute of Management, Phagwara.

Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business horizons, 53(1), 59-68.

Appendix:

Regression Results using SPSS by IBM

Variables Entered/RemovedaModel Variables Entered Variables Removed Method

1 activity2013, Weekly Average Temperature, activity2014b . Enter

a. Dependent Variable: sales2014

b. All requested variables entered.

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .625a .390 .352 2353.887

a. Predictors: (Constant), activity2013, Weekly Average Temperature , activity2014

CoefficientsaModel Unstandardized Coefficients Standardized Coefficients t Sig.

B Std. Error Beta 1 (Constant) 17803.738 2441.992 7.291 .000

Weekly Average Temperature 43.174 54.434 .100 .793 .432

activity2014 96.824 26.725 .672 3.623 .001

activity2013 -12.063 31.718 -.074 -.380 .705

a. Dependent Variable: sales2014