Web analytics and the role it plays in the fashion industry

The internet has made the world smaller than ever. With growing technology as well as an increase in information available, every industry in the world has had to adjust to the digital age. The fashion industry is no different and as a result, website traffic and consumer online shopping habits have been more scrutinized than ever by fashion companies. 

In the past, web analytics did not play as big of a role in the fashion, but with the advancement of technology, web analytics have came to the forefront of the entire industry with no signs of slowing down.

What is Web Data Analytics? 

Web data analytics is first the collection of information from a website, followed by the examination of the data collected. The benefits of collecting this data is plentiful for a company wanting to increase sales and consumer loyalty. 

The company is able to pinpoint a consumer's habits which help them assess how to move forward in the growth of their company. The difficultly for these companies is first figuring out how to collect the data, followed by figuring out what data to collect. 

How is Web Analytics Collected?

Web data analytics is collected by a combination of different technology that includes new applications as well as Artificial Intelligence technology. Artificial intelligence (AI) is a combination of technologies including natural language processing, computer visions, machine learning and deep learning algorithms, VR / AR / MR technologies, and more. AI-based merchandising applications can be applied in the data analytics process to provide better answers to business questions generated by fashion brands and retailers (Keunyoung O. The roles of data analytics in the fashion industry.) 

It is very apparent how important AI- driven technology has become in the fashion industry as sixty nine percent of national or large retailers in the United States indicated that using AI driven merchandising applications is important to improve AI performance according to a survey done by Keunyoung oh .

An example of this AI technology is Skypad software. Skypad software used by 72% of global luxury brands collects data from various retailers and allows brands to see how their products are performing based on a variety of attributes such as color, size, fabric and geography. It also tracks sales performance via wholesale business and the brands' own stores and online channels. With predictive analytics, they provide insights about season management, what sells, what doesn't sell in different regions, merchandising planning on the retail floor.   (Keunyoung O. The roles of data analytics in the fashion industry.) 

With this advanced technology, retailers are able to get a better look at the average consumer without having to resort to outdated data collection tactics such as phone calls and paper surveys. As a result, companies have been racing to assemble teams of employees that specialize in data analytics. Companies such as Nike and Pandora have seen a rise in hires of people with this type of technological background. With how competitive the fashion industry is, it makes sense for most of these big retailers to try to get an advantage, and web analytics is something that can provide that advantage if the right data is collected.

What Data is Collected?

With all of this special technology, it is paramount for companies to be able to know what data to collect. If the wrong data is collected, not only is time wasted, but also money and resources as well. As a result, companies have narrowed their scope of what type of data is collected which will enable them to be able to specifically pinpoint who is shopping on their website, and what they are looking for.

Depending on the company objectives are different. For example, Luis Vuitton has a different market than Aeropostale, so objectives will obviously be different. With the being said, most fashion companies look at similar things in their data analytics to try and figure out how they can both boost sales and increase consumer loyalty towards their specific brand. 

Demographics

Demographics is defined as information about the consumer's background that is beneficial to understanding their purchasing habits. This can include sex, race, gender, age and income. This is important to a company because it helps them understanding exactly who they are selling their product to and why. Without demographic information, it is very possible that these companies could be wasting precious time and resources on clients that would not even be inclined to purchase their products  

Historical Behavior

Past behavior is a huge indication about what will be done in the future, and the fashion industry is no different. Using AI technology, companies view patterns that their consumer show on the website. this includes things that range from items purchased, all the way to items viewed, as well as items placed in the cart and later abandoned. With this specific information, brands are able to view what people like and dislike, but more importantly what items consumers are indifferent about. With this information, brands are able to understand the consumer at a deeper level and predict trends.  

Streaming / Contextual Behavior

Due to the rise in technology as well as capitalist laws, companies are able to view a person's streaming and web browsing habits. This is very important to the company as they are able to seethings such as what type of tv shows they enjoy, as well as what type of ads the consumer usually clicks on. This is an important development because it gives the companies even more of a specific view of who they are targeting and how better to appeal to that consumer.  

Conclusion 

With the rise of the digital age, web analytics have become more important than ever. The fashion industry has been growing at a rapid pace with no signs of slowing down. As a result companies have had to be more specific and decisive about who they target, and web analytics have become the main way companies have been able to do so. With the fashion industry still growing, all signs point to web analytics to continue to rise in importance in the fashion industry.

Keunyoung O. The roles of data analytics in the fashion industry. J Textile Eng Fashion Technol. 2020; 6 (3): 102-104. DOI: 

-Jeffery Gordon

#Fashion #Data #FashionIndustry #Science