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Big Data – A Game Changer in the Fashion Industry
By Kamal Jacob
Fashion changes with the blink of an eye. A movie star makes a powerful red carpet or Met Gala appearance and the entire dynamics of the fashion industry changes overnight. To keep up with the demands of the ‘fast fashion changes’ and to reduce the ‘turnaround time’ from ramp to stores, fashion retailers are increasingly turning to ‘big data’. Big Data is touching every aspect of the fashion industry from design to demand, resale, as well as operations.
The fashion industry is one of the latest sector to aggressively embrace data analytics, probably because of its proven result. Extremely large sets of data are segregated into groups and analyzed to reveal patterns, associations, and define the latest trends in the fashion industry. Big data helps designers come to startling conclusions about their designs and help them create a product line that will sell.
Here is how big data analytics play a crucial role in helping everyone from designers to manufacturers, retailers, resellers, and models stay abreast with the ever-evolving shifts and stay on the top of the fashion game:
Big Data Allows You to Harness the Power of ‘Social Media Data’
Fashion has a deep relation with social status. Quite naturally, social media plays a pivotal role in the fashion industry. User engagement on posts, Instagram trends, Twitter hashtags, likes/reactions on popular celebrities, celebrity fashion styles, reactions on posts of popular fashion bloggers, etc provide us with a vast collection of rich insightful data.
Websites such as Twitter, Facebook, Instagram, and Pinterest are sources for raw and uncensored public opinion. Sentiment analysis is used to get insights from public opinion. The volume of this data from public opinion is huge and mostly unstructured, which needs to be cleaned and transformed. If harnessed properly, this data has a lot of potential to give insightful data.
Many companies now initially release photos of a new collection on social media and study the general public reaction and comment to make changes before a large scale launch of the collection. It helps the manufacturers know their audience and their likings on a real-time basis. And it is also beneficial from the perspective that designers can provide the product as desired by their customers.
Fashion has now become an experience and industry leaders are now using the power of social media to convert this experience into a well-defined data set to devise their next fashion trends.
Big Data Help Reduce the Time Elapse between Order and Distribution
We all know Zara is one of the biggest fashion brands and key retailers with hundreds of stores across the globe. Until a few years ago, most Zara stores faced the problem of limited supply. Retail stores used to wait for the stock to finish before placing their order and hence there was mostly a time gap between order and distribution leading to supply crunch.
Zara created an adaptive, data-driven supply chain management to deal with the problem. Unlike traditional retailers who order ‘bulk clothes’ for the entire season, Zara orders only a small amount of merchandise. Once a given product line hits the store, Zara keeps a track of its sales data and analyzes its sales against supply for a particular SKU. In addition, Zara also analyses the sales data of each SKU to identify the sales trend in that area. For example, they might find that in a particular country, slim fit pants sell better than loose fit or a given coloured clothes is given preference. Zara then uses all these insights and data to guide their following order. This ensures that the product is stocked again on time based on sales trends.
With the use of data analytics and big data, Zara has now adopted the concept of ‘fast fashion’ where the entire process of designing a collection to putting it for sale in stores takes a maximum of 21 days.
Image source - https://www.kdnuggets.com/2018/03/data-science-fashion.html
Big Data and Fashion Quality Control
‘Replica fashionwear’ or ‘Pirated merchandise’ is one of the biggest problems that has plagued the fashion industry. As per the report released by the office of the US Trade Representatives in its annual Notorious Markets List, pirated merchandise imports are estimated at nearly half a trillion dollars or around 2.5% of global imports.
From replica Gucci apparel to fake Rolex watches, from counterfeit ‘Victoria Secret’ lingerie to even replica ‘L’Oréal make-up’, anything and everything is now being pirated. And with the latest technology available, now it is possible to replicate even the minute details, which makes it difficult to differentiate between the two. Also, e-commerce giants like eBay, Amazon, and Flipkart have made it easier to sell and buy pirated products. While counterfeiting has been there for ages, it is only recently that is has started having such huge scale impact on the fashion industry.
The huge numbers have garnered the attention of industry leaders who are now using big data to solve the problem of counterfeit merchandise. Companies are now using pattern recognition coupled with big data to protect the integrity of their company. For example, a designer can create a new design and can use pattern recognition to find if something of a similar nature has ever been created. Cognitive Prints, a suite of AI tools can be used for pattern recognition where it will scan through huge amounts of data looking for similarities.
This in addition to fighting piracy helps in building a brand exclusivity and recognition. Also, if a designer wants to include some patterns from a given era in his designs, big data will help him/her achieve that.
Continuous Feedback Loop Analyzation Helps Creating In-demand Product Line
The popular fashion rental service Le Tote collects data about its customers’ style preference. They keep a close track of every signed up customer’s favourite clothing options and their buying trends and analyze their choices. These preferences are then sent to designers who create clothes that are both in demand and affordable. In addition, machine learning is used by Le Tote to analyze the huge volume of written feedback submitted by customers in the store.
This is just one example of how data science can be used by fashion companies to forecast trends based on customer preference. This saves a lot of time, money and effort for both companies and designers and helps them create a product line that is almost sure to sell. This process is way better, efficient and pocket-friendly when compared to the traditional process of making the products and sending them to retailers hoping that it will be well-received.
Dealing Competition with Data
One of the biggest concerns of most fashion retailers is to understand their competitor's strategy and to be ready to outshine them. With appropriate big data tools, fashion retailers can get real-time insights on what their competitors are creating and how their campaign is performing. Armed with these insights, they can strategize their campaigns better than their peers. Your competitor is planning to launch a sporting cloth line? You can introduce a new clothing line of ‘athleisure’ and appeal to both sporty and non-sporty people. Big data can help fashion retailers be on the top of the game and launch the right products, at the right time and in the right way.
Analyzing what is the next breakthrough in the fashion world is just what you need to make it big in the fashion business. In order to stay relevant amidst growing competition, it is important for fashion designers to come up with unique products. However, trends are hard to analyze using traditional monitoring techniques. By integrating big data with AI and ML, retailers can use live insights to create the most innovative fashion trends. Big data will also help you analyze customer behaviour to suggest a product price that will ignite demand while locking a rise in overall profits.
If you own a fashion brand and could use data science training to consistently create winning products, wouldn’t you do it? Most definitely yes! So engage the loyal customer, create new customers, be a trendsetter and most importantly break a brand perception by adopting big data and data science at the earliest.
And that’s a wrap! Hope this blog post proves to be insightful for you! Feel free to share your thoughts in the comments section! Also, If you seek to upskill your Data Science skills, feel free to check out our Data Science Courses here. Manipal Prolearn Data Analysis and Visualization course equip you with sound knowledge of Data Analysis and Visualization and their importance by covering all the essential concepts in the domains. By learning about Exploratory Data Analysis, you will gain an in-depth understanding of which ML algorithm to implement for particular data sets and a lot more.
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