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Data Science Course

The Data Science Behind What’s Hot and What’s Not

Posted on August 30, 2019 by Kritika Gadhvi  | Academics | Blog

Growing up, (and even now, to be honest), I would gaze the high-end fashion magazines for hours to comprehend the glamour oozing out of the Hermes Birkin handbag or the bottle of Chanel No. 5. It would take all my early-teenage mind to correlate the prices of these products with the status quo they represented. As I got older, I realised that fashion is an intricate part of our lives, irrespective of the fact whether you follow it or not. Now, as a fashion enthusiast and being a part of the generation that has access to more information than we will ever imbibe, I can safely say that this industry is one of the most dynamic and fastest-evolving. What's hot today might be stale by the time the sun rises the next day.

Fashion, as an industry, has a long-standing history, which gives it the power and most importantly, a humongous amount of data, to be the driving force it is today. Ever wondered why the Chanel 2.55 is an iconic bag and has been recreated by designers all over the world? Or why the tiny sunglasses went out and oversized sunglasses are back in fashion? These are not just strokes of luck or fluke happenings; the modern-day fashion moguls have volumes of unorganised data that they can leverage to tap into the customer psyche.

The fashion industry has two major aspects- design and retail. Now, one wonders, where is this data coming from? The brands keep a track of the consumer spends through digitisation of trade. With every firm having an e-commerce presence, there is easy availability of records. Additionally, social media has soared new heights in this digital era, opening doorways for new metrics like engagement, trending posts, hashtags, celebrity styles that are turning heads, blogger reviews, likes, comments and shares, and many more.

Even in offline sales, the retailers of high fashion mass consumer brands look at how long a consumer spends time in the store, which section does he/she spend more time in, how often do they come back and their spending habits on each section. These metrics help the brands identify and segment their audience, taking data-driven decisions concerning marketing and promotional campaigns.

Comparable parameters are taken into consideration while assimilating the online data. How many times a person visits the website or app, the frequency of the visits, did they end up buying or abandoned the cart, how much time did they spend on a particular section or a product? Identifying patterns and relationships in this data can answer something as elementary as ‘what colours a particular shirt should be printed in’.

Huge brands have the bandwidth to employ data scientists that examine the haute couture brands’ collections year-round. They recognize the hidden trends in the cut, patterns, colours, fabrics, fit, length and much more that get the approval from the society as a collective. Interpreting the sales numbers using big data algorithms also helps brands put a price tag on each item. The data analyzes the consumers’ purchase trends, frequency and the highest amount they are willing to pay.

Data analytics and machine learning programmes also aid in revealing what products should be kept on the shelves, which categories should be discontinued, what new products/ clothes/ accessories can be introduced and what should be brought back. For instance, the early 2000s saw a throwback rise of the 80’s trends. We now see models, celebs and even the kids next door bringing back the 90’s fashion in a distinct way.

Consider the case of sunglasses. Gigi Hadid became the pioneering sensation of tiny sunglasses and suddenly, everyone jumped on the bandwagon to create and wear them. She started a trend that received more raving reviews, shares, hashtags and comments than ever! Once the buzz around them had subdued a little, there was a disrupting move by none other than the Duchess of Sussex herself. Meghan Markle stepped out in a pair of oversized sunglasses, and the tiny ones, while not forgotten, got put on the backfoot. This goes on to show that even the smallest data metric when explained has a strong story to tell.

The data scientists’ and their wealth of knowledge in sifting out information is useful to such an extent that they can help create entire floor plans for fashion houses. Ever wondered why small items like hair ties, clips, key fobs, socks, perfume samples, cosmetics are placed near the checkout counters? Well, that’s because such items are almost always 'impulse buys' and the data on these purchases is what helps fashion houses and brands figure out which items go where. The consumer shopping data helps in determining the behavioural patterns and buying trends which are then used to create store setups.

This simply goes on to demonstrate the omnipresence of data and the application of data science tools to make informed decisions, from minute to large-scale. An industry like fashion, largely assumed to be driven by impulses and sentiments, too, is now directed by the advanced data science algorithms and analytics tools.

It is precisely for the fact that their skillset is not restricted to merely one area of the global world that the job of a data scientist has been stated as the hottest career of the 21st century and Data science courses are a trending choice. The knowledge and expertise gained can be applied to any sector and functional area, as we just saw in the case of the high flying fashion world.

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