How data science can change the way we buy clothes

Over the last decade, technology has changed the way businesses operate and consumers buy. The field of fashion has been plugged into this changing scenario from the very start. Physical retail stores are now being replaced by e-commerce websites. Online and offline retailers today are using artificial intelligence (AI) to understand their customers tastes and preferences better. Using data science, fashion stylists and designers are able to identify trends and match the expectations of their end consumers.

Simply put, data science is technology’s ability to aggregate a large set of data, analyse it, and accordingly produce insights that determine future business decisions. More interestingly, rigorous and well-thought-through use of data is also a potent tool for predicting outcomes and taking actions well in advance, based on those predictions. With the advent of technology, there is no doubt that all decisions we make—demand, supply, curation, operations and logistics—should be completely data-driven.

Consumption in fashion (more than any other industry) can be most disrupted through the effective use of data science. If you really peel away the mystique behind fashion consumption and dig into the real data, you will find that we are subconsciously programmed to make buying decisions in a relatively predictable manner.

In the global context, we have seen several companies building incredibly powerful, data-driven businesses with great success. Spotify changed the way we listened to music by using data effectively to curate playlists for us. Facebook and Instagram can sift through billions of data points to show you the most relevant content on their platforms. It is this effective use of data that brings users back to their platforms on a consistently increasing basis.

Similarly, in the field of fashion, through data science, a user’s likes or dislikes, details of items exchanged, delivery timelines, optimal supply chain management, and a variety of additional information can be collected and acted upon to ensure that the users have an incredibly seamless consumption experience.

Here’s how data science is changing the fashion industry:

Personalised fashion

The future of fashion hinges on personalisation and relevance. Given that fashion consumption is so subjective, customers do not want to see a million items when they shop (online or offline). Rather, they want to be shown only the items that are relevant to them based on their several preferences.

Given this backdrop, the use of data becomes an incredibly potent tool in providing relevance and true personalisation at scale. Through data science, brands are now able to process several hundred data points and learnings about their customers in a matter of seconds to understand their consumption preferences.

Predicting trends

Through data science, brands can continuously collect information about the changing tastes and preferences of their target consumer. This data can be used by designers to forecast trends and curate products that are in line with what customers really want. With the advent of social media, shoppers are sharing what they buy, what they like and dislike, areas of improvements, and much more. This treasure trove of information can help fashion brands make the necessary changes in their products and offerings.

When introducing new product categories, brands can be aware about the kind of customer response the product will garner. Instead of conducting on-ground surveys with the target audience, retailers can rely on data science.

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Minimising wastage and managing inventory

Data science can play a key role in predicting the shopping behaviour of consumers. For brands that produce on a large scale, it can help them predict the demand for certain products and minimise production of product lines that are not in demand.

On the other hand, it can also help retailers maintain optimum inventory levels. With fashion trends changing every few months, it’s a challenge for brands to ensure that the right products are in stock. Through data science, retailers can minimise inventory problems and offer products that are in vogue.

Fewer returns

For those of you who love online shopping, finding the right fit and look is always tricky in spite of the wide selection of products. If the consumer receives a product that doesn’t meet his/her expectation, it leads to an unhappy shopping experience. On the other hand, for retailers, the process of return and reimbursement is an added cost. Through data science, fashion brands can understand the nuances of a customer’s tastes and preferences to create products that match a customer’s expectations.

It’s important to recognise the fact that the human element must also be involved in key processes to enable the best use of data science. While machines are great at processing large amounts of data and predicting outcomes, they lack imagination and creativity.

An ideal experience for a user will have a seamless integration of rigorous data inferences derived by the machine along with human creativity to produce unique ideas on data application.

While the sharp application of data science is at the heart of every business and industry, it is even more potent when applied to the fashion industry. Where personalisation and relevance are pivotal factors to winning in the fashion space, the effective use of data becomes every retailer’s closest ally. However, it is pertinent to note that while data can be very effective for your business, it is what you do with those insights that differentiates you from your peers.

(Edited by Evelyn Ratnakumar)