The fashion industry is known to be fickle and subject to changes that are dictated mainly by the designers and changing taste of the consumers. Since creativity has been the major factor to drive the fashion business, it was considered difficult to predict future trends accurately. However, Big Data automation tools and analytics have cut through this perception barrier and given a boost to the fashion sales in recent times.
Wrong pricing – one of the main factors of decreasing sales
Julia Fowler, an Australian fashion designer, believes that products in the fashion industry are wrongly priced due to an improper judgement which in turn is caused by the lack of concrete data that indicates the changing tastes of consumers. To rectify this very root cause, she founded EDITD, a fashion apparel warehouse that supplies key data to the fashion retailers that help them push the right product at the right price and at the right time.
Merging of data with the analytics
EDITD uses Big Data tools to aggregate trends and sales in the fashion industry through various sources like retail websites, fashion runway reports, social media sites, fashion blogs, and so on. The dataset is as comprehensive as it gets with about 53 billion data points that cover around 1000 retailers from the world over. The retailers who use the EDITD apps get their data in a simplified manner. They are also able to customize the results to meet their requirements.
One of the key factors to determine the fashion trends is the influence of designers. Big data tools are used to pick and map these influences, find a pattern, and predict the style changes. Heng Xu, of the Information Sciences and Technology, Penn State, and her team analysed the runway reviews of 816 influential designers to identify the keywords and phrases like the fabric, color, origin, and mapped them within the dataset to establish a definite pattern. They even corroborated the results by comparing them with the list of the top designer labels.
How does Big Data make an impact?
This goldmine of data will be able to:
A boost in sales revenue: Sources reveal a 37% increase in revenue for a leading retailer, Asos, as a result of the applied insights from EDITD. Pushing the right product at the right price is the secret behind the success of companies like Asos.
Determination of the right price: For retailers, perfecting prices is one of the main advantages derived by Big Data analytics. The same applies to the fashion industry that is perhaps the most affected by incorrect pricing. The Data gives the retailers a chance to correct an overpriced or under-priced product.
Identification of best-sellers and poor performers: Customer reviews reveal a lot about their taste and the market value of a product. A product that is not doing well can be modified or trends that are not popular can be taken down from the market.
Competitive comparison: Knowing the customer is important but equally important is to know how the competitors and their products fare in comparison. The retailer strategies, products, and their prices must be in line with the industry standards and Big Data analytics help retailers achieve that.
Insight on the pulse of the consumer market: While a buzz around a product gives the brand valuable insights, talks around the competitor’s product help the organization re-think on the product strategies. Changing customer preferences can be sensed early on and remedial action can be worked upon.
Big Data tools and automation will facilitate a responsive market for the fashion industry where a two-way communication between the labels and the consumers allows for the brand to be more sensitive and appreciative of the consumer likes and opinions. The consumers get a chance to be in control of what they prefer to see on the shelf rather than accepting trends blindly. It’s definitely a win-win situation to be in.
The technology is still in the nascent stages with fashion designers expressing skepticism over the credibility of such analytics for an industry that operates on instinct and whim rather than logic. However, researchers are confident that as the data and its sources improve the day is not far away when the likes of Twitter and Instagram will be used to predict styles.
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