Is Big Data the New ‘Show Stopper’ in the Fashion Industry?

Mar 17, 2018

fashion industry

For long, the applications of big data analytics in various industries such as healthcare, finance, marketing, and telecom have been the talk of the town. Interestingly, the use cases of big data are not limited to these sectors alone. This advanced technology is slowly spreading its wings in the fashion industry. With a large number of designers and the latest technologies becoming prevalent in this niche market, the fashion industry is not all about playing the ‘dress-up’ game anymore. Big data has given countless businesses a substantial competitive advantage by redefining the quality of data available and the speed at which they can respond to market conditions. And the icing on the cake? Profits have soared consequently. Though the fashion industry has lagged behind when it comes to embracing this technology, the stakeholders in the fashion industry have realized that data analytics can work wonders for them. Here are five ways big data analytics will be a game-changer in fashion:

Analyzing trends

The trends in the fashion industry change faster than you can change your clothes. What was trending yesterday could become outdated by the next fortnight. In such a dynamic market, monitoring the changes using traditional methods often prove to be ineffective. Using big data analytics, fashion industry players can easily understand the market changes using techniques such as sentiment analysis on social media and know their target audience. What more?  Data analytics can analyze the impact different seasonal trends have on the buying behavior. This will help retailers in the fashion industry to make the right merchandising decisions in the future.

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Identify target markets

It is a stated fact that culture influences fashion and trends to a large extent. For instance, a fashion trend that is ‘hot’ in the USA might be considered too revealing for middle-eastern countries. However, the same designs can be widely accepted in countries like Spain. Big data helps you analyze the preferences of people across the globe and provide insights into the changes in mindset with culture. With this, fashion industry retailers can target a wider audience for their designs; thus, bringing more potential for outreach and revenue.

Opportunities for new designers

Merchandises under the label of top designers in the fashion industry bring in good money to big size retailers. But the drawback of these designs is that their prices are high enough to burn a hole in your pocket, which is something that won’t please customers of mid-size retailers. Using big data analytics retailers can analyze the designs of new talent and predict the impact of the designs on the market. This will facilitate medium-sized retailers to make purchasing decisions about the newcomers in the industry, which will, in turn, uplift new designers and increase sales of mid-size stores.

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Improve conversions

Retailers in the fashion industry often face the problem of improving their conversion rates. New collections often face the issue of not being able to convert high ticket purchasers. What is the solution for this? Markdown optimization might be the answer that you are looking for. This technique analyzes customer behavior and suggests a price that induces demand and ensures stock clearance while ascertaining a rise in overall profits. This technique can be highly useful to increase the effectiveness of clearance sales at the end of every season.

Improve cross-selling

Cross-selling and up-selling are retail techniques that are used to ignite more sales. By leveraging data analytics techniques like market basket analysis, retailers can analyze which products consumers are likely to buy in the future. This mechanism uses historical purchase data to identify which products go well along with each other. Using this data, retailers in the fashion industry can send more effective marketing messages. Market basket analysis helps to upscale revenues by facilitating in cross-sell more efficiently.

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