The fashion industry is irrefutably one of the most prominent segments that embraces latest technologies in the fastest possible manner. While trailblazing eCommerce trends like Augmented Reality, Chatbots, AI, are making online shopping an electrifying experience, retailers on the other side need to start deploying analytical trends to take their sales to soaring levels. Owing to this, robust platforms like Magento and Shopify are integrating business intelligence solutions to assist retailers with real-time analytics on customer behaviour. Fashion is a best-known industry that explores latest trends for profits augmentation and hereby are the emerging analytical trends that are being gradually grasped by fashion merchants for boosting their retail sales.

Digitalisation and SMO optimisation

Retailers of fashion sites spend lucratively on marketing apart from promotionals through all possible digital mediums including social media to lure more customers. Social marketing and online advertising became paramount for online businesses as it brings forth the brand value and image to customers. Later, social media analytics and digital marketing responses reveal the influence of brand on customers and level of their satisfaction with the products. Small instances are the number of likes in brand’s Facebook page, number of views in a promotional YouTube video, or the number of shares made from a blog.

Anticipation of recent lifestyle trends

In such a highly volatile industry where fashion trends come and go in no time, merchants need to be impulsive to change their clothing and apparel collections and ranges as per the latest lifestyle trends. To get insights on fashion trends, they need to accumulate information from contemporary retail sites, social media, fashion articles and runaway reports, and blogs across all apparel categories, accessories and lifestyle items. Such detailed analytics aids eCommerce merchants in taking decisions for prompt sales.

Optimising for festival/holiday sales

Most fashion merchants face the backbreaking challenge of sales fluctuations on different seasons. They often fail to capitalise the unerring opportunities in the peak seasons and boost customer loyalty with alluring offers. With real-time analytics on the current market scenario, retailers can offer catering to the changing preferences of the buyers, negate surprises and maximise profits. In other words, time-to-time analytics help fashion eCommerce merchants to gain supreme flexibility in their supply chains.

Personalised recommendations

While upselling and cross-selling are innovative ideas for cumulating sales and conversions in online shopping sites, they can be employed in an effective and personalised way to engage more customers. By analysing the core customer data like age, gender, region, retailers can perceive the buying behaviour to some extent. Depending on the findings, they can show personalised offers or product recommendations. The more retailers are capable to judge the purchase behaviour and attitudes of customers, the better they get in deciding the products for upsell and cross-sell criteria.

Brand engagement through mobile use

Brand engagement is one of crucial success indicator for eCommerce retailers, be it fashion or any other sector, that is further driven through mobile use. Thus, besides using up different online ways including social media for increasing engagement, retail brands also require taking their online store to mobile platforms, through which they can interact with customers better. Brand engagement is positively correlated with in-store experiences. This rightly implies the better customers engage with you through mobile, the more satisfied they are with the online store.

These are the few most prevalent trends observed in fashion retail sector in eCommerce, for the sole purpose of augmenting sales by catering to the changing market scenario and altering customer behaviour. However, what Magento and other likewise platforms will unfold for enhancing eCommerce analytics in coming days is worth knowing.

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