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Retailing in the Age of Big Data Analytics

The last couple of years have witnessed a massive shift in the ways customers shop due to the COVID pandemic lockdowns; but the pandemic has only accelerated the shift that was ongoing for the last two decades through technological advancements and big data.

By Jadd Elliot Dib, Founder and CEO of Pangaea X

When the rise of social media at the turn of the millennium gave retail a voice, and social media adoption and e-commerce started to grow, retailers understood that listening to customer demand was key to staying relevant in the market.

This data-driven revolution underpinned by extraordinary convenience and unprecedented swiftness significantly impacted the traditional brick-and-mortar model for retailers. In the UAE, e-commerce sales were predicted to reach $16 billion pre-pandemic. Post pandemic e-commerce sales grew by 35% in 2021 when compared to 2020 showcasing that the shift is continuously accelerating.

Retailers today have started to engage with customers on e-commerce websites and social media through advertisements and campaigns that aim to help them connect with their customers. But how can they be more successful in connecting with relevant customers? The answer is Big Data.

Big Data analytics is being used at every stage of the retail process to understand customer behavior, predict demand, and optimize pricing. Big data applications are helping retailers drive down costs, revolutionise supply chains, improve online and in-store customer experiences, and target customers effectively.

How Big Data Analytics works in retail

When a customer interacts with advertisements or posts on social media, the retailers understand their preferences. Retailers analyse millions of data sets belonging to customers to better comprehend the way a customer shops.

Big Data Analytics serves as an important tool for retailers to make crucial decisions such as improving in-store shopping by providing experiences to certain buyers or promoting a product exclusively online as the audience for it does not purchase the product in-store. For instance, traditional shops that just ‘sell’ phones have been replaced with experiential stores, which offer a more immersive experience to customers by engaging them with the device, its brand and culture. This trend reflects the fact that most people are aware of the phone’s appearance and features through social media and other online sites and only need to experience its usability. Hence, experiential stores allow these prospective buyers to get a feel of the phones, ensuring a better return on investment for retailers by providing customers with relevant experience instead of repeating the already available information online.

Similarly, Big Data Analytics can be used to predict trends and customer reactions to product pricing or modifications in a new model of a product. With the analysis of social media feeds and sales data, retailers can simply fine-tune their pricing and innovations to facilitate customer feedback and loyalty.

Supply chains have always been difficult to manage as they require a certain degree of predictability of customer demand. With Big Data Analytics which looks at millions of data sets from social media and e-commerce websites, supply chains can predict patterns, and seasonal demand and track stock levels to efficiently manage supply chain operations.

With the adoption of machine learning algorithms and Artificial Intelligence, Big Data Analytics can bring more utility to retailers in their quest to serve customers better and improve margins. AI can bring forward a landscape that is increasingly customer-centric and technology-driven to revolutionise the way retailers engage with their audience and define the offerings they have.

While Big Data Analytics has been revolutionising the world for a while now, exciting times lie ahead of us as the application of this technology becomes widespread and advanced.


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