Customer segmentation and personalisation Consumers increasingly expect personalisation and to do this retailers need to segment customers in increasingly more granular ways – things like demographics, buying behaviour, preferences and purchase history. Doing this involves crunching a huge amount of data to enable highly targeted marketing campaigns and personalised product recommendations. For luxury brands, offering a highly personalised experience is all the more critical. Younger consumers in particular, who will eventually become the majority market as they mature and grow in earning capacity, require a particularly individualised “white glove” service. A brand name alone won’t be enough. Luxury brand Burberry has been pioneering the use of artificial intelligence (AI) and analytics to drive sales. It first collected customers’ personal details through loyalty and reward programs, with sales associates able to access this information in real-time. This helped the brand to offer highly tailored product recommendations in store as well as online, driving an 11% increase in revenue. Netflix is another company that leverages a vast amount of data to tailor customer experience. It collects data from nearly every interaction with its millions of customers, from the devices they use to the times they watch, when they pause and resume content, and the searches they carry out.  It also uses customer feedback to generate content recommendations, which account for 80% of content viewed. Identifying new markets and customers Using AI and data analytics can help brands discover new customer groups and expand their reach. To do this, they need more than just in-house data, but to draw in third party data from other businesses and competitors, as well as epidemiological data from government sources.  For example, luxury goods brand Shinola initially designed its Vinton watch with women in mind, but discovered through MakerSights analytics that it appealed to all genders, and deepened its buy-in by 70%.  Real-time analysis and response Customer preferences and buying intentions change constantly: even a shower of rain will result in shoppers buying different products – and not just umbrellas. One study found that people paid more for green tea and gym membership after being exposed to sunlight.  Retailers can’t afford to wait for quarterly sales figures to track and respond to changing purchase patterns. From product selection and display to promotions, they need to always keep moving, adjusting, adapting. Walmart, for example, enhances its vast amount of transactional data with information from hundreds of other sources, including meteorological data, economic data, social media, local events and so on. It has algorithms that can rapidly analyse this data and generate real-time insights into what it needs to do to enhance sales.  This influences everything from how Walmart schedules staff and optimises product assortment to determining the best checkout type for a particular store and anticipating customer needs. Dynamic pricing Data analytics can help retailers optimise pricing strategy to meet a specific goal: whether that’s higher turnover (to move excess stock) or higher profits or trying to undercut competitors and own a market segment. By analysing factors such as customer price sensitivity, competitor pricing and demand elasticity, they can identify the optimal price point for a particular product at a particular time. One retailer who does this extensively is Amazon, which uses data analytics for dynamic pricing, making millions of adjustments every day. Product prices are adjusted based on demand, competition and other factors to maximise sales and profit margins. By leveraging data analytics and combining it with AI and machine learning, retailers can make more informed decisions, enhance customer experience and ultimately drive business growth at a faster pace than ever before.  But despite the attention brands are driving with early forays into AI tools like Chat-GPT, being successful is not just about handing decisions to machines. A robust data analytics strategy will involve both technology and human insight to ensure not just an effective outcome but one that is also ethical.