Using big data to drive retail spendingSeptember 14, 2016 / By
Every time you shop, you potentially share your consumption behaviour with retailers. Through data analytics, retailers can infer consumer preferences and patterns. For example, women in the first 20 weeks of pregnancy typically load up on supplements such as calcium, zinc and magnesium. Through the history of purchases and other demographic data, a retailer could infer if a particular woman was pregnant. When the same customer suddenly purchases large quantities of scent-free soap and lotions and extra-large bags of cotton wool, the retailer could infer that she is close to the delivery date. With a deeper understanding of a customer’s preferences and needs, retailers are able to target individual-specific promotions and boost retail spending.
In Singapore, landlords and in-store retailers are currently using loyalty point programmes to drive retail spending by encouraging return visits based on aggregate spending, without analysing data. In contrast, online retailers use a superior data-driven approach, by suggesting specific items to consumers based on past shopping purchases, and even items being researched. Clearly, landlords and in-store retailers have to catch up on the use of big data to enhance the in-store customer shopping experience and operational efficiency.
Recently, CapitaLand Malls Asia (CMA) announced a tie-up with American Express (Amex) for a co-branded credit card. Based on Amex’s analysis of the customer’s detailed spending patterns, CMA could adopt individual-specific promotions to boost retail spending and improve customer retail experiences, rather than the non-targeted marketing noise prevailing in the industry. For example, a customer visiting a Chinese restaurant in a mall for the first time could have a targeted promotion to revisit the mall for other Asian restaurants or a newly opened Asian restaurant. A customer signing up for a gym membership could receive a targeted promotion for sports apparel in the mall. This is possible as Amex’s unique closed loop network provides a view of both detailed retail spending data and payment processing data. As a result, Amex is able to provide aggregated data insights that add value for card users, retailers and its partners, and in this case, CMA.
Notwithstanding the benefits of data analysis, landlords and retailers will need to strike a fine balance between serving customers and an infringement of privacy. Users of data analytics must be mindful of not being too invasive to the point it creates discomfort for customers.
Ultimately, big data used properly can lead to increased customer satisfaction from relevant promotions and improve tenant sales and/or profitability with higher spending and more repeat visits. Additionally, landlords can use big data to reposition the mall quickly to meet changing customer tastes and improve mall rents and capital values. Going forward, big data is likely to drive the future of retail as more landlords and retailers embrace the use of data analytics, and meet the challenges of the changing market place and regain a competitive edge.
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