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How big data can equal loyal customers for retailers

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In the past few years, retail executives have been keeping a close eye on innovation not only in terms of products, but also in terms of how to predict the most effective forms of customer service, pricing and merchandising for their brand. There is little doubt that big data will be the next frontier in helping retailers make smarter and timely decisions to give customers exactly what they are looking for, writes Gary de Menezes, NetApp country manager.
Big data analytics can be applied at every phase of the retail process – from predicting trends in order to work out what products will be popular in the future, to forecasting where these products will be in demand, to optimising pricing, to identifying the customers likely to be interested in the products and then working out the best way to approach them.
The South African retail sector has been particularly successful in this arena with the explosion of rewards programmes that has hit the market. One of the leading rewards programmes locally has a little over 10 -million members and has been recognised as South Africa’s best loyalty offering three years in a row.
Going beyond merely offering promotions to their customers, these reward programmes examine consumer data, which is collected when shoppers swipe their card at the till. Each consumer’s shopping patterns are analysed by algorithms, which then recommend discount coupons based on each shopper’s product and brand preferences. These coupons are offered as a reward for shopping at the retail store, and as an incentive to shop there in the future. Thus, through careful analysis of their consumer data, this well-known retailer is able to interact on both a cognitive and emotional level to influence behaviour change in their customers.
However, while big data analytics creates big opportunities for retailers, it is still a dynamic field that is being continuously redefined. The volumes, velocity and different varieties of data make it an area open to several interpretations and it has lately become crowded with processes, tools and concepts that are complex even to the tech-savvy.
It must also be remembered that today’s average retailer already has a large pool of data that qualifies as big data. From its social networks, internal systems, clients, employees and customers, retailers generate more data than they can handle. Unfortunately, this data is of little value without the application of advanced analytical tools that can build meaningful patterns and trends, in order to give them insight into customer habits, behaviours and interests.
But even with such analytics tools and platforms, some of which are Open Source and widely available, it takes the expertise of someone with specific skills ranging from data science to the knowledge of privacy laws to decode it and use it compliantly. Such investment is not always available to research and development teams, especially for smaller, independent retailers. In addition, even when budgets are availed, the volume of data is so big, moves so fast, and is so detailed that more often than not, it exceeds the current processing capacity of the retailer.
In spite of such setbacks, big data – when captured, formatted, manipulated, stored and analysed -has the potential to help organisations gain useful insight to increase revenues; get, grow or retain customers; improve efficiency and make more intelligent decisions. As a result, more businesses, retailers included, are increasingly looking to informed big data analytics to accumulate sales figures, improve customer service, increase operational efficiencies and ultimately increase their profits.
However, there is still a long way to go for retailers in effectively implementing successful big data analytics platforms and processes that cause significant bottom-line shifts. They must continuously find ways to successfully store and manage vast quantities of data in a seamlessly accessible manner in a cohesive Data Fabric that will help them better understand consumer needs, thus connecting bricks and mortar with online offering.
When it comes to data storage, it’s all about consistency and predictability. Supply and demand will vary, and retailers should look to implement an infrastructure that can support leading-edge performance and access to data when it’s needed and bounce back to a base-level of service when not needed.
For example, a cloud such as NetApp Private Storage allows retailers to switch between clouds at any time, while keeping control over their data. Equally, for consistency and performance, products such as NetApp’s All Flash FAS 8000 series demonstrate how the high performance of flash can be delivered to retailers as a cost-effective storage decision rather than an expensive luxury.
Unequivocally, success in the future marketplace will be determined by which organisation best uses big data analytics to form a consistent and coherent view of its customers and its sector, which they can leverage to their competitive advantage.
In order to tackle the bottlenecks in big data applications, retailers should consider starting small. A well-defined pilot, for example, would be able to provide insights into the ROI of big data analytics and give business leaders confidence in their ability to properly manage their data.
For any retailer looking to get ahead of the competition and appeal to the specific, individual needs of each customer, investing in a data management strategy and infrastructure that is flexible and scalable is fundamental to their future success.