By: Rajan Manickavasagam is a Solution Architect at Tesco Bengaluru.
The evolution of retail must be credited to an integrated global economy, advances in technology, the development of new business models and increase in the number of shopping channels and a well-informed, mobile consumer base. This is further contoured by trends like social media, mobility, analytics and big data which are actively defining and changing the retail landscape. The dynamic, fast-paced retail industry is no longer limited by physical and geographical boundaries. Today, no matter where you are, you can log on to an online retail site, and search and pick your choice from a catalog with tens of millions of products. With more and more players embracing the online channel, retailers are looking for ways to capture the shortening attention span of consumers with lucrative deals, highly personalized offers, and product availability across platforms.
Customers while shopping across multiple channels, such as mobile and web, use real-time information on social media, customer forums and blogs – “digital footprints” as they are called. A careful examination of such data will throw up a significant amount of deep and accurate information on buying behaviour and get a measure of customer demand. Such high-volumes of largely unstructured data pouring into enterprise datacenters at unprecedented speed from across communication channels goes by the name – “big data.” Big data is characterised by humongous volume, variability in terms of data types, and velocity of processing it calls for (3Vs).
Structuring unstructured data can help retailers “squeeze” key insights out of it. Tesco-owned Dunnhumby for instance, can crunch data and enable the leadership to make meaningful decisions around profitability, cost reduction, employee wellbeing, and commitment to the environment. The Company helped build the Tesco Clubcard Programme which is the driving force of Tesco’s retail strategy.
Savvy retailers use big data to predict trends, prepare for demand, target customers optimize pricing and promotions, monitor real-time analytics and results. With increased adoption of digital models, retailers can reap the benefits technology has to offer, stay abreast of trends, ensure a more meaningful connect with customers and target sales efforts accordingly,
By analyzing this tidal wave of data, they can customize offerings toward niche groups of buyers and create new products. Retailers can also look ahead to retaining customers, converting non-buyers into buyers, and monitoring customer preferences and purchase patterns. The retailer is on the threshold of a whole new opportunity to process data and develop better and more customer-centric offerings to ensure a deeper engagement with the customer. Sadly, only 23% of UK retailers feel they can make sense of data available to them to take the right business decisions, according to a report by a leading provider of decision solutions for omni-channel retailers. Also nearly 50% of retailers surveyed believe their current business intelligence tools fall short of their needs. 16% were confident that their data analytics tools were giving them the organisational visibility they were looking for.
The analysis of data from disparate sources is both a time- and cost-intensive exercise. Increasingly, forward-looking organisations use technology frameworks like Hadoop to uncover hidden insights around customer sentiment from tidal waves of unstructured data. This framework works by “stitching” structured and unstructured data together to provide a more detailed analysis that will significantly benefit retail’s C-Suite. Based on this powerful analytic, retailers are taking steps to create an even more favorable experience for the customer, no matter what platform she/he is on, using technologies like RFID (radio frequency identification) tags embedded into clothing, reward card apps, and so on, Overall retailers are also paying attention to improving their online merchandise.
Customer analytics is a good use case for Big Data. A retailer needs to analyze the structured and unstructured data about customers. The structured data would include data around customer profile and preferences and shopping trips. The unstructured data is about customer feedback, reviews and ratings, social media behavior.
By identifying opportunities to reduce waste, optimise promotions and to match stock to fluctuations in demand, retailers can save millions of pounds. Consider for example a statistical model which factored weather into demand forecasts. This helped avoid having too much or too little stock, saving £6m each year.
In essence, retailers must join the big data bandwagon if they haven’t already. Because big data is integral to retail. Or shall I say, retail is big data? Big data tools will make retail operations more efficient, ensure more standardization, and help retailers save money, leading to better profits. Of course, challenges are many in employing big data analytics, but these pale into insignificance beside the potential gains thereof.
