Many high street retailers in the UK are experiencing falling footfall in their physical stores as customers increasingly switch to online competitors for better convenience, choice and prices.
One way retailers combat these disruptors is by analysing their customers’ buying behaviours to identify new ways to further differentiate their in-store and digital offerings – for example, using insights from loyalty card data to target offers at individual customers.
Yet such an approach gathers data from isolated, self-contained engagements that only form one part of a customer’s time spent on the high street shopping – the customer experience as a set of disjointed silos versus the seamless, end-to-end personalised experience of buying online. Arguably this fragmented engagement is also one of the root causes driving customers away from physical retail stores.
One way to potentially address this challenge is by competing retailers sharing real-time, dynamic in-store customer browsing and buying behaviour data. This free flowing – “liquid” – Big Data would enable collaborating retailers to make on-the-spot offers and other personalised engagements to a customer directly in their stores based on an individual’s wants or needs that day; creating moments of customer delight not even possible when buying online (with the added bonus that purchased products are available to take home immediately). This co-opetition model would be mutually beneficial to all with any revenues from sales (direct, cross or up-selling, etc.) attributable to this process being split equitably across participating retailers.
An example of this model could be a “Virtual High Street Assistant” – a mobile app that a customer opts in to that gathers data about their behaviour across different high street stores (such as recording any purchases made using Mobile Payments or by detecting IoT sensors in labels of products being browsed). Cloud Analytics is continuously, rapidly analysing this data for insights like identifying cross or up-selling opportunities to complement goods already brought, the price a specific customer is likely to pay for an item they have been browsing across different stores and potential Social Media activities that could promote further engagement. Based on their smartphone’s GPS location, suitable insights (like nearby relevant special offers) are then shared with a customer via the app. In addition, analytics aggregates different customers’ data to identify any buying trends on the high street that day and makes recommendations to participating retailers about how to best exploit these sales opportunities.
This notion of Big Data as “liquid” that can be shared (rather than solid, hidden in one organisation’s silo) is not without considerable challenges and barriers. In addition to technology, there is a range of legal and security issues affecting the use of personal data in such a model. However, UK Health and Social agencies have been pioneering new ways to share highly sensitive patient data across different organisational boundaries to improve services. Learning from this experience could help build a compelling business case to fund a pilot to test the risks and benefits of “Liquid Big Data”.