In the fifth of our articles on the Store of Tomorrow, we explore how this offline-online model positions customers at the heart of the business—by using data, AI and machine learning to generate insights and fuel decisions.
Retailers today face a growing array of customer expectations—for more flexibility, more availability, more personalization, greater sustainability, and a cohesive experience across ecommerce and in-store shopping.
As retailers plan the Store of Tomorrow, they must look to meet these expectations while increasing organizational efficiency and driving profit by integrating their online and offline operations.
The critical importance of data in retail
The Store of Tomorrow illustrates how this can be achieved through a flexible modular approach. It allows retailers to mix and match different components—dematerialized sections, sensory and social experiences, digital customer experiences, hyper-efficient micro-fulfilment—according to the needs of each individual store type.
Data, and the insights provided by advanced analytics, underpin all these capabilities. As they plan their future data strategies, it’s important that retailers lead with value, asking:
Retailers should be looking to use data to provide new customer experiences with new services, supported by digital payments and optimized delivery across channels.
For example, the right data can enable automated decision-making, in real time, across the retail store. That might mean transforming customer experience by providing personalized product recommendations via interactive screens or smartphones as shoppers walk round a store. Or providing full visibility into a whereabouts of a delivery.
Retailers need to invest in their workforces by offering retraining and upskilling programs, while ensuring they build workplaces that are inclusive and able to maximize the unique value that each individual brings. Empowering retail workers with data means giving them actionable insight, which should look to augment their retail expertise, thereby helping them decide what activity would be most valuable at any given moment.
Right now, is it most valuable to go restock a shelf, open the delivery gate, or take this priority pick and pack order? This decision can be supported with in-the-moment micro-training. For example, it might ultimately make most sense to have an untrained member of staff fulfill a pick and pack order more slowly, with digital guidance, than waiting for a trained staff member to become available.
Retailers should be looking to enhance network operations through improved data analytics, optimized supply chains, and greater productivity. That should include identifying omnichannel synergies from closer operational integration across the business—everything from product discovery to payment to fulfillment.
Automation is a central capability. For example, retailers should be looking to train algorithms that learn from employee behavior and so automate a greater number of operational decisions. Over time, this should also enable optimized scheduling by better understanding and predicting workload peaks and troughs.
Retailers should also consider how to monetize the media space within the store. That might include, for example, selling targeted advertising space by using data to show the right customers the right ads at a digital display.
Put experimentation at the heart of the retail strategy
The Store of Tomorrow contains many advanced data-driven capabilities that are still new to physical retail. To harness the full value from data, retailers need a spirit of experimentation and exploration, which focusses on business outcomes and specific use cases, as well as a change in culture, processes and ways of working across the organization and with key partners. Welcome to the Store of Tomorrow. Read full report.