Mumbai: Retailers can benefit immensely form a structured analytics-driven approach that will help them understand how their customers are using their products and services, how their operations and supply chain are performing, how to manage their workforce and how to identify key risks—insights that they then can then act upon, according to a new report titled Driving retail growth by leveraging analytics by consulting firm PricewaterhouseCoopers (PwC) and the Retailers Association of India (RAI).
A successful retail analytics strategy, the report suggests, will cover these six areas:
Predictive modelling: Developing an analytical model to predict future outcomes and empower business users to take decisions quickly.
Big data and hybrid architectures: Convergence of structured and unstructured data through data integration across apps, sensors, social media and other channels.
Cloud analytics: Highly scalable and easy way to store and access relevant information, which allows users to access more data faster.
Advanced visualizations: Present data in visually compelling ways, enabling companies to expand business intelligence capabilities extended to their executives and other employees.
Self-service analytics: Making analytics a more democratic process by allowing users to make decisions based on their own queries without requiring any sophistication.
Real-time in-memory: A move ahead of the traditional relational database that can help retail analysts to generate deeper insights across the entire value chain of retail operations, including procurement, supply chain, sales and marketing, store operations, and customer management.
The report also recommends an analytics framework that retailers can use to structure their programmes in four areas—merchandising, marketing, supply chain and store operations.
Merchandising analytics: Retailers can use merchandising analytics to stock the right product at the right place at the right time. Merchandising analytics enable planners to align their merchandising decisions with customer expectations. The key areas of merchandising analytics are assortment planning, product adjacency and space allocation. Analytics holds the key to optimising assortment. For each stock keeping unit (SKU), retailers can identify a few attributes, such as brand, package size or flavour, that are meaningful to customers. They can then use the sales of existing SKUs to estimate the future demand at attribute level and further use these estimates to forecast the demand for any combination of attributes, including those that correspond to new products the retailer is considering to add to its assortment. Analytics lets retailers discover new products that have high chances of selling well, the report says.
Marketing analytics: To keep up with changing customer demands and ensure loyalty, retailers need marketing analytics for deeper customer insight, targeted interactions and improved customer service. Marketing analytics quickly combine all relevant customer data—from point of sale systems, customer relationship management system, loyalty cards, etc., with social media data—perform sophisticated analytics, and share insights to help optimize marketing decisions. It can help to deepen customer insight, optimize multichannel performance, improve marketing effectiveness and enhance social media presence.
Supply chain analytics: Retail profitability is directly impacted by the logistics efficiency to maximize demand fulfilment and avoid any back orders or stock-outs. These include interventions in logistics, inventory and supplier performance. Advanced analytics solutions using a global positioning system (GPS) can help in tracking the movement of the fleet, understanding the behaviours of the drivers, identifying hazard points on the routes, etc. This, the report suggests, can help in reducing the overall costs, make logistics safer and efficient.
Store operation analytics: More and more retailers are adding sensors to people, places, processes and products in order to gather and analyse information for better decision-making and greater transparency. Predictive analytics applications process this data, optimize the supply chain and decrease inventory shrink. Retail stores are increasingly adopting sensors to determine inventory levels and restock shelves automatically. Location analytics can map how customers move through a store. Using a combination of IoT-enabled product and shelf sensors, cameras and RFID (Radio Frequency Identification) devices, one can track which sections of the store receive the most traffic in general over different hours of the day and week. Going forward, the report suggests that retailers can view IoT (Internet of Things) as a tool that enables them to help their customers through innovations such as smart price tags that can change prices in real time, mirrors that allow a person to try clothes on virtually, and packaging that monitors the freshness of goods and alerts the consumer when they are nearing the end of their shelf life.
Retailers, the report concludes, have already started putting data analytics at the heart of their operations across the value chain—procurement, supply chain, sales and marketing, store operations, and customer management.
However, they now need to establish a big data ecosystem, which processes multiple terabytes of new data and petabytes of historical data, which will help them improve their revenues via analytics-based decision-making.