Retailers have traditionally used loyalty card data to understand purchase times, trends and preferred locations. This data can really only highlight past buying patterns, but Big Data is the tool to help retailers predict future demands and implement operational improvements. This is because Big Data offers additional data sources across various channels that include social media, web browsing, enterprise data, and demographic research.
Optimizing Operational Efficiency
Predictive analytics is being used to increase distribution reliability and decrease out-of-stock conditions by anticipating demand and executing preemptive actions. Big Data and predictive analytics can be used to identify previously hidden trends and forecast new demands. For instance, accurate predictions will better balance supply and demand for perishables.
This minimizes waste and spoilage because retail management can accurately decide which items to mark down, keep in inventory and best utilize optimal shelf space. The consumption of specific products, such as batteries, canned foods, and camping gear, is intimately connected to weather patterns. Big Data solutions can leverage weather predictions to proactively allocate supplies and determine how inclement weather conditions will impact storage distribution performance.
Retailers who have historically relied on data warehouses and business intelligence to understand customer behaviors now may access new data reservoirs. Big Data analytics will help streamline operations to meet industry changes, customer demands, competitor pressure and internal requirements. This will help provide an engaging customer experience that will drive higher customer satisfaction and retention rates.
Being able to accurately predict demand and customer preferences will help retail management optimize operations, logistics, and scheduling. Big Data can be used to formulate effective marketing campaigns that will accurately target audiences and gain better value for money spent. It can also be used for up-selling and cross-selling to existing customers based on familial and community purchasing patterns.
Big data, machine learning, artificial intelligence and advanced algorithms are often beyond the expertise of in-house IT staff. This is why many retailers and organizations chose to outsource their data needs to qualified service providers. Retailers that do this may enjoy a variety of benefits. For example, better market research may improve profit margins by localizing merchandise. That is, retail managers will receive differentiated merchandise assortments based on their local customer profiles.
On the other hand, the key to reducing stock-out situations is through accurate demand forecasting and optimal inventory planning. Retailers will understand not only by the products being sold in their stores, but the items being moved in warehouses. As a result, they may better understand which suppliers excel at reacting to changing demands. Finally, clearance and markdown decisions will be based on fair market prices, real-time shelf availability and potential inventory losses.
Retailers who better understand customer trends, market segments and logistical activities will be able to increase sales, optimize channels and improve operational efficiency.