Shoppers have digital touch points – online, social, mobile and stores, and all this information can be brought together to build business insights.
Systematic and data built customer profiles help customers find the products they want quicker than ever before, and in many cases the items find them even earlier.
At every stage of retail, you can unearth copious amounts of structured and unstructured data about customer behavior and interests. Predicting trends, forecasting demand, identifying interests and giving them an exemplary shopping experience is all within reach when you bring in analytics and insights.
Being in-sync with the customer and forecasting demand not necessarily related to customer retail inputs alone. Economic indicators, demographic data, in-store purchases etc. will all contribute to better demand management.
Social buzz and sentiments drives sales to a great extent. Trend predicting algorithms comb social and web browsing history to determine context, analyze top selling products and accurately predict the season’s best for tomorrow.
Predictive approach to the rise and fall of product demand can help in an accurate pricing strategy. The best play in price reduction to boost demand and increase revenues can be zeroed in through algorithmic tracking of demand, competitor activity and real-time price updates.