From insights to foresight
Modern-day retail and leisure is driven by the empowered, tech-savvy shopper, who can access multiple buying channels in a jiffy, compare pricing and deals, and ensure that they get the best return on their expense.
Customers counted and rising.
Use Cases
Insights for Store Expansion
Companies wanting to expand their physical global presence utilize predictive analytics to understand complex and often expose the previously hidden aspects of opening a store at a new location. Predictive analytics provide retailers with insights regarding audience reach, product preference, potential sales, store layout, and much more to compare locations and finalize the expansion plan and store design.
Improve pricing and forecast revenue
Predictive analytics enables the retailer to consider data like weather forecasting, real-time sales data, inventory levels, purchase history, product movement, and much more to design meaningful marketing campaigns and define ideal sales price and promotions. Retailers need to be able to consider historic, most recent, and real-time data to predict potential future revenue. Buying patterns, market trends, and socioeconomic conditions are considered to produce the right actionable data to reveal employee-wise or day/hour-wise revenue. Retailers use what-if analysis and A-B testing to change independent variables and see the resultant effects.
Enhance Inventory Management
Accurate inventory management combined with availability, demand and sales are critical to ensure the proper interpretation of the underlying reasons for slow or fast moving products. Retailers are made aware if a high demand product is unavailable at a certain location and take action to make it available. Retailers then use this historic data to predict what to stock at what place to optimize revenue. This greatly assists in satisfying customer needs, reduction of loss of sales and inventory cost, and streamlining the complete supply chain.
Provide Personalized Recommendations
In any competitive retail space, to stay ahead, retailers need accurate recommendation engines to be able to effectively upsell while establishing customer loyalty though a positive customer experience.
Data needs to be analyzed from various kinds of sources and analyze footfall, product promotion and interaction, and other data to generate accurate insights. Such contextually applied data considers past buying pattern, motivation, interest to produce the most suitable product recommendation personalized for each customer. The crowdsourced data produced by such systems predict the customer’s next likely purchase based on their behavior and will be able to make upsell suggestions with a high conversion expectation.
Optimize Trade Promotions
Trade promotions being an irreplaceable part of retail marketing need to be analyzed and optimized in detail as much as the online channels. Business Intelligence tools equipped with predictive analytics analyze structured and unstructured data derived from all the possible customer behavior touch-points and generate an array of valuable and actionable insights. Retailers use these tools to better predict which trade promotions will be more rewarding in terms of location and ROI and which ones should be eliminated.
Improve Speed of Service and Experience
Real-time data placed in context with historical customer behavior data, empowers venue operators to for instance reallocate staff from the back to take orders at the counter or from people in line. Operational data broken down by the hour is used to optimize a venue’s hours of operation for each location. Being able to make these adjustments based on historical facts lowers overhead on staffing and positively affects customer satisfaction. Such location specific historical data is also a roadmap for expansion of new locations to better accommodate guests.