In-store analytics is the process of analyzing all the data collected from within the store and generating insights for the retailers or store managers to optimize store performance in multiple ways. Retailers can get an overview of the end to end operations, as well as view the deepest level of insights by analyzing consumer behavior and sales data in correlation. This data-driven intelligence can lead the stores in improving their marketing strategies, generating better sales, and enhancing customer satisfaction.
Importance of in-store analytics:
The customers of today have an increased level of awareness. They have access to all kinds of information about a product, company, possible alternatives, availability of better prices, and more. This makes the retail space very competitive. To survive and stay ahead, it is imperative for retailers to plan their strategy intelligently. To achieve the two major objectives of retail stores i.e. improve the sales and provide a good consumer experience, retailers need the power of analytics.
Retail analytics can provide detailed information on a consumer basket, the number, and the value of products purchased and can provide an average view. Retailers can figure out their best-selling products and brands and also the ones with the least movement. Analytics can help figure out the peak shopping hours and can give a detailed view of each store executive’s performance. Using these insights, retailers can make enhanced sales, marketing, and supply chain strategies to improve overall operations.
Benefits of In-store Analytics:
Enhancing Queue Management and Customer Experience
Retailers can use analytics to enhance queue management at stores. They can know the average number of people in the queue, the average time spent there, and the number of people leaving the queue without checkout. Managers can determine the optimal queue length, allocate the right number of checkout assistants, and can also predict the potential queue length at any date or time. This can help in reducing the time spent in the queue, avoiding any crowding, and preventing checkout abandonment. Retailers can improve customer experience, reduce costs, and induce customer loyalty.
Store Traffic Analysis
Retailers can analyze the real-time foot traffic data, to know the number of people, entering, exiting, or passing the store. The store manager can identify the peak sales days and hours and allocate resources and staff accordingly. With analytics, the sales volume per store and conversion rate can be found out. Retailers can know their best performing and underperforming stores and take corrective actions. Using sales and customer behavior data, customers can be segmented correctly and be served with the right strategy to optimize sales and experience. Stores can also improve the store assistant coverage by analyzing the customer to associate ratio and corresponding sales data.
Store Heat mapping and Consumer Journey Analysis
Retailers can now have consumer journey analysis in real-time. They can track the consumer path and know the areas where they are buying from and where they are browsing. This enables to identify the prime shopping areas and strategize for better product placement and overall merchandising. With heat mapping, stores can know the attractiveness of recently launched products and make better decisions on improving its placement and the POS setup. Store managers can use sales data to know if a product is underperforming at a prime location and make replacements to improve sales. They can analyze various trade campaigns and store display performances and can improve marketing strategies. With an overall look at the consumer journey, retailers can know the areas visited, correlate abandonment, and enhance the store layout.