Advanced analytics software, like Scylla Traffic Flow Analysis, provides retailers with actionable insights so that they can make informed decisions about how to improve the shop's performance and ultimately grow revenue and profits.
The success and profitability of any retail business depend on the loyalty of their customers. How satisfied they are with their shopping experience has a direct impact on their desire to return to the store and spend more money. Therefore, retailers are constantly looking for ways to improve customer service and create a more efficient store environment. The most important thing is to analyze consumer behavior and understand what prevents them from purchasing and what helps maximize sales. That is when advanced analytics software, like Scylla Traffic Flow Analysis, comes into play. It provides retailers with actionable insights so that they can make informed decisions about how to improve the shop's performance and ultimately grow revenue and profits.
Traffic flow analysis in retail
While the term "traffic flow" refers mostly to the movement of vehicles and people on highways, the same idea applies to explaining the movement of people in and out of retail stores as they make purchases. Traffic flow analysis relies on computer vision and machine learning algorithms to extract meaning from video camera feeds and provide analytics such as heat maps, dwell times, people counting and pathway analysis to help retailers understand customer behavior. Heat maps show the number of people in a particular area of the store. With dwell times analysis you can learn how long people spend in each part of the store. This can be used to identify problems with people waiting too long for service. People counting gives an understanding of how many customers are in the store at given times. Pathway analysis is used to track the path taken by customers through the store, which help to identify any bottlenecks, as well as popular or underused areas.
Scylla’s AI-powered solutions are used at major retailers providing all these types of analytics while delivering improved security and public safety. Scylla utilizes AI and computer vision to gather data about customer behavior, service time and popular products and provide actionable insights that can be used to improve store efficiency, enhance the customer experience, and increase profitability. Scylla Traffic Flow Analysis easily integrates into the store’s existing video surveillance system and collects data across all connected cameras. The system can be configured to measure and track specific zones at a specific time to make sure you are getting the information you need for your business.
The benefits of using traffic flow analysis
Retailers can benefit from traffic flow analysis in several ways. Let’s explore how retail store owners can utilize this technology to optimize their store performance and increase daily sales.
Store layout optimization
Poor store layout can cost retailers billions in annual losses. Customers typically don’t spend much time in a store if moving through its spaces and finding what they want takes too much effort. It is also noticed that if the retail store layout does not communicate its value after the first 5-15 feet after entry, customers are likely to leave, giving preference to other, better organized stores.
By using video analytics software, retailers can better understand their shoppers’ needs and in-store behavior. They can spot bottlenecks and choke points and use this information to remodel their existing layout to make it easier for customers to navigate the store. That will help to reduce frustrations, thus creating an inviting atmosphere and a great shopping experience for visitors. The more time customers spend in the store, the higher the chances are that they will end up buying something.
It is also critical for retailers to spot areas of the store that are underperforming. By analyzing foot traffic patterns and customer dwell times, AI analytics can identify which products and sections of the store are being overlooked and take steps to increase their visibility. For example, by moving products to a more suitable location or promoting them more heavily to increase sales.
Based on the analysis of customers’ dwell times in different store sections, their favorite shopping periods and preferred products, store managers can make decisions on how to better place merchandise in the store and manage their inventory to avoid out-of-stock scenarios and prepare for seasonal peaks. Retailers also use AI analytics to improve store design to guide customers to desired areas, or draw their attention to hot items.
Improved customer service
The conversion rate is intrinsically linked with staff efficiency. Customers expect shop assistants to be available at the right time and provide good and fast service. It is observed that store associates’ performance is likely to go down when they have to serve more than seven customers at a time. That negatively affects customer satisfaction, and the conversion rate drops significantly.
To understand whether employees handle their responsibilities properly, it’s critical to assess in-store traffic flow, determine where customers linger, identify bottlenecks and other points of frustration that may cause customers to leave the store. That is what AI analytics can do to assist retailers. With data provided by traffic flow analysis, retailers can optimize staffing levels. For example, if a particular area of the store is consistently congested, it may be considered to expand the area or add more staff to alleviate the bottleneck. Besides, by tracking the time employees spend on restocking, customer interactions, and check-out support, it is possible to assess their productivity and identify roles they are better suited to.
Another way to improve customer service is to provide real-time alerts to staff when a customer requires assistance, such as when they are unable to locate a specific product. Heatmaps may be used to see where customers spend more time and increase product training to help with inquiries. This will ensure that customers receive the help they need and improve their overall shopping experience.
Traffic flow analysis can help retailers reduce queuing, which is one of the major sources of customer dissatisfaction in stores. Apart from frustration for shoppers, it results in significant losses for retailers—around $40 billion every year. Studies show that it is in the nature of human beings to rather not buy a product than have to wait in long lines to get it. Therefore, many potential customers simply abandon their purchases and do not return to this store again. Real-time people counting gives retailers the opportunity to estimate the number of visitors per day, identify the busiest hours, and make informed staff decisions to optimize the work of check-out points and reduce queue time, thus increasing their shoppers' satisfaction.
Personalized customer approach
In a retail business where customer experience rules, retailers are acknowledging the call for more effective personalization. In this regard, AI analytics is the right solution for stores that want to meet customer needs while enhancing their shopping experience.
AI analytics can generate insights based on demographic traits such as location, gender, and age, as well as customers’ interests and habits. With this consumer-related data, it is easier for retailers to identify their buyer personas and provide a curated merchandising experience. For example, stores can offer personalized discounts and adjust pricing strategies for different groups of customers. More than that, they can come up with personalized product recommendations based on the insights from a customer’s previous visits, thus making them feel even more welcome.
Measurement of the effectiveness of campaigns
Undeniably, traffic flow analysis is the right tool to help retailers measure and streamline marketing attribution. Stores can use comprehensive statistical analytics to determine the impact of promotions and seasonal changes on shopper traffic and adjust marketing strategies all year. Store managers can also make decisions on ad placements to produce better advertising effects on hit rates and average purchases.
Final takeaway
The benefits that video analytics powered by AI can bring to retailers are invaluable. It provides data-driven insights that help improve store performance in all areas and create a more enjoyable shopping experience for customers. Advanced analytics can be used for counting customer footfalls, predicting queuing patterns for faster checkouts, and creating heatmaps to better understand store performance and improve the efficiency of the staff. With smart video analytics, stores can optimize layouts, product placements, and staffing levels, make effective merchandise plans, and improve profitability. All this makes Traffic Flow Analysis a worthwhile investment for any retail store looking to optimize business processes, improve customer service, and boost revenue.
Product Adopted:Retail