For accelerating retail growth with people counting and in-store customer analytics, walk-in customers are an essential ingredient for a business. For example, shopping malls, high-value retail stores, events, exhibitions, fashion outlets, etc rely on walk-in customers for doing its business. Customers walk-in to buy something from you. If they leave without buying from you, it means your store is inefficiently designed. Maybe the product they are looking for is not easily discoverable. Maybe the product positioning is confusing for customers. Maybe you are not showing the right offers to the customers. The key to operating a successful brick and mortar store is to have sound knowledge and understanding of customer patterns, behavior, preferences, and needs. To stay profitable and competitive in a market, brick-and-mortar retail stores should optimize their standard operating procedures (SOP) to build excellent customer experience and high customer satisfaction. This enables retailers to evaluate sales potential and establish marketing strategies for maximizing profit.
Merely measuring the in-store customer traffic of your store provides you with valuable data you need to develop a strategy to improve sales and deliver better customer experience. Gathering this intel can be a manual process, or you can completely automate it. Unfortunately, the aforesaid inefficiencies cannot be fixed by traditional retail strategies. You have to leverage technology to automate in-store crowd analytics. The in-store crowd traffic analysis of your physical store can enhance the experience of your customers and increase your sales, in the result. It helps you understand the behavior of your customers, preferences and optimize your store performance – giving you a competitive edge.
In this article, we’ll see how to automate the customer traffic analysis and track visitors to ensure that your store runs efficiently – and that your customers gain a positive experience.
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A CCTV camera, located at the entrance gate, works as a highly advanced sensor that collects and transmits data to real-time video analytics software such as Emotyx for customer analysis. The system automatically performs real-time two-way counting of people passing underneath the camera, keeping a record of people entering and leaving the store. The data generated can be used to generate several insights as follows;
Working in tandem with people counting and occupancy estimation, Emotyx provides data and information that can give you real-time indications on the number of people in your store, or a specific section or area, and calculate the average visit time. The insights generated can educate you about your customers’ preferences, hot zones in your store, customer drop-off trends, etc. Strategizing product placements on top of these insights can ensure increased sales and customer experience for your business.
Emotyx’s retail analytics suite offers a comprehensive variety of options for data analysis. Typical data analysis is beneficial to monitor the actual physical space within your store and collect data using a network of surveillance cameras. This is an all-in-one solution that gives you the count people, estimates occupancy, and monitor the people in the queue.
A queue monitoring system is a valuable asset when you are aiming to improve customer experience, as long lines and extended waiting times may frustrate shoppers, damage their in-store experience and prompt them to leave their purchase – effectively hurting your store’s sales and profitability. Emotyx’s queue monitoring system allows you to decide queue thresholds that will prompt the opening of a new cash counter once the number of customers waiting in the queue exceeds your pre-defined limit.
Monitoring and analyzing data on customer traffic flow, patterns and behavior, average visit time, staff capacity and allocation, and queue length and time will provide you with valuable knowledge and actionable insight that will help you make the right decisions for improving your store’s performance and sales.
The data analysis of your physical store can enhance the experience of your customers and increase your sales in the result. It helps you understand the behavior of your customers, preferences and optimize your store performance – giving you a competitive edge.
Once you can establish the baseline for footfall patterns in your retail store, you can move quickly on measuring the marketing attribution using the following KPI’s:
The in-store analytics can be used to identify the customer as soon as they enter the store. This event data helps you create an automated workflow and personalize the in-store experience. Using this data, retailers can give customized coupons or discounts or provide a service gesture to the customers.
Automating customer traffic analysis enables you to get a better understanding of customer needs. Customer needs can be met much easily once there is an understanding of in-store footfall patterns and the other aspects driving in-store customer behavior. Customer data can also help in the better product replacement and improve the store design to guide footfall to specific areas or products. Nowadays, AI-driven retailers are using every opportunity to use data to analyze customer-changing behavior and purchasing habits across multiple channels to arrange the right product type, promotions, and personalized communications.
Retailers can use machine learning to detect even smaller changes in patterns of customer behavior. They are using AI models to predict the spend potential from existing customers while they are shopping in-store, online, or via mobile. By using the data or customer purchase history, they can provide the most effective offers. Also, this type of model helps retailers to influence their buying decisions and figure out which customers are at risk of churn.
Due to the explosion of choice and accessibility created by digitization, product assortment has become a matter of survival for retailers. Retailers are now using automated models to predict what products to stock in the inventory and in which store, what items should be replaced with new products which are in demand, and what type of items are being returned by which customers.
Automated machine learning models can help retailers improve their ability to predict consumer demand for goods. This allows retailers to manage their inventory to meet the requirements of consumers.
Retailers are now using automated and advanced models to determine where to open new stores based upon the revenue and how to forecast the correct amount of staffing inside the store to identify opportunities to decrease the costs and, at the same time, increase the operational efficiencies to ensure a seamless customer experience. All we can say is that automating customer traffic analysis helps the store operators dig a little deeper into their customers’ minds and behavior. Additionally, retailers can also get a deeper understanding of a specific customer shopping at a particular location chooses from a variety of products, increasing sales and revenue.
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