The benefits of Retail Customer Analytics
Artificial Intelligence based computer vision and retail analytics systems can create value for customers and increase loyalty by providing insights and behavior, thereby helping retailers remain competitive. In addition, the ability of computer vision analysis technology to process large amounts of data in real time can help retailers improve customer experience in various scenarios.
Increase in sales
Increase in sales growth
Increase in ROI
Increase in Profits
According to McKinsey survey
Studying Real-time Customer Behavior
Retailers really need effective product placement, but what if they could leverage it to figure out what types of placements are converting well? This is one of the requirements that can be met with video analytics. Obviously, computer vision video analytics help track consumer behavior around special offer locations. Has the consumer compared a particular product with other brands? Whether they were looking at an object or spending a lot of time on it? What if they chose a product? If they buy a lot of products
Map the Customer's Journey with Engagements & Conversions.
Computer vision video analytics for retail shop optimization is most commonly used to track consumer visits and travels. As a rule of thumb, it's referred to when using video analytics, you may find out how many people are entering or departing the business, as well as how many people are in the store at any time. As a bonus, it records the customer's path through the store, which may be used by businesses to gather information on the busiest places over a specific time.
How Computer Vision Retail Analytics Enhances Customer Experience
OPTIMIZE STORE DESIGN
Find ways to improve consumer contact with employees or products through location design. Visitor analytics and heat maps may be used to measure the efficacy of window displays and to determine where consumers are more likely to visit.
Staffing plans may be optimised depending on the times when your stores are busiest. Save money by removing damaged fixtures, sales floor trash, and misplaced end caps from shop pictures that are taken every few seconds.
Find out how people interact with displays and kiosks such as shelves and display units. Review instances of empty shelves or the speed at which items are placed on display. Use department-level traffic counts and customer data to A/B test each display.
Learn how online marketing campaigns translate into in-store customer traffic and attribute offline conversions. Measure product engagement to identify products in demand and track campaign performance by hour, day, and time of week.