DeepLounge is an off-line customer analytics that provides marketing insights with AI camera technology.
Negotiable Min Order Quantity Unit
- Required Quantity
- Place of Origin
- South Korea
- Brand name
- Payment Terms
- Production method
- Shipping / Lead Time
- Negotiable / Negotiable
- Computer Software
|Keyword||ai kiosk , ai camera , offline store analytics , product recommendation ai||Unit Size||-|
|Brand name||DeepLounge||Unit Weigh||-|
|Supply type||OBM||HS code||-|
Our store analysis AI system, installed with in-store cameras, identifies and records visitor information like gender, age, race, and group attributes. It also tracks visitor behaviors, such as the areas they spend more time in and the products that catch their interest. This data is then analyzed, allowing businesses to gauge the reach of their target customers, understand shifting customer demographics, and recognize which products are popular and which are not. It also provides insights into future trends and product recommendations. This data is readily used on-site through AI kiosks, advertising solutions, and automated services to enhance the overall shopping experience for customers.
[DeepLounge AI platform]
DeepLounge is an off-line customer analytics with AI camera technology. With our solution, the manager of stores can understand their customers more deeply with well-designed analytics and reports through their client’s behavior collected by AI camera. It provides heat-map, snaky-chart, funnel data, and many other visualized analytics to obtain useful marketing insights. Because we use AI camera technology, every data can separated with age or gender so they can determine each product’s marketing performance that narrowed down specific target group. With our well-trained customer data, We can also suggest personalized products to each visitors based on their behavior in the store and with their past purchase patterns if they agreed with privacy terms.
Store analysis solution is a data collection solution for customer profiles and behavior data that occur in offline stores. By obtaining insights necessary for store operations, it can be applied to improve revenue. Using video analysis technology, objective data based on actual customer behavior can be collected, and you can easily check it through store-specific dashboards.
- Inflow and Utilization Conversion Rate Analysis (External footfall - Visit rate - Purchase rate)
- Customer Profile Analysis (Gender, Age, Ethnicity-based Customer Trends, etc.)
- Customer Behavior Analysis within the Store (Heatmap, Flow Analytics, etc.)
- Customer Analysis by Shelf (Visitors per Shelf, Dwell Time Analysis, etc)
- Multi-store Periodic Comparative Analysis
- Store-to-Store Comparative Analysis (Multi-store Management)
- Promotion Performance Analysis
[DeepLounge AI Kiosk]
The AI product recommendation kiosk consists of two components: wine recommendation and style recommendation kiosks. Through detailed consultation, we provide solutions tailored to the product database you handle, ensuring optimal performance and customization.
- "AI Stylist" recommends clothes that suit the customer's taste among new products based on the products that shoppers are interested in, the clothes they are currently wearing, and their past purchase records. It not only guides the location of products in the store, but also provides a virtual fitting function.
- “AI Sommelier”, the AI wine recommendation kiosk that trained on a vast database of global wine information and reviews, engages first-time customers at offline stores by generating context-specific questions like a sommelier, thereby identifying their precise preferences. For existing customers, it leverages their purchase history to recommend either their preferred styles or adventurous choices.
|Price (FOB)||Negotiable||transportation||Negotiation Other|
|Payment Options||T/T||Shipping time||Negotiable|
- SIN DONG HWA
- 1318,16,Maeheon-ro, Seocho-gu, Seoul, Korea
- Product Category
- Computer Software
- Year Established
- Company introduction
- Main Markets
- Main Product
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