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Design & Creative
FashionAI by Ricoh
FashionAI by Ricoh logo
Design & Creative

FashionAI by Ricoh

FashionAI by Ricoh is an artificial intelligence solution specifically designed for the fashion retail industry, leveraging computer vision and machine learning technologies to transform traditional retail operations. The platform analyzes visual data from physical stores to provide actionable insights about customer behavior, product performance, and store operations. It helps retailers understand how customers interact with merchandise, which items attract the most attention, and how display arrangements affect sales. The system uses cameras and sensors to track customer movements, dwell times, and engagement patterns without personally identifying individuals. Retailers can use these insights to optimize store layouts, improve product placement, enhance visual merchandising, and increase conversion rates. The technology addresses key challenges in physical retail by providing data-driven decision-making tools that were previously only available in e-commerce environments. FashionAI represents Ricoh's move into smart retail solutions that bridge the gap between physical and digital shopping experiences.

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📊 At a Glance

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Design & Creative
Fashion & Apparel

Key Features

Customer Behavior Analytics

Tracks and analyzes how customers move through retail spaces, measuring dwell times, navigation patterns, and interaction frequencies with different store areas. The system creates heat maps and journey visualizations showing where customers spend time and what captures their attention.

Product Engagement Tracking

Monitors which products customers touch, examine, or try on, providing data on merchandise interaction rates independent of actual purchases. The system correlates physical engagement with sales data to identify high-interest items.

Visual Merchandising Optimization

Analyzes the effectiveness of display arrangements, window presentations, and product placements by measuring customer responses to different visual merchandising approaches. Provides A/B testing capabilities for display configurations.

Conversion Funnel Analysis

Tracks the complete customer journey from entry to purchase, identifying drop-off points and bottlenecks in the physical shopping experience. Measures how many engaged browsers become fitting room users and ultimately buyers.

Demographic Pattern Recognition

Uses anonymized visual data to identify broad demographic patterns in store traffic and engagement, helping retailers understand which customer segments engage with different product categories or store areas.

Performance Benchmarking

Compares performance metrics across different time periods, store locations, or product categories to identify trends, seasonal patterns, and improvement opportunities. Provides standardized KPIs for retail performance evaluation.

Pricing

Enterprise Custom Solution

contact sales for custom quotation
  • ✓Complete hardware installation including cameras and sensors
  • ✓AI software platform with dashboard access
  • ✓Initial configuration and store mapping services
  • ✓Basic analytics and reporting capabilities
  • ✓Standard technical support during business hours
  • ✓Data storage and processing infrastructure
  • ✓Regular software updates and maintenance

Multi-Store Enterprise Package

contact sales for volume-based pricing
  • ✓All features from Enterprise Custom Solution
  • ✓Centralized management dashboard for multiple locations
  • ✓Cross-store comparison analytics
  • ✓Customized reporting templates for different store formats
  • ✓Priority technical support with faster response times
  • ✓Advanced predictive analytics features
  • ✓Integration support with existing retail systems
  • ✓Dedicated account management

Use Cases

1

Store Layout Optimization

Fashion retailers use FashionAI to analyze how customers naturally move through their stores and which areas receive the most traffic. By understanding traffic patterns and dwell times in different zones, retailers can strategically place high-margin items in high-traffic areas and ensure complementary products are positioned along natural customer pathways. This optimization increases product visibility and cross-selling opportunities while reducing dead zones where merchandise goes unnoticed.

2

Visual Merchandising Effectiveness

Visual merchandisers employ FashionAI to test different display configurations and window presentations. The system measures customer engagement with various arrangements, allowing teams to quantitatively compare which displays attract more attention and longer viewing times. This data-driven approach replaces guesswork with measurable outcomes, enabling continuous improvement of in-store presentations and seasonal campaign effectiveness.

3

Product Performance Analysis

Buying and merchandising teams use FashionAI to understand which products generate customer interest regardless of final sales. By tracking which items customers touch, examine, or try on, retailers can identify products with high engagement that might need better positioning or pricing adjustments. This insight helps optimize inventory decisions, markdown strategies, and future buying choices based on actual customer behavior rather than just sales data.

4

Staff Deployment Optimization

Store managers utilize FashionAI data to optimize staff scheduling and positioning based on customer traffic patterns. By understanding peak engagement times in different store areas, managers can ensure adequate coverage in fitting rooms during busy periods and position knowledgeable staff in high-consideration product zones. This improves customer service efficiency and increases conversion rates through timely assistance.

5

Multi-Store Performance Comparison

Retail chains with multiple locations use FashionAI to compare performance across different stores, identifying best practices and improvement opportunities. The system highlights why certain locations outperform others in terms of customer engagement and conversion, enabling successful strategies to be replicated across the chain. This centralized insight helps standardize operations while allowing for local optimization based on specific store characteristics.

How to Use

  1. Step 1: Contact Ricoh sales representatives through their website to schedule a consultation and discuss specific retail needs and store requirements.
  2. Step 2: Undergo a store assessment where Ricoh technicians evaluate the physical space, lighting conditions, and camera placement opportunities for optimal data collection.
  3. Step 3: Install Ricoh's specialized hardware including discreet cameras and sensors throughout the retail space, ensuring proper coverage of key areas like entrances, displays, and fitting rooms.
  4. Step 4: Configure the AI system parameters through Ricoh's dashboard interface, setting up zones of interest, defining product categories, and establishing baseline metrics for comparison.
  5. Step 5: Begin data collection phase where the system captures and analyzes customer movement patterns, dwell times, and interaction behaviors with merchandise over an initial period.
  6. Step 6: Access the analytics dashboard to view heat maps, customer journey visualizations, and performance reports showing which products and displays attract the most attention.
  7. Step 7: Implement changes based on insights - rearrange displays, adjust lighting, modify product placements, or change store layouts to optimize customer engagement.
  8. Step 8: Establish ongoing monitoring with regular reporting cycles, comparing performance metrics before and after changes to measure improvement and ROI.
  9. Step 9: Integrate FashionAI data with existing retail systems like inventory management, CRM, or sales tracking platforms for comprehensive business intelligence.
  10. Step 10: Schedule periodic reviews with Ricoh specialists to refine AI models, adjust camera placements, and explore additional features as retail needs evolve.

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