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Image Recognition

Product Setup Requirements

What information is required for SKU setup?

  • Minimum 5 Megapixel images of product packaging from all angles
  • For cylindrical products:
    • 3-4 images around the circumference
    • 1-2 images from the top
  • Complete packaging information including branding and color for visual representation

Planogram Management

How many planograms are typically needed?

A market usually requires only a small number of planograms. These are defined based on combinations of:

  • Market
  • Channel
  • Classification
  • Equipment type

For example, a single market might have different planograms for:

  • Highway-adjacent stores
  • City center locations

What information is needed for planogram setup?

Sample Planogram

To create a planogram, you need:

  • Complete product setup
  • Shelf specifications
  • Asset type details (if planogram is specific to certain cooler/freezer models)

Who can manage planograms?

Any user with planogram modification privileges can:

  • Create new planograms
  • Assign planograms to assets

Priorogram System

What is a priorogram?

Priorogram is Coolr's proprietary approach to handling "missed opportunities" and "order recommendations" for out-of-stock situations. This system was developed to address common challenges with traditional planograms:

  • Traditional planograms can be tedious to set up
  • Field implementation often deviates from defined planograms due to practical constraints

The priorogram model allows customers to:

  • Specify SKUs in order of priority
  • Focus on product availability rather than specific positioning
  • Receive recommendations based on priority list and current inventory

Stock Calculation Methods

How is stock calculated across different tiers?

Basic Tier/Navigator

  • Uses Share of Shelf (SoS) or Share of Visible Inventory (SOVI)
  • Common approach among traditional image-based monitoring solutions

Commander and Pioneer Tiers

  • Utilizes depth estimation for stock calculation
  • Stock levels are approximated in 25% increments (25%, 50%, 75%, 100%)
  • Camera positioning affects measurement:
    • Horizontal freezers: Top-mounted camera provides accurate stock level assessment
    • Vertical freezers: Front-mounted camera measures stock by product distance from shelf front

How accurate is stock calculation?

  • Approximately 95% accurate across 30,000+ units
  • Successfully used for order recommendations across various businesses
  • Proven track record of 30%+ sales uplift in first year when recommendations are followed

Can exact stock counts be obtained?

While Coolr's solution prioritizes cost-effectiveness and quick ROI with 95%+ accuracy, alternative solutions for exact counting include:

  • Grab 'n Go systems
  • Just Check Out
  • Weight sensors
  • RFID technology

Key Metrics and Calculations

What are the key performance metrics?

Stock (SOVI - Share of Visible Inventory)

  • Used to identify:
    • Replenishment schedules
    • Product sale patterns at SKU level

Planogram/Priorogram Compliance

  • Measures:
    • Product portfolio effectiveness
    • SKU-level placement efficiency
    • Out-of-stock situations
    • Product distribution

Purity

  • Measures percentage of customer's own products in an asset
  • Also known as Share of "own" inventory
  • Used for trade terms compliance monitoring

How are metrics calculated?

Standard calculations include:

Stock % = (Total Visible Products - Foreign Products) / Total Available Positions
Purity = (Total Visible Products - Foreign Products) / Total Visible Products
Planogram Compliance = (Total Planogram Facings - Compliant Facings) / Total Available Positions

Note: Total Available Positions is determined by either:

  • Defined planogram occupied spaces
  • Or (Shelves × Columns per shelf) as specified in Asset Type configuration

Image Recognition Process

What is Coolr's image recognition workflow?

  1. Image Processing

    • Stitching of fragmented images
    • Human feature obfuscation
    • Area of interest cropping
    • Perspective correction (fish-eye reduction)
    • Quality adjustment (brightness, contrast)
  2. Detection Steps

    • Shelf/basket identification
    • Product shape detection
    • Empty space recognition
    • SKU identification
    • Stacking analysis
    • Stock level assessment

What is the accuracy level and how is it maintained?

  • 95%+ accuracy in automated recognition
  • Verification process:
    • Random 20% of images undergo human review
    • Minimum 10 SKUs per test set
    • Regular benchmarking across 1,000 images
    • Diverse location/asset sampling

Why is continuous training necessary?

Image recognition, like human learning, requires ongoing training to handle:

  • Seasonal packaging changes
  • New product variants
  • Similar product differentiation
  • Image quality variations
  • Product placement variations

The system must continuously learn to:

  • Distinguish between similar products
  • Recognize new packaging designs
  • Adapt to different viewing angles
  • Handle varying light conditions
  • Process multiple product variations

Business Applications

What are the key use cases for this data?

  • Inventory optimization
    • Improved on-shelf availability
    • Portfolio optimization
  • Merchandising management
    • Standards monitoring and enforcement
    • Cost and carbon footprint reduction
  • Compliance
    • Trade terms enforcement
    • Unbiased continuous monitoring
  • Business optimization
    • Missed opportunity identification
    • Delivery routing optimization