Retail Video Analytics

Combines real-time analytics, interactive customized promotions and employee nudges to increase sales and improve operational efficiency

Retail video analytics solutions use artificial intelligence (AI) technology to understand video streams/feeds, offering brick-and-mortar retailers the potential for a similar level of shopper insights as those used by the most sophisticated e-commerce sites.

The more retailers can learn about their shoppers, the better they can serve them – leading to improved sales and profits.

The Retail Video Analytics Platform from SMART Embedded Computing combines real-time analytics, interactive customized promotions and employee nudges to increase sales and improve operational efficiency.

You can integrate with existing security cameras to cost-effectively enhance your customer experience and streamline operational costs.

The system encrypts all data and does not collect personally-identifiable information.

Understand Your Customer and Refine Your Product Offering

  1. Learn about anonymized customer patterns, including age, gender and time of visit so you can target customer experiences based on business intelligence, rather than speculating about customer demographics
  2. Get unique customer counts (including visitors who don’t make a purchase and aren’t captured by your point-of-sale system)
  3. Identify missed opportunities
  4. Identify trends in engagement with product displays to improve your understanding of the link between promotions, displays, and sales
  5. Implement conversion prompts and identify ways to attract customers with cross merchandising

Enhance Store Performance and Operations

By measuring visual data and comparing trends over time, retailers can enhance store performance and operations:

video analytics for retail - gas station pump monitoring
video analytics for retail - grocery check out

Optimize Your Store Layout

Understand how customers typically navigate through your store and how long they spend in certain locations to optimize product placement and in-store traffic flows to encourage sales. Seeing in-store traffic patterns and peaks, and what causes them, can help prevent friction in the path to purchase.

Streamline Operations

  • Track customer types across areas of interest in your stores
  • Capture dwell times and counts at areas of interest
  • Learn average and max line/queue lengths and times
Use AI-driven decision making to predict pain points such as queue lengths and send proactive nudges to employees or managers to prevent long lines from forming. You can also assess the effectiveness of self-checkout solutions.

SMART Video Solutions

SMART Embedded Computing is working with leading video analytics AI software providers, to create a set of scalable solutions to transform the way retailers use, store and access their visual data.

The SMART Edge Server SE1700, with integrated GPU AI inference engine and hosted video analytics software, can process up to 8 cameras using state-of-the-art data analysis software providing the ability to:

video analytics software
video analytics for retail

SMART EC Video Analytics Platform

The SMART EC Video Analytics Platform for retail applications provides a real-time dashboard with immediate access to information such as:

  • People counting
  • Dwell time
  • Queue depth
  • Unique visitor count
  • Count of family units
  • Customer pump-to-store conversion rate
  • Customer carwash conversion rate
  • Customer heatmaps in store or lot
  • Demographic identification
  • Parking lot demand/capacity
  • Car dwell time
  • Parking utilization
  • Gas pump availability
  • Traffic counting on nearby roads
  • Traffic exit time

Personal Information

The solution is designed to capture what is considered non-personally identifiable information. It collects pixel-level environmental information on selected objects, such as a car driving into a gas station or a person walking into a building.

Our AI platform aggregates and analyzes that environmental information against environmental information collected in previous encounters of similar class objects to recognize patterns. It then generates confidence interval data as to whether an object is recognized as the same class of object as in one or more previous encounters.

The confidence interval data cannot be used to distinguish, identify or trace an individual’s identity (no biometric records, etc.), and is only used to recognize, track, and help understand behaviors of classes objects.

video analytics for retail - customer monitoring

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