Self-checkout solutions have changed the retail industry experience by providing convenience, speed, and minimal operational expenses. However, these systems also have some challenges, especially related to theft, fraud, and, human error. This article is about video analytics to control self checkout losses.
AI-based video analytics solutions have come-up as a promising layer that overcomes self-checkout losses.
This guide helps in understanding the characteristics of video analytics in the retail sector, how it can help reduce self-checkout losses, and increase business efficiency.
What Are Losses at Self Checkout Machines?
As we know technology is evolving, and so is the retail sector as well. Nowadays, you get to see unattended self-checkout kiosks at large supermarkets that operate via customers.
This behaviour posses losses like products taken out from the store intentionally or unintentionally through technical mistakes or human errors. Such incidents not only drops the store’s profit margins, but also create a false image in people’s minds that the store is lenient towards theft.
Such losses can be almost completely controlled by stores and supermarkets that invest in AI powered video analytics solutions.
Reasons for Losses at Self Checkout Kiosks
Following are some of the reasons why losses take place at self-checkout kiosks.
Shoplifting
Without an attendant at a self-checkout kiosk, customers can intentionally skip scanning items or scan cheaper barcodes, resulting in shoplifting.
Scanning Errors
Due to human error, customers may scan the wrong barcode or forget to scan an item.
Employee Theft
Employees could manipulate the self-checkout system, either directly or by allowing friends or family to exploit it.
Barcode Switching
Dishonest shoppers might swap price tags with those of lower-value items.
System Glitches
Technical issues can result in missed scans or incorrect transactions.
Impact of Self-Checkout Losses on Retailers
Following are some of the reasons why losses can impact a retail business.
Financial Loss
Financial loss is the worst of all. Theft and errors lead to direct revenue loss.
Operational Inefficiencies
It is a waste of time and resources investigating theft and fixing mistakes. It disturbs the normal store operations.
Customer Experience Problems
False theft accusations can alienate genuine customers and harm the store’s reputation.
Compliance Risks
Failing to combat theft effectively can lead to legal, compliance, and customer privacy issues.
Inventory Mismatch
It arises due to scanning errors, unscanned items, weight verification issues & discrepancies between the system’s records.
How Video Analytics Controls Self Checkout Losses?
It is important to detect self checkout losses to prevent loss, work inefficiencies, unnecessary stress, inventory mismatch, etc.
Theft Detection
A video analytics solution can easily detect incidents like barcode switching or a skipping scan. Basket walk-aways can easily be detected & prevented by alert generation to the store maintenance team.
Reducing Scanning Errors
Ensures items placed in shopping bags are scanned correctly, minimizing errors.
Employee Oversight
Monitors employee interactions with the self-checkout system to detect insider theft.
Illegal Activity Detection
The awareness of video surveillance reduces theft by both, customers and employees.
Types of Video Analytics Used in Retail Sector
Motion Detection
Identification of unusual customer behavior, such as lingering by the checkout or scanning items quickly.
Object Tracking
Tracks items from shelves to checkout ensuring they are properly paid for.
Facial Recognition
Easy detection of habitual offenders and known shoplifters to completely prevent recurring incidents.
Activity Monitoring
Complete tracking – from scanning items to making a payment & end-to-end customer life cycle track in an autonomous manner.
Dwell Time Monitoring
Tracks customer’s time spent in store areas, optimizing layout & product placement based on section-wise dwell time analysis.
Customer Behaviour Analysis
Advanced algorithms identify suspicious behavior, such as hiding items or avoiding payment kiosks.
Customer Segment Analytics
Key customer information metrics like age group, gender, peak hours can help to improve customer acquisation, eventually helping in increase of sales.
Benefits for Retailers Using Video Analytics
Following are the benefits of Retail Video Analytics.
Loss Prevention: Automated theft detection reduces inventory losses.
Enhanced Security: Comprehensive monitoring ensures overall protection across the store.
Data-Driven Insights: Provides detailed reports on customer behavior and transaction patterns.
Operational Efficiency: Reduces the time needed for theft investigations by providing specific video evidence
Better Customer Experience: Ensures a smoother customer shopping experience.
Tracks customer’s time spent in store areas, optimizing layout & product placement based on section-wise dwell time analysis.
Best Practices for Retailers - Video Analytics to Control Self Checkout Losses
Choose the Right System
It is important to choose the right video analytics solution for effective implementation. We at Intozi help you with highly accurate and advanced video analytics platform – Ikshana.
Ikshana leverages cutting edge AI technologies like computer vision, LLM and machine learning to deliver best in class solutions for retail sector.
Data Security
To minimize legal and compliance risks it is mandatory to encrypt sensitive customer data. The technology used in Ikshana does all of that automatically for you. So, you do not need to worry when you implemented our system.
Staff Training & Ease of Use
Ikshana has the easiest to use interactive user interface to make it user friendly. User can easily subscribe to any application as per their requirement.
Be it live monitoring, incident detection, report generation, or any custom use case application you require are few clicks away on Ikshana platforn.
Regular Monitoring
It is also important to regularly monitor store reports and incidents. This not only improves business operations, but also reduces vulnerabilities that can occur at self-checkout kisosks.