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Network Modernization – A Practical Guide for Multi-Site Enterprises

Bud Homeyer — Fri, 23 Feb 2024 05:59:31 +0000

Retail

Bud Homeyer

Chief Operations Officer

Managed Network & Voice PCI Compliance Restaurants Retail

Network Modernization – A Practical Guide for Multi-Site Enterprises

What’s Driving Network Modernization in Multi-Location Enterprises?

Multi-site enterprises and consumer-facing brands such as retail and restaurant chains have bounced back from the shock of COVID-19. In hindsight, the winners are those retail and restaurant brands that could adapt to dramatic shifts in customer preferences, high frontline staff attrition rates, and a slew of unexpected physical security and supply-side challenges.

The only way businesses can deal with future uncertainty is by investing in human capital and technology infrastructure that can support rapid changes in operating models.

Here are the key reasons why network connectivity and security hence take center stage in any program that aims to build an agile multi-location business.

Industry Disruption

Why Network Modernization is Needed

Challenges in Hiring & High Employee Turnover

According to the National Restaurant Association, the restaurant industry is currently operating with a labor shortfall of 540,000 employees. The US Bureau of Labor Statistics reported the annual average total separation rate for the retail industry was 5% compared to 3.9% across all industries in May 2023.

Boost Employee Productivity

Retail and restaurant operators have to keep a sharp focus on boosting employee productivity at every touchpoint and automating mundane activities. A scalable network backbone is critical to deploy time-saving productivity and collaboration applications and improve application performance.

Changing Customer Expectations

According to Brandwatch, in 2023, consumers are placing a lot more emphasis on convenience. There is a 12% increase in social media chatter about convenience and shopping experience compared to the previous period.

Create Superior Customer Experiences

Restaurant brands have to reimagine the dining space with a focus on enhancing the speed of service, offering greater convenience, and a wide range of digital ordering services. Similarly, retailers have to invest in delivering superior in-store experiences and last-mile fulfillment across channels. None of these are possible without the underlying network and connectivity infrastructure.

Rising Food Prices and Inventory Glut

The US Department of Agriculture reported that the restaurant consumer price index in July 2023 was 7.1% higher when compared to the same period in 2022. This has impacted both retailers and restaurants. Rising prices also mean unsold inventory. Markdowns and discounts help move them but leave a serious dent in the bottom line.

Invest in Streamlining Operations

Retailers and restaurant brands want to streamline every aspect of their operations to cut costs. Economies of scale are needed in the entire supply chain and the underlying tech infrastructure to take advantage of standardized processes. Well-designed network backbones can optimize operating costs and support advanced analytics solutions to streamline the business.

Violence & Theft

Business locations, especially those in high-risk areas, face significant threats in the form of violent customers, robbery, and gun violence. Internal theft and cash handling risks have historically been a concern for retail and restaurant operators

Invest in Intelligent Security Solutions

Retailers and restaurant chains have to invest in intelligent security systems that provide advanced warnings and are capable of zeroing in on anomalies across millions of POS transactions. This requires investment in a wide range of cloud-based, AI-enabled sensors and data analytics. None of these applications can be deployed with outdated network infrastructure.

As multi-site businesses attempt transformative initiatives, they are faced with practical issues that go back to network design, labor-intensive network operations, management protocols, hardware procured from diverse vendors with differing capabilities, and network security vulnerabilities across the infrastructure.

Employees are becoming more mobile, accessing the network from various locations and endpoints beyond corporate IT control. They are also connecting to public clouds for essential business applications like Office 365.
IoT devices, widely distributed in remote and unsupervised locations outnumber human-controlled endpoints opening the door for new security threats.
Cloud service providers have expanded their presence across numerous branches, which connect directly to the cloud, bypassing corporate data centers.
With ever-tightening Payment Card Industry (PCI) requirements and data privacy regulations, restaurants and retailers face tremendous risk when migrating data from on-premise POS to cloud-based solutions that offer easier integration with inventory management, online ordering platforms, and the extended supply chain.
Support for bandwidth-intensive applications such as video management systems and devices with edge computing capabilities that deliver critical real-time data on store operations (such as security cameras) can be challenging when the last-mile connectivity is not properly handled.

The goal of any network modernization program should not only take into consideration the evolving technology solutions that drive transformation but also aim to create a resilient technology operating system that can change rapidly without creating bottlenecks in the future.

Challenges in Managing Legacy Network Infrastructure

There aren’t clear-cut answers. Most likely, retail and restaurant chains always have some part of the network designed decades ago and other parts that were added recently.

Outlets with legacy network components can still run online ordering operations, support in-store applications, and manage their supply chains. The real problem is in administering the network and the penalty it imposes through inefficiencies, downtime, latency, and security vulnerabilities. The limits of what a network infrastructure can support efficiently determine whether a network upgrade is needed or not (Refer to Exhibit 1).

Flat Network Architecture

Restaurant chains with a flat network architecture face significant challenges as the business grows and new requirements emerge.

1. Limited Segmentation

Without dividing the network into security zones, all devices and systems within the organization, such as point-of-sale (POS) systems, employee workstations, and guest networks, are interconnected. This lack of segmentation increases the attack surface, making it easier for an attacker to move laterally across the network. For example, a malware infection in a POS system could spread to the corporate network, compromising sensitive data and systems.

2. Increased PCI Audit Scope

In a flat network, the scope of the PCI audit expands, encompassing the entire network infrastructure, rather than just specific segments. This can increase the complexity and cost of compliance efforts.

3. Weakened Access Control

Without segmentation, it becomes challenging to implement role-based access controls, network segmentation based on user roles, or least privilege access principles. This can result in unauthorized access to critical resources, data leakage, and a higher risk of insider threats.

4. Performance and Scalability Bottlenecks

A flat architecture can hinder performance and scalability. Broadcast and multicast traffic, typically limited to individual segments, can quickly propagate throughout the entire network, leading to congestion, reduced bandwidth availability, and degraded performance.

5. Difficult Troubleshooting

Lack of segmentation makes it challenging to pinpoint the root cause of network problems. This results in increased downtime, extended troubleshooting efforts, and potential business disruptions.

Manual Failover

Manual failover relies on the availability of personnel to identify and respond to network failures promptly. There are serious risks of not upgrading to automated failover systems.

1. Limited Scalability

As the network grows in complexity, manually managing failover becomes increasingly challenging. The need for human intervention in every failover event can limit the network’s scalability and agility.

2. Increased Operational Complexity

Implementing manual failover requires detailed documentation, well-defined processes, and trained personnel who understand the failover procedures. It also increases the reliance on specific individuals or a limited group of staff members with the necessary expertise.

3. Prone to Human Error

There is an increased risk of human error during the transition, such as misconfiguration or oversight, which can lead to service disruptions or unintended consequences. Human error becomes more likely in high-pressure situations, potentially impacting the network’s availability and stability.

Centralized Network Security

Some retail and restaurant chains continue to rely on the security infrastructure at the data centers to do the heavy lifting. This approach can stifle application performance and increase network administrative complexities.

1. High Latency

Routing all traffic through the data center for security scans can increase latency and reduce the performance of internet-dependent applications, affecting productivity and user experience.

2. Increased Network Complexity

Implementing a centralized traffic backhauling architecture requires complex network configurations, including routing, load balancing, and secure tunnels to redirect traffic to the data center for security scans. This complexity can make network management and troubleshooting more challenging.

3. Limited Local Response Capability

Backhauling all traffic for security scans to a central data center can limit the ability to respond quickly to local network security incidents. Any network threats or anomalies that require immediate attention or localized mitigation experience delays due to the traffic redirection and the need to wait for security scans performed in the data center.

Legacy Routers, Modems, and Firewalls

Basic routers, modems, and consumer-grade firewalls lack advanced security functionalities, such as deep packet inspection, intrusion prevention systems (IPS), or advanced threat protection. This leaves the network vulnerable to sophisticated attacks and exploits targeting higher layers of the network stack.

1. Insufficient Traffic Visibility

IT teams may struggle to identify and address anomalous or malicious traffic patterns, making it harder to detect and respond to security incidents promptly.

2. Limited Scalability

As the network expands with additional branches, devices, and users, legacy routers, modems, and firewalls may struggle to handle the increased traffic volume and advanced security requirements. This can lead to performance issues and network bottlenecks.

3. Limited Support and Vendor Updates

Basic routers or consumer-grade firewalls often receive limited vendor support. This can result in outdated firmware, unpatched vulnerabilities, and a higher risk of security incidents due to the lack of ongoing security updates and patches.

Unmanaged Switches and Network Installation

Unmanaged switches offer limited or no visibility into network traffic and lack advanced monitoring features. Using them often results in ad hoc cable installations that hinder maintenance, troubleshooting, and overall network management efforts.

1. High Failure Rates & Poor Manageability

Unmanaged switches typically have a lower build quality that can lead to higher failure rates, increased downtime, and the need for frequent replacements.

2. Limited Scalability and Security

They lack features such as VLAN support, access control lists (ACLs), or traffic segmentation, which are essential for implementing network security policies and isolating different segments of the network.

3. Cable Loops and Performance Issues

Without proper cable management and oversight, unmanaged switches can contribute to cable loops leading to network broadcast storms, increased network congestion, and degraded performance.

Legacy WiFi Technologies

Businesses relying on legacy WiFi technologies are saddled with lower speeds, and limited bandwidth compared to newer standards like 802.11ac or 802.11ax (Wi-Fi 6 and Wi-Fi 6E). They may not offer the same level of coverage as newer standards resulting in dead zones or areas with weak signals within the premises.

1. Lack of Support for Critical Applications

Modern restaurant and retail chains often rely on various advanced applications and technologies like mobile point-of-sale (mPOS) systems, inventory management systems, order management systems, kitchen automation systems, and IoT devices. Legacy WiFi technologies may not offer the necessary capabilities to support these applications efficiently, limiting the potential for digital transformation and innovation.

2. Inability to Support High Device Density

As the number of devices connecting to WiFi networks continues to rise, legacy WiFi technologies may struggle to handle the increased device density as they operate on crowded and congested frequency bands, such as 2.4 GHz. This can lead to interference from other devices using the same frequency, resulting in degraded performance and unreliable connections.

3. Security Vulnerabilities

Legacy WiFi technologies may lack the advanced security features available in newer standards. This leaves the network more susceptible to unauthorized access, data breaches, and other security threats.

How Business Needs Can Be Mapped to Network Modernization

According to a 2023 retail industry CIO survey by Gartner, 35% of retailers surveyed cited “growth” as their priority, 27% said they will focus on customer experience, and 20% of the retailers surveyed are doubling down on technology modernization. In the restaurant industry, customer convenience and labor shortage were the key drivers for modernization. According to a survey of 300 restaurant operators by SpotOn, 75% of all restaurants surveyed planned to invest in technology modernization in 2023 to combat key labor shortages and offer better customer experience. These business imperatives are completely reliant on network modernization as highlighted below (Refer to Exhibit 2).

Business Drivers

Network Improvement Needed

Support for increasing transaction volumes, new store openings, and geographic expansion.

Ability to launch new locations using network templates and automation.

Minimize network downtime to ensure uninterrupted POS transactions and customer service.

Redundancy and fault tolerance mechanisms to mitigate the impact of hardware failures or network outages.

Strengthen network security to protect sensitive customer data and payment transactions.

Compliance with industry regulations, such as Payment Card Industry Data Security Standard (PCI DSS).

Enable smooth integration of online and offline channels to support unified commerce.

Connect in-store systems with ordering platforms, inventory management, and customer relationship management (CRM) systems. Support the integration of IoT devices for inventory tracking and layout analytics.

Improve visibility and connectivity across the supply chain for inventory management, logistics, and order fulfillment.

Build integration with vendors, suppliers, logistics, and delivery partners for real-time data exchange and coordination.

Elevate customer experience at the restaurant and the store.

Deploy reliable and fast Wi-Fi for customers and enable mobile device usage. Support dynamic or personalized menu boards, and enhance drive-thru and checkout experiences.

Streamline network operations, reduce maintenance costs, and optimize network resource utilization.

Monitor network performance, security, and compliance from a single dashboard.

Network Transformation Case Studies

The below case studies for a hypothetical multi-site business highlight two different approaches to upgrading the network based on business requirements.

Expand Drive-Thru and Phone Orders for a QSR

FeastOn was operating in a highly competitive QSR segment with a growing digital footprint. The company wanted to deploy a cloud-based POS integrated with an online ordering system, improve its ability to handle phone orders, and expand drive-thru services.

Network Requirements

FeastOn IT team identified the need for a high-availability design with 4-hour hardware replacement, WAN failover, redundant 48 port POE switches, 1 access point (guest and company use), VoIP with 4-6 corded phones, loud ringer, caller-ID integration with POS, integration of phones with text messages for order management. They required the ability to deliver drop-free calls during a WAN failure and to keep online orders (web and POS integrators, 60-70% of all orders) working while running on their backup connection. The current POS integration required a single public IP address that could be used over any circuit at each location for this to work.

Solution

FeastOn implemented two different network solutions based on traffic volumes and the growth potential of the location. One set of locations had a 4-hour hardware replacement SLA and the other set of locations came with a high-availability design that did not require immediate hardware replacement. The solution also included a cloud gateway SD-WAN solution that ensured phone calls did not drop during network failure and orders were able to flow seamlessly via the POS integrator that switched to the backup circuit. There were extra redundancy factors added to protect against any cloud gateway failures. This solution provided FeastOn with both WAN and hardware resiliency and met all application and phone failover requirements. This setup allowed them to continue to use some older POS setups while they transitioned to the cloud-based POS in phases.

Eliminate Downtime and Improve Network Security for a Retail Chain

QualityM realized that store operations were always hobbled by patchy network infrastructure nationwide. Network downtime was commonplace, network security management was a headache, and the legacy POTS phone system kept customers unhappy. PCI compliance kept them on their toes because of poor network design.

Network Requirements

QualityM hired a managed network services vendor to come up with a requirement for WAN redundancy, next-day hardware replacement, secure POS traffic, managed next-gen firewall, 24 port switch, two access points (guest and company use), and three cordless phones with an auto attendant that rings a group of phones to place orders.

Solution

The managed services vendor implemented a broadband circuit with automatic failover to an LTE backup. Every store had a single next-gen firewall, a 24 port switch (4 port POE injector used for the APs to keep cost down), and three cordless phones with auto attendant setup. The POS firewall sat behind the next-gen firewall in its security zone. This allowed the POS to use the WAN redundancy setup and still be secure from other network devices, which were separated into four other networks. This network design provided QualityM with a WAN-resilient and secure network.

Thumb Rules for Network Modernization

Irrespective of business needs or management expectations, there are several key factors and objectives retailers need to consider when evaluating network modernization solutions including:

Design the network to ensure high availability and minimize downtime.
Implement redundant network components such as routers, switches, and firewalls to prevent single points of failure.

Incorporate backup connectivity options, such as failover to secondary circuits or LTE backup, for uninterrupted operations.

Design the network to accommodate future growth and increasing network demands.

Consider the scalability of network devices, bandwidth capacity, and network architecture. Plan for potential expansion, new store openings, and increased customer traffic.

Conduct a thorough assessment of each store’s infrastructure and requirements. Ensure sufficient power supply, cabling, and physical space for network equipment.

Address any environmental factors that may affect network performance, such as temperature control and ventilation.

Evaluate and negotiate contracts with bandwidth providers to ensure reliable and cost-effective network connectivity.

Establish Service Level Agreements (SLAs) for uptime, latency, and bandwidth guarantees.

Maintain ongoing communication and relationship management with providers to address any issues or changes.

Assess existing contracts with vendors and hardware providers that are no longer needed.

Plan for a smooth transition to new vendors and hardware, including contract termination and equipment returns.

Ensure proper coordination between the network design and procurement/contract management teams.

Prioritize critical store applications, like POS (point of sale), inventory management, and sensitive IP traffic.

Allocate appropriate bandwidth and network resources to ensure optimal performance for these applications.

Implement QoS mechanisms to prioritize real-time traffic and minimize latency or packet loss.

Implement robust network security measures to protect sensitive customer data and maintain PCI DSS (Payment Card Industry Data Security Standard) compliance.

Utilize firewalls, intrusion detection and prevention systems (IDPS), and secure remote access mechanisms.

Apply secure segmentation to isolate critical systems and restrict unauthorized access.

Optimize network bandwidth utilization by leveraging caching, compression, and content delivery networks (CDNs).

Implement traffic shaping and bandwidth management techniques to prioritize business-critical applications and limit non-essential traffic.

Segment the network to enhance security, improve performance, and isolate different store functions.

Separate guest Wi-Fi networks from the corporate network to ensure data confidentiality and prevent unauthorized access.

Segment store operations, point-of-sale, and back-office functions to limit the impact of potential security breaches.

Provide reliable and high-performance Wi-Fi connectivity throughout the store premises.

Plan for adequate coverage and capacity to support customer Wi-Fi, mobile devices, and IoT deployments.

Implement secure guest Wi-Fi with captive portals, authentication, and encryption mechanisms.

Develop a detailed migration plan outlining steps for transitioning from the legacy network to the new infrastructure.

Conduct thorough testing and validation of the new network before the switchover. Plan for minimal disruption to store operations during the migration process.

Roll out a proof of concept (POC) at a limited number of stores to validate the effectiveness of the network design.

Measure the performance, reliability, and security of the new infrastructure in real-world scenarios.

Gather feedback and insights from store staff and IT teams to refine the design before full-scale implementation.

Implement network monitoring tools to proactively identify and troubleshoot network issues.

Utilize network management systems to centralize network configuration, monitoring, and reporting.

Ensure real-time visibility into network performance, availability, and security events.

Adhere to industry-specific regulations such as PCI DSS, GDPR, and HIPAA, based on the retail chain’s operations.

Design the network to meet compliance requirements and implement appropriate security controls.

Maintain audit trails, access controls, and security documentation to demonstrate compliance.

Provide training sessions to educate retail staff on the new network infrastructure and its benefits.

Offer guidance on network usage, security best practices, and troubleshooting common issues.

Ensure that staff members understand how to utilize new applications and tools enabled by the upgraded network.

In-House Vs Managed Services Providers for Network Modernization

Can retailers and restaurant brands choose to implement complex network modernization projects internally or should they choose to work with managed service providers? While there will always be opportunities for in-house network transformation projects, most Businesses are choosing to partner with managed network services vendors. According to Gartner, “Enterprises struggling to balance expense reduction with greater WAN and LAN agility and performance are increasingly turning to managed network services.” Also, the need to lower capital spending and gain access to specialized skillsets needed for digital transformation make managed network services vendors a compelling option to consider. Here are some additional insights on what’s driving retailers to hire managed network services vendors.

The demand for diverse network management skill sets and the ability to hire and retain employees with specialized network engineering skills can be a tough challenge for enterprises who would rather spend the time and money to strengthen core business operations and build products or services.

For example, a medium-sized retail chain or a restaurant chain looking to design and roll out an SD-WAN solution will probably need highly skilled network engineering talent during the initial design and roll-out phase. Once the solution is stable, the IT organization’s focus will be on ongoing maintenance and periodic updates.

Network technologies are increasingly becoming complex and involve integrating solution components from a diverse set of hardware, software, and bandwidth providers. Implementing a sophisticated network connectivity backbone for optimal business application performance will involve a mix of third-party vendors and OEMs specializing in different aspects of the solution. Troubleshooting performance issues with a POS machine at any location can involve packet tracing across multiple devices, networks, and the ISP serving the location. Coordinating troubleshooting involving multiple vendor entities can be a significant drain on the internal IT team’s productivity. It can even compromise routine tasks that the IT team is primarily responsible for due to lack of time.

Retail and restaurant chains manage hundreds of geographically dispersed locations or branches. Relying just on internal capabilities and skill sets to manage IT/network operations is not practical for a multi-location enterprise. It’s not feasible for internal network engineers to travel across states or international borders to set up or troubleshoot network issues at branch locations.

In addition to managing a complex security environment, enterprises have to also comply with various data management regulations that are directly related to how secure and compliant the underlying network is. Compliance regulations such as PCI and SOX impose a significant burden on retail IT teams that are focused on complex network modernization challenges. Most retailers face significant cybersecurity challenges as they embrace the cloud and grapple with a combination of lower-than-average staff focused on security operations and inventive cybersecurity attacks that target POS and loyalty management systems.

The urgent need to transform business operations is felt a lot more in consumer-facing industries such as retail. In most cases, investments and upgrades in network technologies cannot be handled in-house due to the lack of skills or tools needed for such complex projects across multiple locations. Multi-location enterprises have complex networks that are often difficult to upgrade. Managed network services providers with proven expertise and the backing of the solution providers or hardware vendors are well suited to pilot untested technologies, demonstrating ROI before implementing the solution on a large scale.

For a more in-depth understanding of why businesses should hire a managed network services provider, please read this informative blog post.

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About the author

Bud Homeyer

Chief Operations Officer

Bud Homeyer is the Chief Operations Officer at Interface Systems. Having worked as an IT and security leader for leading consumer-facing brands like Michaels, Brinker, and Bank of America, Homeyer has a proven track record of solving complex enterprise-wide challenges to drive growth, productivity, and profitability. He spearheads Interface’s efforts to help customers embrace new technologies while minimizing risks.

6 Game-Changing Retail Technology Trends in 2024

Bud Homeyer — Sun, 08 Jan 2023 18:04:49 +0000

Retail

Bud Homeyer

Chief Operations Officer

Business Intelligence Retail Video Analytics

6 Game-Changing Retail Technology Trends in 2024

Digital Transformation in the Retail Industry

Introduction

The retail industry has already undergone significant transformation in the last decade as prominent retail brands invested heavily in digital transformation underpinned by cloud computing advances and widespread acceptance of e-commerce.

The pandemic in 2020 and the subsequent reopening of the economy have further accelerated changes driven by changing customer expectations and non-traditional demands of Millenials and Gen Z customers. Retailers have no option, but to continue investing in new technologies and stay ahead of the curve.

We look at six emerging yet important retail technology trends that retailers should evaluate in 2023.

1. Contactless Stores

What are contactless stores

Contactless stores refer to a suite of technologies and customer experiences that retail chains have implemented to minimize friction or delays experienced by customers at various touchpoints and minimize human contact in the entire buying experience.

Technologies and processes that enable a contactless buying experience include e-commerce, buy online and pick up in-store (BOPIS), self-service checkout kiosks, NFC-based payment cards, mobile wallets, and cashier-less checkout experiences such as Amazon’s Just Walk Out.

What are the key drivers for contactless stores?

While e-commerce, BOPIS/Curbside, and contactless buying experiences are not new, COVID-19 accelerated the roll-out of contactless technologies and delivery models as customers and retailers pivoted to overcome social distancing norms and safety concerns.

According to Visa, tap to pay transactions in everyday segments in the US, including grocery and pharmacy, have grown more than 100% over the last year (2019). 31 million Americans tapped a Visa contactless card or digital wallet in March 2020, up from 25 million in November, with overall contactless usage in the US growing 150% since March 2019.

According to Square, “Mobile wallets have accelerated in popularity due to the low- or no-touch interactions customers can make in order to complete transactions. And for businesses, the tap-to-pay technology can reduce wait times at checkout.”

eMarketer estimated that BOPIS sales revenue in the United States grew 107% in 2020 and will continue to grow upwards of 15% till 2024.

Technology implications of implementing contactless stores for retailers

Depending on the extent and scope of contactless store implementation, retailers may have to review bandwidth availability, LAN/WAN/Wi-Fi capabilities, network security, and security camera coverage at the store.

Cashierless stores require a bevy of technologies to work together. Computer vision, artificial intelligence-enabled cameras, RFID strips, infrared lead sensors, microphones, and mobile apps work together to offer the convenience of walking in and leaving the store without checking out.

POS system may need an upgrade to offer additional payment options and integrate POS with sensors. In some implementations (such as Amazon’s Just Walk Out), the concept of POS, as we know it today, may not even exist as a virtual shopping cart is updated in real-time as the customer adds items into the basket.

Self-checkout, digital wallets, and BOPIS require a recalibrated approach to loss prevention to address theft and online frauds. According to Visa, “Unlike conventional online orders, a BOPIS transaction may contain less information to leverage in assessing the risk of the order (for example, a BOPIS order may not contain a shipping address). Unlike conventional in-store transactions, a BOPIS transaction doesn’t benefit from the secure capture of the payment, like chip-on-chip or tap-to-pay methods.”

Customer privacy concerns can become an issue when every customer is uniquely identified and tracked throughout the store. While mobile loyalty apps and guest wifi systems are already tracking customer behavior in-store, advanced contactless stores rely on enhanced customer movement tracking which may raise uncomfortable questions regarding customer privacy.

2. Artificial Intelligence (AI) Enabled Security Cameras

What Are AI-Enabled Cameras?

AI cameras use artificial intelligence to make sense of the videos being recorded. They can be programmed to detect certain types of objects or human activity, movement, removal of objects, read license plates or even recognize faces.

AI-enabled security cameras can boost the effectiveness of remote video monitoring or surveillance.

Security operators monitoring the feeds can get real-time alerts when the camera detects any of the anomalies the AI program is trained for thereby allowing security teams to act before a crime is committed.

AI-enabled security camera systems offer a searchable footage library making it easy to quickly find footage of relevance and doing away with the need sift through hours of recorded footage during investigations.

Advanced AI-enabled security cameras have deep learning capability and can become progressively good at recognizing patterns and detecting anomalies in the video being recorded.

Interface’s autonomous Anti-Loitering System includes an intelligent IP camera that uses artificial intelligence to detect people or vehicles and play customizable pre-recorded warning messages.

What Are the Key Drivers for AI-Enabled Cameras?

A recent market study concluded that the global AI camera market was worth $7.4 billion in 2020 and is further projected to reach $33.3 billion by the year 2027, growing at a CAGR of 24.0%.

A combination of factors is driving up demand and use for AI-enabled cameras.

A study conducted by PwC showed that US companies accelerated investments in AI technologies in the wake of the COVID crisis. According to PwC, “AI leaders are building a virtuous cycle, sometimes called a flywheel: AI leads to better products, increased productivity, and superior customer experiences.”

AI technology has matured in the last few years to a point where anyone can access AI frameworks and pre-built solution components on popular public clouds like AWS, Azure, and Google Cloud Platform. The ROI of implementing AI-enabled cameras is now compelling resulting in increased adoption.

The U.S Bureau of Labor Statistics estimates that the labor market for investigation and security services is expected to grow by 6.5%. However, security monitoring service providers are finding it challenging to hire and retain security professionals. AI-enabled cameras can address this issue by delivering increased productivity and improving the quality of monitoring jobs by eliminating monotonous tasks.

5G rollout is turning out to be a catalyst for the growth of sensors with edge computing capabilities. Sophisticated cameras with AI capabilities on 5G networks can push more high-quality video streams faster for further processing and analysis resulting in superior monitoring outcomes.

Technology Implications of Implementing AI-Enabled Cameras for Retailers

Enterprises looking to upgrade analog cameras can take one of the below approaches to executing the transition.

Replace analog cameras with IP cameras (with AI capabilities) along with the cabling. This is a “rip and replace approach” and the investment needed will depend on the number of sites and number of cameras that need to be replaced.

Replace the cameras without replacing the coaxial cables is an option worth considering if the cable runs are complex. In this case, IP to coaxial cabling adapters can push the videos into a video management system.

Start with proven AI security solutions before advancing to solving complex problems that might require additional sensors and sensor fusion. AI use cases such as intruder detection during off-business hours, and loitering in the perimeter area is proven and can deliver ROI faster.

AI security cameras can improve productivity and enhance the effectiveness of the monitoring team and cannot necessarily replace human monitoring. AI solutions do not understand the context of the visuals being recorded and require human review to take vital decisions.

3. Retail Video Analytics

What Is Retail Video Analytics?

Retail video analytics is a relatively new category of retail applications that leverages technologies such as computer vision and AI to capture real-time information from security camera footage.

Most retail chains have installed security cameras at all their stores. These security cameras record vital information that can be used to gain valuable operational insights about retail operations and customer behavior inside the store.

Data that can be captured with video analytics include – customer movement or flow, customer engagement with products and shelves, the effectiveness of the store layout, the impact of online promotions on foot traffic, customer service experience at the checkout, and compliance issues related to product display, spills, and cleanliness.

What Are the Key Drivers for Retail Video Analytics?

There are three primary factors driving the adoption of retail video analytics.

There was a massive shift in retail consumer behavior due to COVID-19. Retailers that had the capability to predict and react to these changes fared better than those that were too slow to respond. Retail video analytics can help retail chains move beyond descriptive analytics and implement predictive analytics to answer questions in real-time such as “What window display unit maximizes footfall conversion for a specific store format?” or “How many checkout counters should be opened between 5 pm to 7 pm on a Friday for a particular location to minimize checkout times?”
According to PwC, “Success at the shelf is no longer about the depth and breadth of inventory, but rather creating engaging experiences for customers.” Creating engaging customer experiences calls for real-time access to customer behavior data in the context of the store design or layout, time, and a slew of external contextual factors.
Profit margins are tight even as retailers retool their operations to cater to changing customer expectations. The only way retailers can find the right business model is to release products in the shortest time possible, gain tactical insights in real-time, and validate the success or failure based on measurable customer actions. Retail video analytics allows retailers to rapidly identify correlations (For example, “what’s the impact of a new layout on sales?”), validate the hypothesis (For example, does a new layout increase sales at all locations or at all store formats?), and consistently implement changes across all locations (For example, are all locations implementing the new layout as per the specifications?”).

Technology Implications of Implementing Video Analytics for Retailers

Implementing a retail video analytics solution is not a complex initiative. However, the real challenge is in identifying the right metrics to track and having the capability to draw meaningful inferences. From a retail IT organization perspective, here are the top considerations when implementing retail video analytics.

The primary sensor for gathering data from a video is the security camera. Security cameras are installed with loss prevention as the primary use case and the installed cameras may not necessarily meet the data gathering requirements for a video analytics project. Camera upgrades and new installations may become a necessity.

The ROI of a retail video analytics implementation is directly dependent on the ability to fuse data from multiple data sources to identify correlations and frame hypotheses about customer behavior. Solutions with well-documented API and integration capabilities should be preferred.

Video analytics solutions can track customer movement, product interactions, and behavior inside the store. When used in conjunction with AI-enabled cameras with facial recognition capabilities, retailers can run the risk of violating customer privacy.

Retail video analytics can be used by diverse departments or teams in a retail organization. Loss prevention, marketing, operations, human resources, compliance, legal, and merchandising teams have compelling use cases for reviewing customer as well as employee activities within the store. A robust system to regulate access to video analytics data is a critical requirement for IT teams.

4. Metaverse

What Is Metaverse?

According to Merriam-Webster, “In its current meaning, metaverse generally refers to the concept of a highly immersive virtual world where people gather to socialize, play, and work.”

Metaverse is still an emerging concept with bits and pieces of the building blocks being assembled and considered work-in-progress.

The following table breaks down the building blocks that together will eventually make up the metaverse (Source).

Metaverse building blocks

Vendors

Experience – What we experience in the metaverse in the form of games, events, or live music.

Meta, Activision Blizzard, Nintendo

Discover – How people find about the existence of experiences in the metaverse.

Facebook, Discord, Unity, App Store, Google Play

Creator Economy – Everything that’s used to create and monetize assets on the metaverse.

Unity, Roblox

Spatial Computing – 3D engine, gesture recognition, spatial mapping, and AI.

Unity, Google AI, OpenAI, Unreal Engine, Matterport

Decentralization – Making everything in the metaverse accessible to all participants and decentralized.

OpenAR, Etherium, Polygon, Ready Player Me

Human Interface – Mobile devices, VR headsets, haptics, and smart glasses.

Oculus, Apple, Playstation, Samsung

Infrastructure– Cloud computing, semiconductor, telecom/bandwidth.

GCP, AWS, Azure Intel, NVIDIA, AMD, Qualcomm, Intel, ATT

The Gucci Gardone on Roblox. Source – Roblox

What Are the Key Drivers for Metaverse Adoption?

A diverse set of factors are forcing retailers to take notice of the metaverse and some retail brands have already invested in creating a presence in the metaverse.

The shift to work from a home model and travel restrictions on account of the pandemic has also brought about a decisive shift to “shop from home” or “shop from anywhere”. For example, as people are more comfortable shopping for clothes online, apparel stores are losing footfall steadily. With e-commerce registering rapid growth, the metaverse can potentially become a competitive advantage in the future as retail brands are looking to create amazing customer experiences.

Retail chains are discovering the value of immersive training experiences that metaverse can provide. For example, Walmart rolled out a comprehensive virtual training program for all their US stores using the Oculus VR headsets. Walmart associates are trained on customer service, store operations, security threats, and BOPIS management through immersive, real-world experiences. Walmart claims immersive training has reduced training duration, improved knowledge retention, and enabled consistent implementation of customer service best practices.

Luxury retail brands such as Louis Vuitton are betting on getting a headstart with metaverse for brand building. In 2021, Louis Vuitton launched Louis The Game, a mobile game with 30 embedded NFTs (non-fungible tokens) that can be found only by playing the game. Balenciaga has launched a division in the company that’s focused on building a presence in the metaverse. Gucci’s Garden Experience on Roblox offers people the opportunity to experience themed rooms to experience the virtual gardens, share their experiences with others and win virtuals prizes. Through metaverse, luxury brands want to get in front of young audiences who may otherwise not otherwise interact with their brands.

Technology Implications of Implementing Metaverse Presence for Retailers

The metaverse is a nascent concept and making any investments in the metaverse has to be carefully calibrated with a clear focus on outcomes. Here are some of the considerations for retail CIOs and management teams looking to join the metaverse bandwagon.

Investing in the metaverse is likely to be a risky venture in terms of meeting business objectives. The metaverse as we have it today falls well short of the seamless interoperability that it aims to provide in the future. Any investments made now on a vendor platform may become irrelevant in the future as industry standards and solution frameworks are still evolving.

According to Gartner, the current maturity of metaverse solutions is collectively called the “Emerging Metaverse”. The emerging metaverse is likely to morph into an “Advanced Metaverse” by 2025 and later into a “Mature Metaverse” by 2028. With time, the technology risks are likely to come down and the ROI of metaverse initiatives will be much clearer for retailers.

Metaverse initiatives are spawning legal challenges and roadblocks for retail brands. StockX, a reseller for sneakers and other items, launched Nike shoes NFTs that can be purchased and traded online. Nike filed a trademark infringement suit. This is an example of some of the potential roadblocks retailers may face when creating a presence in the metaverse.

While there are risks in investing scarce Dollars in a metaverse initiative, being left behind is also not an option for retailers. A better strategy would be to make small bets on a wide variety of metaverse platforms to see what’s working and what’s not.

- Creating a presence on metaverse will require new IT skills either in-house or with the vendors closely aligned to the IT organization. When Home Depot implemented blockchain technology to track vendor supply chain and invoicing issues, they relied on IBM to support the implementation. Another scenario would be when retailers decide to accept cryptocurrency, They need to evaluate not only the technical challenges of integrating a crypto payment gateway but also figure out the most suitable cryptocurrency exchange to encash the payments.

5. Retail Demand Planning

What Is Retail Demand Planning?

Retail demand planning is the process of forecasting demand for products across all channels (in-store, e-commerce, BOPIS, ship from store) taking into consideration historical demand patterns, business decisions and external factors such as competitor offers.

Demand planning helps retailers navigate complex challenges across all aspects of retail operations.

Streamlined store and distribution center replenishment
Better workforce planning and optimization
Optimized product promotions and discounts
Optimized assortment planning
Better space planning and optimization
Improved budgeting and cash flow management

What Are the Key Drivers for Retail Demand Planning?

The pandemic forced retail chains to go through years of growth in just a few months! e-Commerce and new fulfillment channels are no longer seen as a differentiator as customers expect to find the products they want no matter where and how they shop.

The only way retailers can meet these expectations is by upgrading to a data-driven, real-time demand planning approach. Retailers are already upgrading their demand planning capabilities to process more data or variables in the planning process at scale while relying on human planners for qualitative input and exception management.

Here are some of the key drivers driving demand planning innovation in the retail industry.

Trust depends on retailers’ ability to deliver value and excellence in managing the inventory. Having the right product and getting it to their hands in their preferred channel is a great strategy to rebuild trust, According to Walter Robb, Former Co-CEO of Whole Foods, “Some customers say they want to be served in certain ways, such as grocery customers saying they want to pick up items in-store or have certain things delivered. It’s up to brands to develop the capabilities to serve customers how and when they want to be served. Brands earn trust when they listen to customers and are willing to meet their needs.”

Fulfillment complexity is increasing with new channels graduating from a fad to becoming mainstream. In addition to e-Commerce and BOPIS, grocery retailers will soon have one more fulfillment center, i.e the customer’s home. According to Gartner, by 2025, ‘at-home replenishment as a service’ will surge to 5% of multi-channel grocery retailers’ total revenues. Amazon and Walmart are already piloting smart refrigerators and coolers that can be automatically tracked and restocked.

Customer consumption patterns are also evolving and the pandemic created distinct consumption patterns that impacted demand planning for retailers. McKinsey identified four distinct consumption patterns during and after the COVID-19 pandemic:

Pantry load and consume (consumption expands). Example: Cleaning supplies, vitamins, and supplements. Consumption increased during the pandemic and continues to hold ground even after the pandemic.

Pantry load and preserve (consumption does not change). Example: Pet food and toilet paper. Consumption did not actually increase during the crisis, resulting in post-crisis volume declines.

Now at home (consumption shifts). Work-from-home and shelter-in-place policies force consumers to shift on-the-go occasions for products such as coffee and alcohol from in-person food-service outlets to online retailers.

Not now (consumption declines). Declining consumer confidence and a focus on essential categories lead to a decrease in purchasing certain goods during the crisis—for example, beauty, food, and beverages for immediate use. Purchasing levels for these categories are returning to normal after the crisis.

While most retailers have a lot of valuable data, many of them still lag behind in their ability to extract meaningful business insights from the data on time. Considering the retail market is going through a dynamic growth phase driven by changing customer preferences, there is a need for innovative approaches to demand planning. According to Relex, a retail demand planning platform, retailers can bridge the data utilization gap by combining artificial intelligence and machine learning with statistical modeling and optimization, simulation, classification, as well as rules, and heuristics.

Better demand planning requires an ability to crunch data at scale. Advances in in-memory computing, hybrid analytical/transactional data processing, and cloud computing infrastructure that can scale processing on-demand have allowed demand planning vendors to deliver innovative products for realistic scenario simulations.

Reducing food waste is becoming a key challenge for grocery retailers. Increasingly socially conscious customers are looking to spend their money with retailers aligned with similar values. California’s SB1383 legislation mandates grocery retailers to follow specific guidelines to dispose of unsold produce. The goal of this legislation is to minimize produce being dumped in landfills (which contributes to pollution) and instead divert the produce to non-profit organizations. Retailers failing to meet the norms are liable to pay penalties.

Technology Implications of Implementing Retail Demand Planning

The following are some of the factors CIOs and IT organizations should consider when choosing to implement sophisticated demand planning applications.

Among the several critical features required in a retail demand planning platform, Forrester has identified segmenting and clustering, demand management, inventory planning and operational effectiveness, and agile merchandising as core features to be evaluated when choosing a retail demand planning application.

Any plans for a unified demand planning software implementation should take into consideration organizational bottlenecks. If the retailer has planning teams working in silos with each team responsible for specific channels, implementing a demand planning solution that requires closer collaboration and joint decision-making on a daily basis will cause friction and turf wars. Implementing a demand planning software will involve some level of organizational restructuring to leverage all the benefits associated with a collaborative approach to demand forecasting cutting across channels.

Considering the intensive compute requirements for running machine learning and AI algorithms to create demand forecasts, retailers need to budget for cloud computing cost escalations along with fund allocations for backup and disaster recovery.

Any retail demand planning implementation should take into consideration robust data-sharing protocols with suppliers. The applications should have the ability to automatically share demand data at the right level of granularity that the suppliers can then use to plan their deliveries and manufacturing. A feedback loop may also be needed from the supplier end to plan for any variation between demand and supply.

Data quality and data governance are critical to the successful implementation of any demand planning software. Retail chains that have grown through acquisitions and saddled with diverse systems with inefficient data collection or data centralization processes can find it challenging to implement a unified demand planning solution. A focused approach to solving data collection and data quality issues should be in every retail CIO’s roadmap.

6. Generative AI

What Is Generative AI?

Generative AI creates new content, such as images, videos, music, text, or other forms of data. Unlike traditional AI models used by retailers that are primarily focused on classification or prediction tasks based on large data sets, generative AI models are designed to produce original and creative outputs.

Retail chains can leverage generative AI in various ways to enhance their operations, customer experiences, and decision-making processes. Here are a few examples of how generative AI can be used in the retail industry:

Generative AI models can be employed to create realistic product images or videos for e-commerce and catalogs even for items not yet photographed. This accelerates product development and testing without having to invest in manufacturing expensive samples that will never be sold. Merchandising teams can accelerate the process for new product development and display formats.
Generative AI can power virtual try-on applications, enabling customers to virtually “try on” clothing, accessories, eyewear, or cosmetics. By using computer vision and generative models, retailers can overlay virtual representations of products onto real-time video or images of customers, allowing them to see how items would look on them before making a purchase decision.
Retail chains can create virtual assistants and chatbots that can interact with customers, answer inquiries, provide product recommendations, and assist with the purchasing process using natural language responses, making customer interactions more conversational and personalized.

According to McKinsey, In the next three to five years, generative AI could add $150 billion, conservatively, and up to $275 billion to the apparel, fashion, and luxury sectors’ operating profits.

What Are the Key Drivers for Generative AI

Generative AI has rocketed to prominence on the back of several enabling factors that allow this advanced technology to find its way out of research to the mainstream. Here are some of these factors propelling the growing adoption of Generative AI.

Retailers have been grappling with labor shortages. In 2022, we saw the “great resignation” wave and the industry hasn’t recovered from the trend yet. According to the US Chamber of Commerce, the quit rate for the retail trade industry is hovering around 3.3 percent so far in 2023 against a falling national average of 2.6 percent. Generative AI offers a credible solution as retailers find ways to mitigate labor shortages and improve employee productivity.
The cost of AI model training has been coming down making AI accessible to enterprises and startups. According to a report from ARK Investment Management, “AI training cost declines continued at an annual rate of 70%, the cost to train a large language model to GPT-3 level performance collapsing from $4.6 million in 2020 to $450,000 in 2022. We expect cost declines to continue at a 70% rate through 2030.”
Microsoft invested over $10 billion in OpenAI in January 2023 as the company laid out a plan to integrate OpenAI’s ChatGPT with Azure to enable customers to build enterprise-grade AI applications. The speed with which ChatGPT has found traction among consumers has also created a credible proof-point for retailers to embrace Generative AI.
CIOs are already investing in building AI applications. According to this report from Databricks, 60% of CIOs surveyed planned to deploy AI-based solutions across all departments by 2025.

Technology Implications of Implementing Generative AI Applications for Retailers

According to the World Economic Forum, “Many organizations are underprepared for AI, lacking the proper oversight and expertise from key decision-makers to manage risk.” The truly transformational nature of Generative AI comes with the risks associated with venturing into a relatively new territory for retailers. Here are some of the technology implications for retailers planning on implementing Generative AI applications:

Generative AI models often require large amounts of data for training. Retail chains need to ensure they have the necessary data infrastructure to collect, store, and process the data efficiently. This may involve implementing data management systems, data pipelines, and scalable storage solutions.
Training and running generative AI models can be computationally intensive. Retail chains may need to invest in powerful hardware or leverage cloud computing services to handle the computational requirements. According to Tirias Research, Generative AI’s data center costs are projected to surpass $76 billion by 2028, potentially impacting the profitability of services like search, content creation, and business automation.
Developing and training generative AI models requires expertise in machine learning and deep learning techniques. Retail chains may need to hire or collaborate with data scientists and AI specialists to build and fine-tune these models. They also need to allocate time and resources for model training and experimentation.
Generative AI solutions need to be integrated with existing retail systems and workflows. This may involve connecting with inventory management systems, point-of-sale systems, customer databases, and other relevant systems. Integration may require developing APIs or middleware to facilitate data exchange and communication.
Generative AI can raise ethical concerns, such as the potential for bias or misuse of generated content. Retail chains need to be aware of these considerations and establish guidelines and frameworks to ensure fairness, transparency, and responsible use of the technology.
Once the generative AI solution is deployed, it requires ongoing monitoring and maintenance. This includes monitoring the model’s performance, addressing any issues or errors, retraining the model periodically to improve its output quality, and staying up to date with the latest advancements in generative AI research.

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Retail Store Layout Optimization with Video Analytics

Steve Womer — Fri, 06 Jan 2023 20:00:01 +0000

Retail

Steve Womer

SVP, Engineering

Business Intelligence Retail Video Analytics

Retail Store Layout Optimization with Video Analytics

Introduction

What is Retail Store Layout?

Retail store layout refers to how brick-and-mortar retailers set up product displays, fixtures, and merchandise in their stores. A well-planned store layout can positively influence customer experience, a critical factor to drive product engagement, sales, and improve customer lifetime value.

Retailers have several tools to gather and analyze data to understand if the store layout is working well and identify problem areas that need optimization. Some of these tools include store audits, mystery shopping, and customer surveys or shopper feedback.

However, there are three fundamental limitations of using these measurement techniques for evaluating store layout effectiveness.

Some of the observations may be subjective or subject to interpretation.
Observations or feedback from a customer or from a store audit may or may not be consistently true across all locations or at all times.
There is no way to access real-time data on store layout performance using any of these traditional measurement methods.

Video analytics addresses these limitations as it is driven by aggregated data, captured and processed in real-time, at all locations where the store layout is being monitored.

That’s not all. Video analytics can be implemented by leveraging security cameras that the store may already have in place. Click here to learn more about video analytics.

In this blog post, we will explore how retail chains can optimize store layout using video analytics.

Types of Retail Store Layouts

Let’s explore the pros and cons of retail store layouts along with examples of prominent retail chains and categories employing these strategies.

Grid Layout

Pros:
Easy navigation for customers.
Efficient use of space, allowing for a high product density.
Simplifies inventory management and restocking.

Cons:
Lack of uniqueness in design.
Limited opportunities to create focal points or highlight specific products.
Example: Large format retailers like Walmart and Target often adopt a grid layout to streamline the shopping experience and accommodate a vast array of products.

Loop or Racetrack Layout

Pros:
Encourages exploration as customers follow a predefined path.
Promotes impulse buying with strategically placed displays.
Ideal for larger stores with a wide product range.

Cons:
Potential for congestion and bottlenecks, especially during peak hours.
Limited flexibility in creating distinct shopping zones.

Example: IKEA, a furniture and home goods retailer, utilizes a loop layout, guiding customers through various showroom sections and strategically placing smaller items in the pathway to encourage additional purchases.

Free-Flow Layout

Pros:
Offers a relaxed and casual shopping environment.
Encourages creativity in display and decor.
Allows for easy adaptation to changing merchandise or seasonal themes.

Cons:
Challenges in guiding customer flow and promoting specific product areas.
Risk of customers missing certain sections of the store

Example: Apple Stores, known for their electronics and tech products, often feature a free-flow layout, allowing customers to interact freely with products, creating a more personalized and engaging experience.

Angular Layout

Pros: Creates a visually interesting and dynamic store design. Facilitates the establishment of distinct departments or product categories. Encourages customers to explore different sections of the store.

Cons: Requires careful planning to avoid confusing navigation. May result in underutilized space in corners and angular areas.

Example: Zara, a fast-fashion retailer, often employs an angular layout to create visually striking displays and highlight specific clothing collections.

Geographic Layout

Pros: Organizes products based on their intended use or category. Provides a logical and intuitive shopping experience. Enhances the customer’s ability to find specific items easily.

Cons: Limited flexibility in adapting to changing merchandise or promotional displays. Challenges in creating a visually appealing overall store design.

Example: Best Buy, specializing in electronics and appliances, organizes its stores based on product categories, adopting a geographic layout to simplify the customer’s search for specific items.

Mixed-Use Layout

Pros:
Combines elements of different layouts for a customized approach.
Offers flexibility to cater to various product categories.
Allows for creative and innovative store design solutions.

Cons:
Requires careful planning to maintain a cohesive and harmonious overall look.
Potential for confusion if not executed seamlessly.

Example: Sephora, a beauty and cosmetics retailer, often adopts a mixed-use layout. The store seamlessly integrates grid-like shelving for a wide range of products, while also incorporating free-flowing beauty stations that encourage customers to experiment with makeup and skincare products, creating an interactive and personalized shopping experience.

Significance of retail store layout

Before we explore the various strategies to deploy video analytics for measuring store layout effectiveness, let’s understand how retail layout has a direct impact on customer satisfaction and store performance.

1. Create a great customer experience

The massive shift to ecommerce and BOPIS (Buy Online and Pickup in Store) has turned the spotlight on in-store customer experience.

Even pure-play ecommerce brands like Amazon are opening brick-and-mortar locations to offer customers the experience of traditional shopping with never-before-seen convenience.

Customer experience is probably the single most important tool that brick and mortar retailers can use to give customers enough reasons to come back to the store boosting chances for engagement and conversion.

Retail chains with the capability to measure and monitor changes in customer experience can make changes in the store layout so that product categories that are in high demand can gain prominence.

REI has created a unique in-store experience that includes a combination of interactive displays, expert sales staff, and in-store activities that give customers who enjoy the outdoor lifestyle plenty of reasons to visit an REI store.

2. Increase sales

A good store layout plus customer flow combination can increase sales of even the most underperforming product. A layout that reflects specific shopper missions and needs can help customers achieve their mission and increase sales of products that meet those mission requirements. Here is an illustration of how store design elements can help a customer accomplish their mission.

Customer Mission: I need to wrap up shopping before the meeting.

How Store Layout Can Help: Layout that places popular product categories prominently

Store map at entrance: Improved signage for all departments with popular products listed on the signage

Customer expectations, needs, and wants are dynamic and retailers with the ability to optimize and reinvent store layout can turn challenges into new sales opportunities.

For example, putting BOPIS or click-and-collect counters near the store entrance makes it more likely that customers will use this service in the future, helping retailers capture more of this market in the future. Since the click-and-collect market in the U.S. is forecast to grow to $120 billion and beyond by 2023, this type of store layout option can increase overall sales dramatically for retailers.

3. Manage stock more efficiently

Managing space optimally can maximize sales, minimize stockouts. improve employee productivity and align with the objectives of the store itself.

For example, a store that’s designed to introduce new products and carry little inventory is designed to maximize product engagement with checkout counters taking the backseat completely as employees focus on product engagement and possibly help customers place online orders at the store. A classic example of a store design that focuses on product engagement is the Apple store.

A store that’s focused on convenience and breadth of assortment such as a specialty grocery store has to be designed for easy access and having enough space for carrying all the assortments.

Trader Joe’s small format stores focus on convenience and quick access to a wide variety of specialized grocery and food items. Their design is easy to work with not only for customers but also for employees. Their inventory management and ordering is perfectly timed with demand and shelf breaches (items being placed outside the designated areas) or prolonged stockouts is never a problem as the store layout gives easy visual and physical access to all aisles.

4. Minimize theft

According to the 2020 NRF Security Survey, retail shrink is at an all-time high, accounting for 1.62% of a retailer’s bottom line — costing the industry $61.7 billion. While retailers use a variety of technology solutions such as video surveillance, protective display cases with locks, chords, EAS (electronic article surveillance), retail store layout is one of the many factors that determine the success of loss prevention efforts.

Some of the store layout and design principles that minimize theft and an assortment of inventory losses include:

A design that offers a clear line of sight for employees at the checkout counters to all parts of the store.
A layout that doesn’t obstruct camera views or signals from EAS installations.
A layout that avoids cramped spaces and allows employees to identify misplaced items quickly.
A design with well-planned exits that minimizes chances of snatch and grab thefts.

5. Improve safety and accessibility

Safe retail store design keeps employees safe from environmental and health hazards. A store layout that’s accessible ensures anyone can access the store regardless of ability.

Depending on a store location, any retail layout has to comply with regulations and policies associated with safety, security, and accessibility.

A well-designed retail store will take into consideration a number of design parameters that directly impact safety and accessibility. Here are some examples:

A store layout with aisles and customer paths wide enough to accommodate wheelchairs with adequate ramps and accessible facilities throughout the store.
Ergonomically designed display units or shelving to prevent injuries for customers or employees handling the product.
Store layout designed for proper airflow and ventilation throughout.
Stores with properly designed flooring and elevated platforms to avoid trips or falls.

Designing an accessible store layout is good business. According to the United States Department of Justice, “More than 50 million Americans with disabilities are potential customers for retail businesses across the country. These 50-million-plus customers, along with their families and friends, patronize clothing boutiques, mall outlets, grocery stores, and more, if the businesses are accessible. This market grows even larger if the 78 million baby boomers in this country – who do not always require but benefit from accessibility – are included. Accessibility makes good business sense: an accessible retail establishment brings in new customers and keeps them coming back again and again.”

Store layout optimization strategies using video analytics

1. Streamline customer flow

Video analytics is well suited to map and measure customer flows through the store. Understanding customer flow helps retailers evaluate the effectiveness of the store layout, understand the best possible placement of products, measure customer actions at every section or department, measure the impact of specialized displays, in-store promotions, or marketing campaigns, to name a few.

Video analytics, when combined with POS data, can help retailers conduct a real-time audit of the store layout to identify bottlenecks in the design, and align the layout to help customers successfully accomplish their shopping missions.

Here are some of the ways video analytics can help retailers troubleshoot store layout issues to improve customer experience and revenues:

Use pathmap metrics to understand how customers move around in the store. Redesign the store layout or make changes to problem areas in the store to drive desired customer actions such as better footfalls in previously low activity areas or reorganize the placement of products to help customers get what they want faster.
Identify and eliminate ‘dead zones’ in terms of footfall activity or engagement and take remedial actions such as better signage, lighting, or a redesigned layout.
Use traffic data at store entrance and exits or department/category entry and exits to measure overall or department/category level conversion metrics. Analyze conversion metrics together with other factors that determine conversions such as weather or seasons. Test hypothesis on reasons for conversion with a/b testing spanning layout, product placements, promotions, or in-store displays/announcements.
Track dwell time across the store to understand customer interest in specific products as well as flow bottlenecks that force customers to wait longer than expected such as in the checkout areas. Use these insights to offer more products that customers want or reorganize the checkout flow to reduce wait time.
Dwell times at entrances of different departments can measure bounce rates and call attention to better product placement, staff engagement to bring guests further into the store.

Store floor plans dictate customer flow and how customers interact with the products. Irrespective of the type of store layout or floor plan (grid layout, herringbone, free-flow, geometric, mixed, to name a few), video analytics can play an important role in uncovering real-time business intelligence on how the layout is performing. The pathmap overlay on the video stream from the overhead camera offers valuable data on how the layout is impacting customer flow through the store.

2. Delight customers with a better checkout experience

The last impression is probably the best impression for retail chains looking to provide a memorable customer experience. A long checkout queue can completely wipe out any positive experience customers might have had at the store before they wanted to checkout. In some cases, customers in a hurry can just abandon their carts and never return to the store!

Retailers have already implemented an assortment of improvements to make the checkout process efficient. These include changes in how queues are organized, self-checkout kiosks, app-based self-checkout, shelves near the checkout counters to entice impulse purchases, to keep the process predictable and customers engaged as they wait for their turn to pay.

Video analytics can help retailers use real-time data to understand what’s really happening at the checkout area.

Because video analytics offers vital video “evidence” that retailers can see first hand to understand queue efficiency and cart abandonment.
Video analytics can identify dwell times at queues to calculate the average waiting time for a customer and use the data to plan for additional checkout lanes or self-checkout counters in addition to ramping up staffing levels to meet periodic demand surges.
With video analytics, retailers have the flexibility to conduct data-driven tests when making significant changes to the checkout area design flow before rolling it out to the rest of the stores. The impact of layout changes on the average transaction value and items per transaction can help retailers take a decision on the revamped store layout.

Here is an example of a report from Interface’s video analytics solution that provides average dwell time waiting in queue and the average time at the checkout counter. This data can be viewed in the context of how effective a new checkout experience or layout is. In addition, this data can be mapped to other factors such as time of day, day of the week, holidays or staffing levels:

3. Improve product placement

Product placement is probably one of the most important factors that impact retail sales and is closely tied to the store layout.

While planograms offer the template to slot products in the shelf space, real-time data-driven decisions can help retailers maximize sales by aligning shelf space to customer preferences and other factors that drive purchases such as the weather or local events.

Video analytics can be a game-changer in giving retailers instant visibility on potential product placement issues and identifying opportunities to maximize basket size.

Some of the insights retail video analytics can uncover to improve product placements include:

Hotspots around display units and shelves to precisely identify the products that customers are looking at or interacting with. This allows the retailers to identify products that need to be relocated to boost sales or change the assortment around the hotspots to promote cross-sell.
Retailers can look at dwell time data to understand what might be causing customers to interact with the products in shelves with high dwell times and find a correlation to other factors such as time of the day, packaging, marketing campaigns (in-store or online), or any other factor that might be driving increased dwell time.

Retailers with data on the performance of shelf space across product categories and departments can optimize the store layout and display units or even change the design of the store to offer most product categories the best possible exposure to customers.

Video analytics can empower local teams to make store layout decisions based on real-time data while ensuring planogram compliance or even using data as evidence to change the planogram.

Here is a heatmap view from video analytics that precisely highlights the product displays that attracted maximum customer interactions or engagement. Using this data in conjunction with sales data or marketing campaign calendar can uncover challenges and opportunities with the product placement tactics or even store layout changes to improve performance.

4. A/B test store design or layout changes

The practice of A/B testing is a common practice to test or validate ecommerce sites and online experiences. With video analytics, retailers have the capability to conduct real-time, data-driven A/B tests to validate the effectiveness of the store layout or a specific component such as a brand new display unit measured in terms of dwell time, heatmaps, and sales conversions.

In addition, video analytics also allows retailers to A/B test the effectiveness of store design on new customer behaviors such as BOPIS. For example, video analytics can help find answers to questions like “Would it make sense to design the in-store pickup area to showcase products that are not available online?”.

Here are four different product display designs and the associated pathmaps of customers interacting with the displays generated by the Interface video analytics solution

With video analytics, retailers have the flexibility to test any number of store layout or design variations across multiple locations at a time. This is a useful feature that allows retailers to

Conduct multivariate A/B tests with the right sample size
Ensure tests run for the required duration to reach the threshold KPI uplift

5. Supercharge merchandising tactics

The merchandising function is at crossroads as retailers scramble to offer an omnichannel customer experience and stay ahead of evolving customer expectations. Technology solutions, like video analytics, can play a significant role in helping merchandising teams recalibrate their priorities, and become agile to keep pace with changes.

According to McKinsey, the merchandising function will be driven by automation and here is how video analytics can help enable the transition to agile merchandising.

Merchandising area of responsibilities

How video analytics can help

Merchandising planning

Video analytics, when combined with POS data, allows merchandising teams to get real-time visibility on customer demand for categories, assortments, sizes in addition to predictive insights on how they are impacted by geography, time, and events right down to the store level.

Pricing and promotion

With video analytics, retailers can see how pricing and promotions impact customer walk-ins, product engagement, and sales giving merchandising teams complete visibility into the sales funnel. This insight is valuable to make pricing adjustments or recalibrate promotions to drive sales.

Assortment planning

When real-time customer engagement data at the shelf level from video analytics are combined with sales data, retailers can optimize assortment planning and change shelf space allocation or placement of specific assortments to maximize sales and profits.

Space planning

Video analytics can provide real-time feedback on the effectiveness of the planogram in driving engagement and giving customers easy access to the products they want. This data can become the basis for a feedback loop to improve the planogram and use the shelf space better.

Here is an example of customer engagement data for a display unit. Merchandisers can use this insight to manage shelf-level displays or even floor layouts. In addition, inventory checks and audits can be done in real time without waiting for onsite audits.

6. Improve storefront design

Storefront design has always been a major factor in retail success. When done right, creative storefronts make new customers feel curious and existing customers satisfied with their choice.

Video analytics can help retailers gather valuable data to improve the performance of storefront design.

Heatmaps and dwell times can be used to understand which display elements are attracting attention.
Changes in display design or layout can be put through an A/B test to measure improvements in engagement and walk-ins.
Pathmaps can show how many people walk past the storefront (bounce rate) and what percentage of people walking by enter the store.
All of the above insights can be correlated to other data such as holidays, time of day, day of the week, promotions, online campaigns to name a few to give a better insight on storefront designs that encourage desired customer behavior.

Different storefront window treatments can have their effectiveness measured via customer entry counts.

7. Ensure layout and design compliance

There are three reasons why store layout and design compliance is a major operational requirement in retail chains:

One of the key promises of a brand is delivering consistency at every touchpoint. In-store design and layout is an important factor that helps retailers create a unique yet consistent experience for customers.
Layout and design compliance is also a key requirement for brands that may have paid for premium shelf space or set up special display areas. Retailers rely on physical in-store audits to verify design or layout compliance across stores.
Ensuring that the store layout does not cause unintended issues such as closed spaces, bottlenecks, or blocks access to lighting or surveillance which might contribute to poor safety or health hazards for employees and customers.

Video analytics offers retail compliance, marketing, and merchandising teams monitor store layout and design compliance at scale across a large number of locations. The screenshot below demonstrates how Interface’s video analytics solution enables a visual verification of layout and design compliance across stores.

In addition to monitoring layout compliance in real-time with no need for physical audits, store managers can use video analytics to spot maintenance issues, identify stockouts, and validate if compliance violations are fixed properly.

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About the author

Steve Womer

SVP, Engineering

Steve has a passion for simplifying the complex. He has been designing and supporting secure network infrastructure solutions for distributed enterprise brands for the past 17 years. His current mission at Interface Security Systems is to ensure customer solutions are built with the highest levels of security and performance with an overarching theme of standardization and scalability. 

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Retail Video Analytics – 6 Insights to Transform Operations

Steve Womer — Mon, 02 Jan 2023 13:31:09 +0000

Retail

Steve Womer

SVP, Engineering

Business Intelligence Retail Video Analytics

Retail Video Analytics – 6 Insights to Transform Operations

Introduction

What is Retail Video Analytics?

Retail chains leverage data from a variety of sources to identify operational gaps. Rapid strides made in technologies such as computer vision and artificial intelligence now allow retail chains to uncover meaningful business insights from cameras originally deployed for securing the stores. These developments have now spawned an entirely new class of enterprise applications called retail video analytics or video-based retail business intelligence.

Retail video analytics can uncover hidden opportunities to improve all aspects of store operations such as:

Customer behavior and store design
Merchandising
Marketing campaign performance
Customer service
Security or loss prevention
Compliance, health, and safety

In this blog post, learn about the key challenges addressed by retail video analytics and the insights it offers to improve sales, deliver better customer service, and reduce operational costs.

Why video analytics is a game-changer for retail chains?

Retail chains are facing major challenges in maintaining operating margins as consumer preferences have changed dramatically and there is a heightened awareness about health and safety.

The economic impact of the pandemic and the ever-present threat of lockdowns has forced retail chains to look for solutions that can help them rapidly identify changes in consumer behavior, spot operational gaps in real-time, and fuse data from multiple data sources to take decisions proactively. Retail video analytics offers all of these capabilities and more.

There are two primary reasons retail chains should consider implementing an intelligent video analytics solution.

1. Graduate from descriptive analytics to predictive analytics

Analyzing historical retail performance and operations data from data sources such as inventory management systems, Point of Sale (POS) systems, people counting devices are the foundation of what Gartner calls “descriptive analytics”.

Descriptive analytics is all about understanding what happened in the past. However, relying only on descriptive analytics may not be enough especially in a world where brand loyalty is fast eroding and customers have very little tolerance for:

Out-of-stocks, pricing inconsistencies, and “store friction”
Inability to offer the convenience of Buy Online, Pick-Up in Store (BOPIS)
Inaccurate fulfillment and gaps in customer service

With video analytics, retail chains can graduate to predictive analytics to get answers for questions such as:

What should be the ideal staff size for a store on a Saturday evening between 5 pm to 8 pm?
Which display unit generates more sales for a product at a location and how does it vary across locations?
What product should a store stock more than usual on a sunny day at a specific location?

2. Go beyond loss prevention to gain a competitive advantage

Retail chains are now forced to invest a lot of money in new and untested technologies to keep pace with the rapid changes in consumer preferences.

Investments in new fulfillment centers, customer delivery and returns management solutions, to name a few, are eroding margins and forcing retail chains to justify the investments or keep a close tab on ROI.

The first wave of video analytics solutions focused on delivering smart or intelligent video data interpretation solutions for a proactive approach to loss prevention and security such as:

Locating suspects in a crowd of shoppers
Tracking the movement of specific items or high-value inventory
Validating employees via facial recognition to allow access to secure areas

Retail video analytics have now moved beyond just video surveillance and security to provide data insights relevant for almost all retail departments. Retail chains can now justify the cost of implementing a sophisticated video analytics solution as the benefits from the data insights can boost store performance across the board. That’s not all. Video analytics applications are available as bolt-on solutions that leverage existing security camera infrastructure that most retail chains already have in place thereby simplifying implementation to a large extent.

How does retail video analytics work?

A combination of video data and visual maps can then be merged into another set of variables such as product SKUs, sales, store geo-location, date and time of the day, in-store marketing campaigns or promotions, lighting, signage, temperature, access to product, product placement and display types, store layout changes or design updates to yield insights on customer behavior inside the store and how store design and other variables play a role in driving sales or any desired customer behavior.

Here is an overview of the top 6 applications of retail video analytics that will allow retail chains to tackle some of the biggest operational challenges facing them.

1. Use customer behavior visualization to optimize store design

Retail chains with a successful e-commerce presence use a variety of software applications to visualize online site traffic to track average time spent on a page, bounce rates, exit rates, shopping cart abandonment, and conversions. Visualizing online site visitor heatmaps, A/B testing of page content, call to action, and other digital marketing techniques are widely used by most e-commerce sites.

Retail video analytics brings the same set of techniques that e-commerce sites use to the physical store.

A store equipped with a 360° ceiling-mounted camera, with a clear line of sight to every corner of the store, can generate valuable data to answer questions such as:

How far do customers go into the store?
How many customers directly go to the register or the service kiosk?
How many customers go to a specific product section or aisle where there are products related to a marketing campaign?
What are the dwell times at various spots within the store?
Which are the location and product display hotspots that draw customer attention?
What are the “dark” areas of the store that customers choose to ignore?
How many people stood in front of a particular product?

An overhead camera with visibility into the entire store shows how far people go into the store to identify high-traffic areas and parts of the store that get little or no store traffic. This insight can be a valuable input for improving store design or A/B testing new design elements in the store.

Video analytics can be used by retail store designers to:

Test and create new floor plans that improve customer experience and maximize sales
Utilize key spaces with the store and minimize dead spaces
Understand how design changes can impact sales and dwell time in adjacent spaces
Decide on product placement by category that helps the customer find what they want quickly
Diagnose issues in lighting, temperature, and signage that might result in poor conversion

In summary, retail chains can use video analytics to make critical updates to store designs that directly influence customer behavior and run fine-tuned experiments in a small set of stores before rolling the changes across the board.

2. Speed of customer service

Delivering fast customer service and efficient queue management at checkout is a major challenge for retail chains. Firstly, the high volume of customers during peak hours or seasons can lead to long queues and delays. Secondly, limited checkout counters can exacerbate the problem. Opening additional counters is not always feasible due to space constraints or budget limitations.

Other challenges include staffing issues, where insufficient or untrained staff can hinder customer service. Complex product or service inquiries can also slow down the process if staff members lack the necessary knowledge or experience. Additionally, an inefficient store layout can contribute to longer waiting times and confusion among customers. Technological issues, such as outdated systems or glitches, further impede service speed.

With video analytics, retail operations teams can monitor queues at the checkout counters to gain real-time insights into queue length, waiting times, and service efficiency. This information helps retailers optimize staffing levels, open additional checkout counters when needed, promote self-service or app-based payment, optimize product mix at the store, and train employees to serve customers faster.

Video analytics can offer insights into some of the critical questions that determine the speed of customer service:

How long do customers typically wait in queues before being served?
What are the peak hours or periods when queues are longest?
Are there specific areas or checkout counters where queues tend to form more frequently?
How many staff members are needed during different times of the day to ensure efficient service?
Are there any bottlenecks or areas in the store layout that slow down the customer service process?
What is the average time it takes for a customer to complete a transaction at a specific checkout counter?
Are there any patterns or trends in wait times based on factors such as day of the week or product category?
How do different staff members or shifts compare in terms of speed and efficiency?
Are there any particular service areas where customers experience longer wait times or delays?
Are there any instances of abandoned queues or customers leaving due to excessive wait times?

Using video analytics retail chains can deep dive into customer service points of friction at every store by time of day, day of the week, and season. Issues with technology (POS screen loading times), training deficiencies, and poor design at checkout can easily surface via drill-down reports.

Retail chains can improve the speed of service at the checkout and in the curbside area using video analytics. Some of the benefits include:

Redesign checkout experience to address points of friction
Align your merchandising and promotional strategy to eliminate purchase complexity
Deliver employee/cashier training to address minimize billing errors and improve customer communication
Optimize shift management to align closely with variations in customer footfalls

In summary, the ability to serve customers faster improves by gaining insights from video analytics can help retail chains improve customer experience and maximize sales.

3. Improve in-store merchandising to drive sales

Maximizing shelf space usage, ensuring every store stocks just the right quantity of products, and choosing the right product mix across product categories to maximize sales are among the most valuable goals for retail merchandising teams. While planograms, merchandising software, and visual audits can get the job done, the speed with which display units can be tested or standardized marketing campaigns can be rolled out at scale continues to be hampered by the lack of real-time data and the inability to take instant corrective actions.

A planogram provides a detailed visual view of the store and is used by the merchandising departments to implement the best possible placement of products on the shelves to maximize sales. Credits: Ejemplo Planograma Textil CC BY-SA 4.0

With retail video analytics, merchandising teams can conclusively evaluate the effectiveness of merchandising strategies and rapidly change tactical implementation at the store-level. Video analytics can shed light on a wide range of valuable insights such as:

How are seasonal sets or floor plan changes impacting customer experience and sales/shopper?
What can be learned about the floor planning process that can be leveraged in future seasons?
How are consumers interacting with the product at an end cap or shelf?
What’s a product display performance in terms of its ability to draw people, sell products, and maximize space usage?
How effective is the new window signage or window display design?
What is the extent of planogram compliance across all the stores?
How are consumers engaging with ads or in-store displays within the store

Retailers can rapidly test the effectiveness of merchandising decisions at a small scale, roll it out across stores, and visually validate compliance.

Retail chains can rapidly zero in on instances of store associates not following merchandising or even re-stocking protocols. For example, video analytics can validate the impact of how restocking/unboxing during store hours can affect customer behavior, product engagement and sales.

Retail chains can boost the productivity of merchandising teams using video analytics. Some of the benefits include:

Align your merchandising strategy closely with customer preferences through video-verified data insights
A/B test every fixture and display type to determine the best option
Get instant alerts when shelves are out of stock with visual validation
Change display tactics in real-time based on date, time, season, or any other event
Do away with manual verification of display guidelines
Get space vs sales data for every display type within the store or across locations

In summary, the ability to keep a tab on how your products are displayed, stocked, and sold using video analytics can help retail chains keep costs under control and maximize sales.

4. Measure online & in-store marketing campaign performance

Rapidly changing consumer behavior and preferences are leading retail chains to overhaul their online marketing campaigns and In-store marketing tactics.

As footfalls nose-dived during the COVID-19 pandemic and retailers retooled to pivot to keep pace with the emergence of e-commerce or BOPIS models, marketing teams in retail chains have discovered that well-established planning practices that are based on historical consumer preferences and sales data have lost their value.

According to Gartner, nearly 30% of marketing leaders believe lack of agility and flexibility negatively impacted marketing execution during the COVID-19 pandemic. The Gartner survey also concluded that marketers are under immense pressure to deliver insights faster than ever before.

Retail video analytics not only gives an accurate, real-time view of how consumer preferences are changing but also offers marketing teams the capability to execute marketing programs with a high degree of confidence even when the world around is changing rapidly.

Specifically, marketing teams can leverage video analytics to answer critical questions such as:

How many people are walking by and not entering the store?
Did the online marketing campaign drive incremental traffic and how did the store perform?
How can new traffic patterns be used as an effective marketing tool?
As traffic flows are there unanticipated changes in adjacent zones?
What stores in certain geographies are outperforming stores in other regions?
Why did the upsell or cross-sell campaign fail?
Did all the stores comply with the display guidelines specified by the brand?
Did the in-store event or sample distribution succeed in generating interest and how much of the sales can be attributed to the in-store event?

With retail video analytics, marketing teams can:

Gather data about the performance of in-store displays for planning and post-rollout analysis
Analyze customer dwell times and hotspots on specific shelves/display units connected to the marketing campaign
A/B test displays, audio announcements, lighting, and ‘experiential’ kiosks and roll out changes rapidly
Visualize sales funnels for every marketing campaign at the store-level and map the data to online promotions
Monitor implementation of agile in-store campaigns based on real-time trends such as local events or weather

One of the ways retail video analytics can give marketers the agility they need to separate insights from noise is by generating a real-time sales funnel report that compares conversion data from online to in-store walk-ins, dwell-time on the relevant product aisle to the sale.

Video analytics allows retailers to zoom in on the performance of in-store displays that may be a part of marketing campaigns. In this image,you can see product activity hotspots corresponding to products on a table-top display unit. Data associated with the product activity hotspots can be used to determine the popularity of products and also correlated with sales data.

The sales funnel data at a regional level can be mapped to unique events such as easing of lockdown restrictions in a state that drove up sales thereby giving a realistic insight to help marketing teams make better decisions.

In summary, video analytics can be used by retail marketing teams to confidently make decisions and measure the impact of marketing campaigns with a high degree of accuracy.

5. Energize retail operations

E-commerce and analytics teams are obsessed with improving the efficiency of their operations and rely on data to guide them in their battle to cut costs and get customers to buy more.Online platforms, like Amazon, have the benefit of a technology-first approach to run their operations and they may have completely bypassed the challenges faced by traditional retail chains that may be relying on legacy software, network, and hardware infrastructure.

In addition to data collection challenges from legacy applications, ensuring customer data privacy, health and safety of customers as well as employees are high priority challenges for brick and mortar retail chains.

Retail video analytics can help level the playing field for retail chains looking to streamline operations and offer a better in-store customer experience. With video analytics, retail operations teams can get insights on key questions such as:

What is the optimal staffing level by employee type by department and by day of the week or hour of the day?
What trends can be seen in dwell times at the checkout counter and how does this align with staffing levels?
Are the store displays and shelves stocked as per the merchandising guidelines?
Are the store personnel complying with the safety and hygiene guidelines?
Which products are out of stock and how quickly are they being refilled?
What’s the average time taken by a store to locate and fix a potentially hazardous spill or debris on the aisle?

Retail video analytics can provide reliable data on footfalls for the entire store or for a specific department by date and time. In this screen, real-time footfall count (highlighted in blue) is compared with expected traffic (highlighted in grey).

Sophisticated video analytics platforms can not only count people inside the store, but also detect if people are wearing a face mask, predict where customers may need help or when inventory is likely to run low, and create alerts when additional manpower may be needed at the checkout counters. All these capabilities can be “turned on” using the IT infrastructure that may already be available at the store.

Here are some of the ways retail operations can get a boost with video analytics:

Eliminate or minimize the need for on-site store audits for merchandising or operations compliance using automated inspection and alerts for all points of interest within the store.
Empower store teams to take localized decisions on merchandising improvements, product promotions or inventory management to align with customer demand or preferences.
Trigger automated audio announcements when customers or employees are not wearing face masks or not observing social distancing inside the store.
Finetune staffing schedules or change shift hours to completely align with predicted customer footfalls to optimize costs and improve customer experience.
Automatically detect misplaced items on shelves and shelves that may be running out of products and alert store associates.

In summary, video analytics provides real-time operational awareness to store employees and gives traditional retail chains the ability to integrate data sources to gain actionable insights.

6. Streamline loss prevention and security

The concept of using video surveillance became popular among retail chains way back in the 1970s and ever since video cameras have been playing a key role in securing inventory, customers, and employees.

With the advent of “intelligent” digital cameras with edge processing capabilities and network video recording, video surveillance has become more autonomous and dependence on human monitoring has come down to assessing exceptions as opposed to continuous monitoring.

However, better cameras alone don’t guarantee a reduction in fraud or shrink. According to the 2020 National Retail Security Survey, retail loss prevention leaders felt in-store fraud has seen the most increase.

Retail video analytics can help loss prevention teams get more out of their limited manpower and budget by focusing on anomalies that indicate deviations from established protocols, internal shrink, shoplifting or fraudulent activities. With video analytics, loss prevention teams can gain a better understanding of these critical questions:

Are store associates following store opening and closing protocols?
Are employees handling cash movement following cash custody protocols?
Are perishable items stored and handled as per the standards?
Is a customer concealing an item in the backpack or hiding it under clothing?
Are the refunds, coupons, and voided transactions legitimate?
Is there a way to be alerted when banned individuals and customers with a history of shoplifting enter the store?

While video analytics can deliver insights in real time that’s usually not available with legacy data sources, the fusion of video data with POS systems, intrusion alarm systems, and access control systems can give loss prevention teams multidimensional information on specific events of interest. For example, a voided transaction recorded by a POS system can be mapped to a time-stamped video snippet to capture the event for validation.

Some of the video analytics software applications come with an integrated video management capability that allows loss prevention teams to quickly locate the video recording and securely share it via password-protected links with team members and law enforcement greatly reducing the time taken to investigate and gather evidence.

Here are some of the ways loss prevention teams can use video analytics to ramp up productivity and get more out of the camera network:

Monitor customer activity and movement of high-value products by designating areas of interests or virtual tripwires
Easily search and locate tagged video recordings by product category, transaction, employee, store location, date or time
Automatically track the movement of employees in high-security areas of the store with the ability to validate access via the access control system
Configure alerts for loitering, suspicious activity, shelf sweep attempts
Detect sweethearting at the checkout counter by avoiding scanning the item, covering the barcode deliberately or by stacking one time on top of the other
Detect instances of price-lookup abuse and directly bagging the item without using the bag weight scale at the self-checkout counters
Cut down incidents of false alarms and avoid false alarm penalties by combining video data with alarm events

In summary, loss prevention teams can leverage retail video analytics to get more done even when operating with sub-optimal staffing levels with increased scrutiny on budgetary allocations. With security cameras playing a central role in uncovering operational insights for all departments, loss prevention teams can maximize ROI for the business.

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About the author

Steve Womer

SVP, Engineering

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navin@cyces.co — Wed, 14 Dec 2022 19:34:57 +0000

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Introduction

Explore some of the key insights that network and security leaders should know as they transform their IT infrastructure to embrace new ways of doing business.

Interface’s network services partner, Fortinet, and Canam conducted a survey among network and security professionals in the retail industry to understand how the COVID-19 pandemic has changed consumer preferences and how these changes have created new network and security challenges.

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About the author

Don Fruhwirth

Director of Product Management

Don Fruhwirth is Director of Product Management at Interface Security Systems. He brings more than 20 years of expertise in security integration strategies, next-generation networking design and wireless system architecture. Don has enabled numerous enterprises in transforming their IT and asset protection system infrastructures. Don is an industry thought leader and frequent speaker on topics such as SD-WAN, cloud video architectures, remote video surveillance and advanced network security. Prior to joining Interface, Don has held senior solution engineering positions for regional security and global cellular telecommunications companies. In addition to his security background, Don holds advanced networking certifications from Cisco, Fortinet and Cradlepoint.

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Making IT Happen – The Interface Blog for Distributed Enterprises

Michael Shaw — Sun, 11 Dec 2022 13:27:23 +0000

Retail

Steve Womer

SVP, Engineering

Restaurants Retail

Making IT Happen – The Interface Blog for Distributed Enterprises

Challenging Business Environment

Distributed, consumer-facing enterprises are grappling with an uncertain economic landscape buffeted by a once in a generation health crisis.

We thought this is the right time to launch Interface’s blog – Making IT Happen, to bring in clarity and help our customers navigate this uncertainty. All these challenges translate into two critical questions for enterprise loss prevention and IT leaders: How do we secure our people, customers, inventory and assets? How can we upgrade our connectivity and collaboration platforms to adapt to changes in our operating business model? These questions are particularly difficult for our restaurant, retail and hospitality customers who are already going through significant changes to their business models.

These questions are particularly difficult for our restaurant, retail and hospitality customers who are already going through significant changes to their business models.

Retailers who are closing down are facing the need to increase security for their vacant premises. Others, such as grocery stores, are finding it hard to get staff. Some customers are already adapting to the situation by changing their business models — offering such services as curbside pickup, home delivery, and more.

As necessity is certainly the mother of invention, these changes are requiring our customers to think differently about their security and networking needs and adopt new business models.

For instance, those who are offering curbside pickup must now also widen the areas they monitor, secure new areas, and observe comings and goings (a remote monitoring option would be a great choice in these scenarios). In addition, enterprise employees are being asked, where it’s feasible, to work remotely and collaborate using online applications. IT teams are having to make sure that employees have secure remote access to all their corporate applications, that their voice applications and help desk services are seamlessly ported over to work from anywhere.

It’s these scenarios and more that drive Interface Security Systems to be the very best at what we do.

We work closely with our customers in the retail, hospitality, restaurant, and financial markets to help them combat various networking and security challenges and loss on a daily basis. In fact, Interface offers Managed Network, VoIP, Asset Protection, and Business Intelligence solutions that maximize our customers’ ROI. It’s something that we’re good at — we’ve been doing it for more than 25 years.

Business Voice Over IP for Better Collaboration and WFH

If an enterprise has not yet converted to Voice over IP, it should certainly look into doing so. VoIP solutions deliver simple, powerful and cost-effective calling services for distributed enterprises.

Most of the business VoIP solutions include features such as unlimited local calling, free long distance on network, four-digit enterprise-wide dialing and voicemail and ‘find-me-follow-me’ to make it easy to be contacted no matter where you are physically.

For essential businesses, having a cloud cloud-based auto attendant that comes with VoIP solutions can be game-changing. The value here is that when someone calls a store or business, the phone is not actually ringing at the location. It’s ringing a cloud-based auto attendant which presents the caller with clear options from there.

For instance, if the caller is looking for directions or for business hours, there’s no need for the call to be transferred to the store or business location. Those kinds of questions can be answered with a recording. If the customer really wants to speak directly with someone at the location, there’s an option for them to transfer.

With this solution , businesses can reduce between 50 to 60 percent of the call volume for employees and free their time to be used for other important tasks.. And while there is some management involved, such as updating information like store or business hours, the customers that we’ve deployed it for absolutely love it, especially when they’ve asked us to manage and maintain the information as it changes throughout the year.

Customers also appreciate the ability to define ring groups and call flows that can be used to dial specific phones in a specific order or in response to different conditions. If no one is available to take the call in a store, for example, after a certain number of rings, the call can automatically be routed to a call center to be handled by the next available agent. Or it could be routed to a cell phone. Or to another store.

There are many possibilities.

Additionally, there are business continuity/disaster recovery options for VoIP that, in the event of a disaster, can reroute calls from one location to another all from a cloud-based console.

What’s Ahead for Essential Businesses

There are many other interesting emerging technologies on the horizon. Some we haven’t covered yet but look forward to announcing soon such as cameras that can detect people with fevers and cloud-based mobile text messaging for handling customer support at scale. We already have some of these in our labs being studied, tested and assessed.

In this blog post, we’ve looked at a number of technologies that have been making serious inroads in various businesses and are currently being deployed. Many are directly making a difference in our current climate.

A managed service provider such as Interface, with years of deep networking and security expertise, can help business, IT and security professionals make the right choices when deciding which technologies are ripe for deployment. We then work closely with them to design, integrate and support custom solutions to meet their unique challenges.

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Steve Womer

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Steve Womer has experience designing and deploying WAN/LAN infrastructure for distributed enterprise clients since 2008 and has served in various engineering, sales engineering, and operational roles for industry leading managed services providers.

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