Drive Retail Impact This Holiday Season By Deriving Video Intelligence

0aaaStephanie Weagle BriefCam

How Retailers Can Prepare For The Holiday Season With Video Content Analytics

Retailers adopting data-driven approaches to business operations and management seek ways to maximize existing processes based on intelligence — especially ahead of the holiday season, when increased demand and traffic requires heightened situational awareness, visitor engagement and experience personalization. Business intelligence and the enabling technologies that drive productivity and generate data are critical for everyday operations, but even more so for exceptional time periods such as the holidays, when the stakes for meeting customer expectations are higher.

One such technology, video content analytics, empowers retailers to extract comprehensive intelligence from video surveillance footage. While retailers typically deploy video surveillance for security monitoring and post-event investigation, enhancing surveillance with video intelligence software grants merchants access to granular and valuable data, which can be leveraged in preparation for and throughout the holiday retail season.

Evaluating Past Footage To Inform Future Engagement

Retailers execute data-driven retail strategies in order to better understand and know their customers. Becoming familiar with the typical customer profile enables retailers to tailor their offerings to meet the needs and expectations of their particular audiences, while also exploring how other demographics could be better attracted and engaged by the brand.


Video content analysis empowers retailers to uncover shopper demographic data by analyzing footage; detecting, identifying and extracting all the objects that appear; and then cataloging and indexing the objects so that they can be searched, aggregated or applied for analytic capabilities, such as real-time alerting based on object detection. Based on Deep Learning and artificial intelligence techniques, video intelligence software enables retailers to visualize all the data about objects that have appeared in their store(s) into reports, heatmaps and graphs to drive intelligent decision making.

Beyond consumer demographics, ahead of the holiday, retailers can review data visualizations about visitor traffic flows in the store and understand how customers tend to navigate their space. Analyzing in-store traffic patterns is critical for identifying under-utilized space, the highest traffic areas and hours, and the pathways where bottlenecks and crowding tend to occur. In so doing, merchants can evaluate their floor plan to ensure optimal throughput even when higher traffic is expected.

This same data can also inform retailers about product and display popularity: By evaluating heatmaps that indicate where customers tend to dwell in-store, merchants can affirm which displays, promotions and products have been popular, and even cross-reference the video data with inventory and POS business intelligence, to understand whether heightened interest in the particular item translated into increased revenues and sales. More importantly, if there is no correlation between dwell data and sales data, the retailer can take the opportunity to understand and reconcile that disparity.

Analyzing Product Interactions And In-Store Traffic Flow

While it is beneficial to proactively review historic video intelligence ahead of the holidays to empower data-driven decision-making and strategizing, video content analysis can be equally as critical in real time. Retailers that review their daily traffic flows, dwelling patterns and product interaction insights throughout the holiday season can keep their fingers on the pulse and proactively engage with their customers.

Merchants also can leverage video intelligence solutions to configure and trigger real-time alerts for certain behaviors and objects. For instance, if crowding has been a challenge, retailers might want to set up count-based alerts and receive notifications any time a pre-defined number of people is detected in a specified space within a certain time period.

In this way, operators can be notified when a crowd might be growing, assess the video surveillance feeds and respond as needed in real time. This could be useful in a fitting room setting of a department store: When potential crowding is imminent as queues grow for trying on clothes, staff can be deployed to redirect shoppers to empty fitting rooms, preventing frustration and driving customer engagement.

Detecting Security Threats And Preventing Theft

While maintaining situational awareness is important for driving timely and relevant customer engagement, it is also crucial for enabling security. Public safety stakes are also much higher during the busy holiday season and the potential for security breaches and threats — not to mention theft — increases exponentially. By empowering security teams to set alerts for the behaviors and/or objects that might indicate a threat, personnel can carefully assess incidents as they unfold to respond more dynamically and immediately.

For example, alerts can be configured to trigger notifications when activity is detected after hours, at times when few people should be on-site. Loitering in parking lots or movement within the store in the middle of the night is suspicious, and instead of proactively monitoring video feeds — which is prone to human error, distraction and fatigue — store security can be notified when specific incidents require their immediate assessment and full attention.

During the day, it might be helpful to receive alerts for extended dwelling around high-value product displays or near restricted areas. By calling attention to lingering in sensitive areas, the video intelligence platform empowers security to make early, informed decisions when there is reason to suspect theft attempts.

Another major security challenge during the holiday season is crowding. Bottlenecks and congestion can lead to outbreaks between consumers, and crowd formation can also indicate a potential incident that is drawing attention. A medical emergency or an attempt to commit a crime, for instance, could attract a crowd, and the ability to automate notifications of these events can help security personnel react dynamically and proactively, tracking events as they occur.

For retailers that have identified suspects in previous shoplifting cases, face recognition-driven alerting can be a critical enabler in developing situations. Video analytics software empowers operators to match faces that appear in real-time surveillance against a pre-defined watchlist comprised of digital images of persons of interest. When face matches are detected, an alert is triggered and the operator can review more closely, confirm whether the detected face can be identified as the theft suspect and determine how to react — whether it means monitoring the person more closely or confronting the suspect in order to prevent future losses or deter additional crime.

Executing A Data-Driven Retail Holiday Strategy

Enhancing engagement, analyzing activity and streamlining security and loss prevention are details that retailers must address all year long, but which are even more significant during the holiday season. By integrating intelligent sensors and data-driven business methodologies, retailers can proactively prepare for the expected and unexpected during high traffic periods and everyday activity.


Stephanie Weagle is the Chief Marketing Officer at BriefCam®, the industry’s leading provider of VIDEO SYNOPSIS® and Deep Learning solutions for rapid video review and search, face recognition, real-time alerting and quantitative video insights. Weagle leads the company’s global marketing initiatives, accelerating market adoption of BriefCam’s comprehensive video analytics solutions. Before joining BriefCam, Weagle was Vice President of Marketing for Corero Network Security, where she led global marketing for the company’s cyber-threat mitigation product portfolio. Previously, she held senior marketing roles at Lionbridge Technologies and Novell, Inc.

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