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Transforming video content analytics into retail business intelligence

Stephanie Weagle, CMO, BriefCam, explains why the use of retail video analytics plays into understanding consumer behavior. Consumer attitudes, preferences and buying behaviors change frequently and merchants must adapt and innovate — or risk being left behind.

Photo by iStock.com

September 21, 2018

By Stephanie Weagle, CMO, BriefCam

Retailers have long trusted video to enable in-store surveillance for loss prevention, crime deterrence, and employee oversight. However, retailers know the value of surveillance footage is not limited to traditional security applications. With the introduction of intelligent video content analytics (VCA) solutions, surveillance footage has become an even greater asset for merchants. The use of retail video analytics originates from the proven principle of the retail industry — understanding consumer behavior. Consumer attitudes, preferences and buying behaviors change frequently and merchants must adapt and innovate — or risk being left behind.

Based on artificial intelligence, in-store video analytics introduce a new, critical layer of dimension in delivering actionable insights, enabling strategies that enhance the shopping experience, stimulate shopper retention and increase sales. By measuring hotspots, traffic flow, dwell time, and product display activity and comparing these trends over time, retailers are empowered to enhance store performance and operations in many meaningful ways.

Maximize store layout and navigation

For all retailers, store layout is a key factor to ensuring a successful shopping experience. By understanding how customers typically navigate through the space and how long they spend in certain locations, retailers can optimize product placement and in-store traffic flows to encourage sales.
Data-driven video business intelligence enables merchants to quickly see and distill down patterns of in-store behavior so that they can design a more productive store layout.

By responding to actual performance indicators that may have been difficult to identify without the advantage of video synopsis, you can maximize your floor space; identify underutilized and strategic, high-traffic spaces; better leverage your staff; and even A/B test different layouts – all based on actionable intelligence from in-store video surveillance content.

For example, as we approach the back to school shopping season, merchants can optimize in-store navigation to ensure zoning and display strategies are effective and better identify friction points. By testing layout and design, in-store opportunities are better understood by visualizing shopper behavior data through shopper paths, dwell time analysis, heat maps and buyer demographics. These datapoints can be analyzed and product placement can be optimized based on the data gleaned from video analytics.

Optimize promotions and product displays

New business intelligence derived through video content analysis can also be used to formulate effective promotions and display designs that encourage stronger engagement. By identifying trends in engagement with product displays, merchants can better correlate promotions, displays, and sales. It may even be possible to identify ways to attract customers with new cross merchandising ideas. With the capability to uncover the amount of time customers dwell near displays and correlate with purchase data, retailers could implement new conversion prompts to impact change — often simply mobilizing a staff member to personally engage the shopper can be enough to drive a sale. With AI-driven VCA technology, management can even customize real-time staff alerting to proactively intervene and encourage purchasing when customers linger at a display.

Manage store traffic

It is not enough to know who is in the store. Retailers must also know how those customers prefer to navigate it. The inability to accommodate the ebb and flow of shoppers will result in a compromised customer experience due to overcrowding and bottlenecks.

Leverage video analytics to understand in-store traffic patterns and peaks — when they happen and what causes them — to prevent friction in the path to purchase. Configure crowding alerts to proactively help guide traffic and engage customers — even before an influx of customers enters the shop. With video content analytics, retailers can detect traffic as early as when it begins forming in the parking lot and effectively react. Planning for predictable busy times and responding in real-time keeps customers engaged and moving through their path to purchase.

Streamline checkout

Ensuring efficiency at checkout is equally, if not more, important than all the customer engagement that leads up to it. Here too, video content analysis can help eliminate conversion barriers by preventing long lines from forming. Triggering real-time alerts when crowds start to form so that retailers can respond to increased traffic and deploy additional cashiers or EPOS stations before customers abandon their purchase. In the long term, retailers can leverage this business intelligence to assess whether self-checkout solutions are necessary to maintain a positive and efficient experience.

Analyze consumer demographics

Advances in video are now making it possible for brick-and-mortar merchants to better understand in-store consumers to personalize engagement and drive retention and sales.

Using machine learning technology, video content analytics solutions help quantify and classify individuals shopping in-store. Instead of speculating about customer demographics, retailers can uncover who they are attracting and better target customer experiences based on business intelligence.

Equally as important, management can use this data to discover how to improve engagement with other demographics to grow your customer base, support your merchandising strategy or determine new pop-up store locations.

While it is clear that AI has extended video’s utility beyond simply security and loss prevention, the new analytics can significantly maximize these activities as well. For example, retailers can configure real-time alerting to react to suspicious behavior, such as high dwell time in changing rooms, lingering objects carrying bags, and correlations of high speeds on certain pathways. They can even accelerate the identification of repeat offenders with appearance similarity and feature recognition, tracking suspects throughout shopping centers or finding perpetrators during post-event investigations.

By utilizing quantifiable demographics, customer volume analysis, and object interaction and tracking that aggregate data over time, retailers can see trends that identify anticipated results, and areas where improvements need to be made. By leveraging this extracted and aggregated video metadata, users can derive actionable insights, enabling data driven safety, security and operational decision making.

For all its uses, it is not surprising that retailers are looking at video content analytics technology as a core requirement for business intelligence, efficient operations, and performance optimization that will improve the overall business.