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Retail Video Analytics, Wow Factor In Enhancing Customer Experience

Video analytics can put the retail stores on par with the online marketplaces, increasing their competitive advantage as well as continuing to attract footfalls and building a loyal customer base.

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With Covid-19 compelling lockdowns and customers getting habituated to shopping online, retail stores will have an uphill task to make personal shopping attractive. E-commerce sites and online marketplaces have created high customer expectations, be it product choice, pricing, or service. Digital technologies have enabled these marketplaces to identify customers from the time they log in to check out by tracking the sections they visit and the products they select and using recommendation engines to suggest related products for cross-selling or alternatives for upselling.

With more and more Brick-and-mortar stores facing the challenge of retail shrinkages, as losses caused by theft, fraud, scanning errors, and other related causes, retailers are looking towards digital technologies to overcome these errors. These challenges have compelled the stores to look to digital technologies such as AI/ML, data analytics and video analytics to improve their services, prevent thefts and increase their profits.

Though shopping in a store can also provide customers with a personalized experience, shop floor assistants do not always know their customers and their preferences well and do not have access to real-time data that can help them serve their customers with as much efficiency as an online store may be able to. With digital technologies, they can anticipate customer needs, cross-sell and upsell products, analyze traffic patterns to create targeted promotions and overall improve customer delight.

Video Analytics - Benefits for Retail

They can use data analytics and AI technology to create customized promotions and pricing to entice customers to back and build a loyal base.

  • For real-world businesses, this level of personalization and upselling can be a challenge. However, with most outlets deploying CCTVs, video analytics or computer vision technology is the logical next step that is bringing efficiencies in more ways than one. Retailers can identify customer traffic trends in their store, the hot spots that see more footfalls and the cold sections where it is less or sporadic. Heat maps can be generated to understand customers’ behavior in the store, their preferences, identify patterns and create appropriate campaigns to improve engagement.
  • This can also help in understanding:
  • Customer traffic trend and augment staff resources during peak periods to improve customer service
  • Video analytics also can help improve queue management at the POS areas
  • Improve in-store experience for customers.
  • The performance of the salespeople can also be monitored, and appropriate training provided to ensure top-notch service in the store. Empowering the salespeople with information about the customers can help improve conversion from interest to purchase.

How Does Video Analytics Work?

Most retail outlets already have one or more CCTV cameras installed for security purposes. However, in case of a theft, the footage must be reviewed patiently to find the culprit. It can be time-consuming and tedious. Also, these footages are used only in case of any such untoward incident and not for improving customer satisfaction. 

  • Digital technologies today enable us to leverage these videos to generate heat-maps and capture footfall patterns in the store by area or sections and the time of the day and analyze it for further decision making. 
  • A neural network model for image processing and video analytics can be built and trained for pattern identification. 
  • Facial recognition software can help understand who left the store without making a purchase, which sections they stopped by and why the conversion did not happen. 
  • In case of a successful sale, the analytics can help analyze what worked and how to build a loyal customer base based on their experience in the shop. 
  • Reports and dashboards with real-time data can facilitate capturing customer behavior and shelf zone performance.

With video analytics, brick-and-mortar stores can base their strategies on data rather than gut feel. While personalizing customer service, they can also improve the product mix in the store to improve sales. Promotional campaigns can be more targeted for greater effectiveness and employee performance enhanced by providing them with access to relevant customer-related information for better conversion. 

Video analytics can put the retail stores on par with the online marketplaces, increasing their competitive advantage as well as continuing to attract footfalls and building a loyal customer base. It will empower physical stores to prove that they mean business and cannot be dismissed lightly.

Some of the common use cases: A reflection of its increasing popularity is the phenomenal growth projected for the video analytics market at a CAGR of 24.5% from USD 1528.1 million in 2019 to USD 4142.7 million by 2025. 

Walmart, for instance, was able to leverage computer vision technology in its more than 1,000 stores to spot errors in scanning at the time of checkout as well as prevent theft using image recognition cameras. Kroger, another large retailer, was able to personalize the shopping experience for its customers and improve associate productivity by using video analytics. Amazon Go’s checkout-free shopping outlets thrive on an array of cameras and sensors that help track customer movements and facilitate billing of the products selected by them automatically. 

Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house. Unless otherwise noted, the author is writing in his/her personal capacity. They are not intended and should not be thought to represent official ideas, attitudes, or policies of any agency or institution.


Tags assigned to this article:
Retail Video Analytics retail customers data analytics

Mr. Ashish Kumar

Principal Data Scientist, teX.ai, Indium Software

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