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by Daniel Martinho-Corbishley, CEO and Co-founder, Aura Vision
Industry View from
Retailers are sparking a high-street revolution by using cutting-edge AI to understand customers, drive sales and protect customer privacy.
The rise of online shopping and the high street’s struggle to compete have both been facts of life for some time. However, the high street is far from finished – 91 per cent of global retail purchases are still made offline, and across the UK, more independent stores opened than closed in 2018.
This clearly indicates a continued demand for innovative shopping experiences, but the problem for retailers is knowing how to provide this. Until recently, it’s been difficult for offline retailers to compete with the detailed “shopper journey” that is key to online retail’s success.
Retailers around the world are expected to invest £160 billion in new technology solutions, up 3.6 per cent since 2018. In the UK alone, 39 per cent of the largest retailers, including Zara, H&M and Tesco, are already experimenting with AI to improve the customer experience and automate decision making.
A high-profile example of AI automation is that used by Amazon Go, entirely cashierless stores that let shoppers walk in, pick up their items, and walk out again, without ever stopping by the till.
To achieve this, each store is riddled with specialist cameras and shelf sensors – the hardware alone is said to have cost $1m per store, an expense that has proved to be one of the largest barriers to the adoption of this kind of technology. Despite this, retailers are becoming increasingly reliant on tracking shopper movements as they browse for products across the store, interact with sales assistants and queue for checkouts.
Next-generation retail establishments such as B8TA and Neighbourhood Goods have built their “Retail as a Service” models around collecting this type of information. Brands can now see how many shoppers interact with their products, for how long, and how this compares to the competition on display.
Aura Vision’s AI generates privacy protecting analytics in real-time, so video is never stored and shoppers are never identified (image of people from stock footage).
Until now, tracking consumers’ mobile phones through Wi-Fi or Bluetooth sensors has offered a solution to measuring repeat store visits and delivering targeted marketing messages.
However, a number of these solutions are coming under scrutiny, as they fail to protect the privacy of unaware shoppers. Some methods store personal data from visitors’ mobile phones without gaining prior consent, meaning they fall foul of recent GDPR legislation.
In the past, retailers had to choose between privacy-conscious solutions that could only capture unreliable data, or opting for more invasive but less consumer-friendly methods.
This calls for a shift to enable retailers to collect the quality information they need to succeed in an omnichannel world, without infringing on customer privacy or incurring high hardware installation costs.
Aura Vision’s technology can see which products receive the most interest from customers, without needing to track mobile phones or identity shoppers (image of people from stock footage).
Three key factors driving the next generation of retail analytics technologies
• Data quality – how accurately can the system count and track the behaviour of shoppers?
• Privacy – does the system protect shoppers’ privacy and comply with GDPR?
• Scalability – is the system cost-effective and can it be scaled to hundreds of stores?
With the proliferation of surveillance CCTV cameras, and a recent surge in AI techniques like Deep Learning and Computer Vision, there is now a better way to track in-store shopping behaviour.
Aura Vision uses cutting-edge video analytics that piggy-back off existing camera infrastructure to minimise installation cost, and provide retailers with the data-edge they need to compete.
Global sportswear brand ASICS just announces its backing of Aura Vision and considers adopting the technology in its US stores.
Without storing personal data, retailers can optimise staff scheduling based on demand in different areas of the store, and learn which customer demographics are most likely to purchase a particular product. All of which can be scaled cost-effectively to every inch across hundreds and even thousands of stores per brand. To find out more visit auravision.ai.
Video analytics can help retailers to:
• Better understand their customer demographics and their needs
• Increase entries to the store by A/B testing window displays
• Increase dwell time by rearranging products that gain most engagement times
• Optimise staffing by knowing when and where demand is high or low
• Increase sales by stocking products to target the correct customer demographics
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