To stay ahead of the industry curve in a post-pandemic world, retailers have been accelerating their adoption of technology. It’s the right move in adapting to the evolving competition and consumer behaviour. But retailers also need to integrate various features of technology innovation, data-driven and AI/ML-adoption into their business models in more defined and sharper ways. Now is the time for retailers to shift their focus from the relevance of stores to profitable retail by embracing AI tools across the omnichannel retail value chain.
AI can be applied seamlessly across physical and digital stores and AI in retail involves the use of automation and data analytics to deliver highly personalized shopping experiences to customers. By deploying AI-powered algorithms, retailers can track and capture in-store as well as virtual store data. AI will allow them to execute targeted in-store branding and marketing campaigns based on customers’ preferences, historical basket sizes, spending patterns, location, etc. Additionally, it will help them improve customer loyalty and retention with heightened personalization.
Let’s look at some of the most exciting applications of AI in retail:
* Contactless checkouts: Contactless checkouts make the buying journey shorter and easier for customers, thereby increasing in-store customer satisfaction. A contactless or cashier-less checkout combines the use of cameras, IoT sensors, and computer-vision systems to detect customer interactions, monitor the movement of products, and automatically detect prices. It helps reduce retailers’ operational costs and deliver enhanced shopping experiences to digitally-native consumers.
* Inventory management: Deploying AI in inventory management processes can optimize the supply chain, pricing, and promotional planning. Computer vision is deployed to track items on shelves and carry out a 360-degree inventory scan. Automating inventory management will provide retailers with out-of-stock notifications, boost the store layout, produce heat maps, and forecast the on-shelf time of a product. The benefit here is that AI assists the human workforce and helps bring back products on the shelves as soon as possible.
* Customer behaviour analysis: AI-powered retail analytics solutions captures real-time store data and track customer behaviour across offline and online channels. Using predictive analytics, retailers can build propensity models to conduct forecasting and predict customers’ purchase patterns. This will allow retailers to deliver relevant communication, and provide personalized discounts and offers to initiate a purchase. Brands can also deploy retail analytics in visual merchandising and store audits to identify operational gaps and improve in-store experiences.
* Auditing SKU placement and planograms: Traditionally, performing an audit of the retail shelf has been a manual and time-consuming process. Moreover, it was never fully accurate, and error rates were high. Therefore, the planogram management- which defines where products must be located and displayed in-store- has been an imperfect process. This is where automated image detection and object-detection technologies come in. Together they automate the process from checking prices to identifying products’ locations. The upshot here is that retailers get a unified view of their shelf conditions and make necessary amendments to in-store layout. Essentially, AI software will offer two critical KPIs to retailers: planogram compliance and on-floor availability.
Conclusion
Implementing AI/ML-powered technologies to streamline operations and cut operational overheads is a necessity in the age of labour shortages and e-commerce competition. AI is also critical to delivering personalized and immersive shopping experiences from shelf to checkout, track customers’ interactions and strengthen in-store branding.
— Sanjay Kumar
