The rapid pace of innovation in the e-commerce sector propelled by the trend ‘bricks to clicks’ is increasingly shifting consumers to online shopping. A major disadvantage for offline retail stores is their lack of knowledge on customers entering their premises.
The rapid pace of innovation in the e-commerce sector propelled by the trend ‘bricks to clicks’ is increasingly shifting consumers to online shopping. A major disadvantage for offline retail stores is their lack of knowledge on customers entering their premises. Here, artificial intelligence (AI) opens up a big opportunity to predict the purchasing behaviour of in-store customers. AI through its sub-technologies such as machine learning and deep learning can enable offline retailers to derive actionable insights from consumer data (structured and unstructured) to offer predictive and precise decisions for better customer experience.
AI practices incorporated by global offline retailers
The global offline retail industry has been moving toward increased automation, cashless transactions and self-checkout stores based on consumer behaviour patterns, and demand for increased convenience.
App interface and chatbots: App-based applications running on AI enable numerous in-store benefits. For instance, retailers can offer personalised push notifications through beacon technology enabling targeted service. Increasingly, bricks-and-mortar retailers have implemented AI chatbots to serve customers just like an experienced shop assistant. For example, Macy’s store partnered with IBM’s Watson for an in-store shopping assistant called ‘Macy’s On Call’. The bot leverages natural language processing technique to answer customers’ questions related to products, services and facilities.
Inventory management: Offline retailers are greatly benefiting from AI in the area of inventory management. Often consumers in physical stores are challenged with information on product availability. By leveraging AI-based predictive analytics solutions, retailers have better visibility on inventory levels resulting in efficient operations.
Theft prevention: The traditional video cameras and other sensory devices have limitations in preventing theft. Retailers are implementing AI-based computer vision cameras to recognise shoplifters, leveraging information available from a central repository. All suspicious events are captured and alerted instantly.
Sales forecast: Machine learning-based solutions are being used by retailers to predict product performance and identify customer demand based on factors such as sales history, location, weather, promotions, etc. This technology shift is expected to provide a tangible positive impact on the shopping experience and allows retailers to garner better margins.
Personalisation: Leading brands such as Puma, Lacoste and Amazon are investing heavily to improve the personalisation experience for better customer engagement. Amazon, which is expanding into the bricks-and-mortar space through its Amazon Go model, leverages computer vision, deep learning algorithms and sensor fusion technologies to create a frictionless shopping experience by removing the time lag from payments. Alibaba’s FashionAI utilises machine learning algorithms to provide customers with recommendations related to clothes and accessories.
Indian offline retailers should focus on three key in-store retail aspects — enhance customer experiences, optimise store operations, and provide an efficient merchandising and supply chain system. Largely driven by evolving consumer requirements and intensifying retail competition from international entrants and new business models, Indian offline retailers also need to boost their technology investments. Companies investing heavily in technology, such as Reliance Retail and Future Group, are poised to gain competitive benefits in the Indian offline retail market.
By Anand S
The author is VP, Frost & Sullivan