Is hyper-personalisation possible in quick e-commerce with the help of AI?

AI is transforming retail sectors like beauty, fashion, and luxury by enhancing customer engagement and personalisation. From virtual makeup try-ons to dynamic pricing in travel, AI-driven innovations are improving user experiences and operational efficiency. The article explores how AI's predictive analytics and cross-sector collaborations are revolutionising various industries.

AI is revolutionising how retailers engage with customers, streamline operations, and drive growth. This shift is particularly evident in sectors such as personal care, beauty, fashion, and luxury, where AI enhances customer satisfaction and boosts revenue. By improving personalised shopping experiences and refining dynamic pricing models, AI opens new avenues for retailers to deliver more tailored and impactful interactions.

Elevating personalisation in beauty and luxury retail

In the beauty and luxury sectors, AI-powered personalisation has transformed the shopping experience, offering tailored recommendations and interactive content such as makeup tutorials and influencer-driven fashion reviews. For example, AI-driven virtual makeup try-ons allow customers to see how products will look on them before purchasing, and some AI-powered skincare diagnostics provide personalised skincare routines based on individual skin profiles. This fusion of discovery and entertainment creates an immersive journey, building loyal communities. 

Leveraging voice tech

Similarly, voice commerce enhances this personalised experience by allowing customers to engage with brands hands-free through voice assistants. Luxury brands suggest products based on previous purchases, offer personalised beauty routines, or provide real-time updates on exclusive launches. By integrating voice search and personalised services, these brands elevate customer engagement, ensuring a seamless, highly tailored shopping experience that mirrors the exclusivity and convenience expected in beauty and luxury retail. AI’s ability to understand and anticipate customer needs has become crucial, particularly in beauty e-commerce, where personalisation drives almost 80% of repeat purchases.

Smarter dynamic pricing strategies in travel sector

AI is not only enhancing personalisation but also optimising pricing strategies by analysing customer behaviour and price sensitivity. Dynamic pricing on travel ticket booking platforms uses advanced algorithms to adjust fares based on factors such as train routes, booking history, ticket value, phone type, and the time of day. This ensures frequent users get the best prices quickly by analysing their preferences and travel patterns. This smarter pricing strategy delivers convenience and personalised affordability in real-time.

Enhancing customer service with AI

Enhancing customer support with AI allows platforms to address customer dissatisfaction proactively. AI plays a crucial role in multilingual customer service, where chatbots can seamlessly switch languages based on the customer’s input, enhancing user experience. For example, if an AI model detects a customer’s negative experience during a previous conversation—through analysing tone, sentiment, or language—it can flag the issue for the support team. The next day, the team could follow up with the customer via a personalised call to address their concerns directly. 

Harnessing legacy data for predictive analytics

AI leverages legacy data to refine predictions and improve customer interactions over time, learning from past behaviours and preferences. For example, brands use data accumulated over the past decade, including details like customer medical history, to tailor insurance recommendations. This allows the platform to identify where higher margins are likely and where lower margins might occur, optimising sales strategies. While storing legacy data for such analytics was costly, advancements over the past 15 years have significantly reduced storage costs, making it more feasible to harness long-term data for accurate and personalised predictions. 

Cross-sector collaboration and AI’s role

AI’s influence extends beyond individual businesses, fostering collaboration across different sectors. For instance, customers often need furniture and other home essentials after purchasing a house. By collaborating across sectors, such as real estate, furniture retailers, and logistics providers, businesses can create a seamless experience for the customer. AI can facilitate this by analysing data from different industries to predict customer needs and streamline the supply chain. For example, real estate companies can partner with furniture retailers and logistics firms to offer integrated services, ensuring that the furniture delivery is synchronised with the move-in schedule. 

Harnessing AI to its full potential

In the rapidly evolving world of retail and e-commerce, embracing AI technology offers significant opportunities for enhancement.

• Embrace Value Chain Collaboration in Non compete Industries: Integrating the latest AI innovations, such as deep tech and facial recognition, through collaborations with external startups, can significantly enhance the customer experience. This approach not only provides fresh, innovative solutions but also positions companies at the forefront of technological advancement.
• Ensure data accuracy: Maintaining accurate and up-to-date learning datasets is crucial. Adapting to changes in data schemas ensures that AI models remain relevant and effective, leading to more precise insights and predictions.
• Leverage reliable data sources: Sourcing data directly from trusted and authoritative institutions is essential for health tech platforms. This practice guarantees high accuracy and reliability, which is vital for achieving effective AI-driven outcomes.
• Optimise sales strategies: Analysing call timing can reveal important patterns, such as the higher urgency of evening calls. Prioritising these interactions can improve conversion rates and boost the effectiveness of sales strategies.
• Accurate prediction of customer ordering behaviour in Geographic Cluster to formulate the warehousing and SKU decision.

Focusing on these key areas can help businesses harness the full potential of AI, driving improved customer engagement, better data management, and enhanced sales performance.

This article first appeared in ET Retail.com on 23 September 2024.