In today’s competitive retail environment, brands must adapt to the rising expectations of their customers. Recent data from BigCommerce’s 2024 Global Ecommerce Report reveals that fashion and apparel brands globally experienced an impressive increase of 10.7 percent in gross merchandise value (GMV) from Q1 2023 to Q1 2024, partially driven by a 7.2 percent hike in orders. This surge is attributed to an average order value (AOV) rise from $160 to $165 within the same period. In light of these encouraging trends, the integration of Artificial Intelligence (AI) into retail strategies has become essential, particularly in creating personalised customer experiences across various channels.
The Importance of Personalisation in Retail
As consumer behaviour evolves, the demand for tailored experiences intensifies. Shoppers prefer interactions that reflect their individual preferences. Achieving “true personalisation” remains a challenging endeavour for many retailers. According to a study by Manhattan Associates, only 20 percent of more than 100 surveyed brands reported customising product recommendations based on customers’ purchase histories. This highlights a critical gap between technological capabilities and actual implementation.
Composable Retail: A New Approach
The concept of “composable retail,” as described by BigCommerce, underscores the need for retailers to adopt flexible e-commerce platforms. This enables brands to create experiences that can be individually tailored not just to customer cohorts but to specific individuals. By utilizing AI and machine learning, brands can harness customer data to provide timely and relevant messaging.
In practice, this means deploying intelligent algorithms that predict customer behaviour, which can significantly enhance product recommendations, search results, and ultimately drive sales. For example, platforms can analyse previous customer interactions to present products that align with a consumer’s past shopping habits or preferences, creating a more engaging shopping experience.
Dynamic Pricing through AI
Price sensitivity among consumers remains a pivotal part of their purchasing decisions, especially in an economy marked by inflation. Retailers need to implement dynamic pricing capabilities that respond to changes in customer purchasing power while optimising profit margins. By employing AI, businesses can adjust pricing based on real-time data reflecting both market conditions and individual customer insights.
For instance, brands can leverage customer journey data to offer personalised pricing that resonates with a shopper’s specific context, fostering quicker purchasing decisions than traditional discount strategies. The alignment of dynamic pricing with timely marketing efforts can enhance conversion rates, benefiting both customers and retailers alike.
Enhancing Customer Engagement with AI Technologies
Retailers are increasingly turning to advanced AI applications such as chatbots and AI-generated content. A notable example is Tommy Hilfiger, which introduced an AI chatbot on its Facebook page as early as 2016. This initiative aimed to assist customers in browsing collections and selecting outfits based on their style. Such technologies not only streamline the shopping process but also give customers a sense of personal attention—something that is often lacking in traditional retail settings.
The consultancy firm McKinsey estimates that AI could enhance marketing productivity by 5 to 15 percent, primarily through more engaging campaigns personalised to the consumer’s context. However, many online shopping experiences remain static, which can detract from the audience’s expectations for interactivity. Employing AI to enrich the content experience can align retail offerings more closely with consumer desires.
Operational Efficiency through AI Integration
A successful retail strategy hinges on operational efficiency. Generative AI can assess supply chain data—covering aspects like sourcing, production, and demand forecasting—to identify inefficiencies that can be improved. Additionally, trend forecasting powered by AI allows brands to make proactive decisions about inventory and merchandising, ultimately leading to better customer satisfaction.
A report from McKinsey & Company suggests that generative AI holds the potential to add as much as $275 billion to the operating profits of the apparel and luxury sectors. However, only a small fraction of fashion executives are prepared to fully leverage this transformative technology.
Conclusion
AI-powered personalisation is not just a trend; it is an essential strategy for retailers aiming to thrive in the e-commerce landscape. By adopting a composable retail model and harnessing dynamic pricing, customer-facing AI technologies, and operational efficiencies, brands can enhance customer experiences and drive engagement. As consumer expectations continue to rise, the integration of AI into retail strategies represents not just an opportunity but a necessity for long-term success.
