We came away from NRF APAC 2025 earlier this month with a clear takeaway: Data has become a strategic engine for retail, informing everything from how retailers engage Gen Z to how they enable trend discovery, design products, and partner with suppliers. This allows them to deliver highly relevant and tailored products at astonishing speed.

Across the region, leaders are using data not just to optimize their operations, but to rethink their business models. Here are three examples of how data is shaping the next chapter of retail in the Asia-Pacific region.

1. Data delivers more relevant products in record time

Trend-driven retailers now mine social conversations for product inspiration, then put highly relevant styles in front of shoppers in less than 30 days. Brands are also engaging more powerfully with customers and influencers, as data-driven marketing evolves into more of a conversation. 

For Gen Z, fashion is about self-expression, not blending in—so authenticity is paramount. Their trend discovery flows through social creators rather than marketing campaigns, and trust beats out polish, with peer reviews and unfiltered content carrying more weight than brand ads. Engaging this cohort means shifting from broadcasting to co-creating, and from marketing “one to many” to “many to many,” with the help of powerful influencers. 

In the fashion industry, user experience data is refining homepage personalization, and AI models that have been trained on visual data can now offer intuitive visual searches and virtual try-ons. For example, users can type “Coachella looks” or upload an outfit photo to receive instant styling recommendations. AI also supports creators, tags content, and enriches catalog data for better merchandising.

As marketing and product development merge and evolve into two-way engagement, front-end and back-end systems are being built to inform each other. This is the case with a major Indian retailer’s new brand. Here, the company is using data for forecasting, cataloging, and even product development, allowing them to bring new trends to consumers in record time. Using AI-powered demand-sensing and supplier integration, the brand can gather consumer input and design relevant, trend-driven fashions, then put those products in front of customers within 30 days. As data allows retailers to almost directly connect factories with shoppers, the fashion industry is quickly closing the gap between ready-to-wear and on-demand.

2. Quick commerce runs on prediction, not just speed 

In India, data is allowing retailers to go from mobile order receipt to doorstep delivery in the time it takes to read this article. It’s not mind-reading, but quick commerce (q-commerce) is as close as it gets.

Q-commerce has become popular in Asia, where high urban density and low cost of wages make it economically viable (where it isn’t in most parts of Europe, Australia, and the US). The convenience of having a product delivered in 30 minutes or less resonated with customers, especially in mobile markets, and it’s changed expectations; now, the baseline in India is delivery in under 10 minutes for nearly all retail sectors, from essentials to electronics.

The model’s success depends on an ability to accurately predict which products you need to have, for which consumers, in which locations. This requires micro-fulfillment infrastructure, current inventory, and selection relevance, and data drives all of this. For example, in India, retailers use historical purchase data to predict which SKUs will be in demand from specific cohorts, and they draw on weather data to determine when to feature raincoats, ponchos, and umbrellas on the website.

Q-commerce retailers also use analytics to optimize their dark-store locations, both from a real estate perspective and for making instantaneous decisions on fulfillment at the order level. When an order comes in, data points to the closest distribution locations that can fulfill the order fastest, often pulling products from multiple warehouses for a single order.    

3. Data is personalizing the omnichannel experience

Some of Southeast Asia’s most successful retailers are using data not just to understand customers, but to make product offerings more relevant across both digital and physical channels. Integrating loyalty platforms with offline stores is giving them a universal view of customer behavior, allowing for personalized offers, curated shopping lists, and localized promotions tailored to specific regions and customer segments.

This results in stronger loyalty built on relevance and not just rewards.

When a leading convenience retailer in Indonesia launched its website in 2019, it provided an expanded product selection and fast, free, same-day delivery. For customers, it was a major convenience. For the retailer, it changed their entire way of doing business.

A data-fueled model helped this retailer discover that younger, more affluent customers were drawn to their imported and specialty items online and made much larger monthly purchases than their core, in-store shoppers. Meanwhile, live insights into shopping frequency, product preferences, and regional demand patterns enable smarter cross-selling and more precise segmentation.

Critically, this company chose to hire its own AI and machine learning talent, which allowed it to quickly turn its data insights and local knowledge into tailored strategies and campaigns, from personalized offers to shopping lists to cross-selling promotions based on historical online and offline transactions.

As data allows retailers to strategize and scale more quickly, one challenge is not allowing the human element to slow things down. Whether digital talent is outsourced or in-housed, the key is speed, and retailers must now keep pace with a new generation of customer expectations.