Our perspective
Our perspective
Fashion brands can ride the quick commerce wave by leveraging AI/ML and AR/VR to streamline supply chains, cut costs, and boost revenue and thereby meet evolving consumer demands.
In India alone, the quick commerce (or Q-commerce) market has grown from a gross merchandise value (GMV) of $0.10 billion in FY20 to $3.3 billion in FY24. By 2029, it’s projected to reach $9.95 billion.
Companies like Zepto, which has leveraged AI to optimize its delivery routes, Blinkit, which has a robust inventory management system, and Swiggy’s Instamart, which has focused on enhancing customer experiences, are leading the charge in quick commerce. They are generating gross sales of around $5.5-6 billion monthly. But this surge isn’t confined to grocery retail; it’s also making waves in other retail sectors, transforming how businesses operate and consumers shop.
The fashion industry is tapping into this momentum, partnering with quick commerce leaders to sell clothing and accessories. New entrants like Flipkart Minutes, a service that promises delivery within a few minutes of ordering and is already live in select cities, and Amazon Tez, a similar service set to launch in early 2025, are also making forays into the space. Early adopters like Myntra, which piloted its two-hour delivery M-Now service in Bengaluru, are already seeing promising returns, including a 25% increase in operational revenue in FY23, reaching ₹4,375 crore from ₹3,501 crore in FY22.
This trend highlights the growing importance of speed and convenience in fashion retail. Consumer demand is pushing the industry toward even faster, more reliable delivery options.
However, adopting this new business model does present some challenges for fashion retailers. Companies must consider the significant logistical and technological demands of quick commerce and the need to meet heightened consumer expectations for speed and convenience. Shopping for clothes is very different from shopping for groceries, and fashion retailers must find a way to translate the best aspects of an in-store shopping experience to the virtual space, such as the ability to try on clothes and interact with merchandise before buying.
Advanced technologies like AI, machine learning, and data analytics can help address these challenges by optimizing inventory management, streamlining delivery logistics, and enhancing personalized customer experience. This will free up time and resources for retailers to focus on other critical transition areas.
The grocery industry has been a trailblazer in quick commerce, setting benchmarks from which other sectors, including fashion, can learn.
Dark stores—small warehouses strategically located within metropolitan areas—have enabled quick commerce companies like Getir to store high-demand products close to consumers. This setup ensures that orders can be fulfilled quickly and efficiently. Retailers can develop their dark stores or partner with quick commerce companies, which will then distribute the retailer’s products to their dark stores. The latter allows retailers to benefit from quick commerce infrastructure without the extensive upfront investment.
For example, manufacturers like Dabur supply products directly to the warehouses of quick commerce companies, which then distribute them to their dark stores. This model helps offset traditional retail’s high channel costs by eliminating distribution layers and directly connecting the internal supply chain to the dark stores.
The potential of AI to predict purchasing needs and suggest timely promotions is a game-changer for fashion retail. High average order value (AOV) is critical in quick commerce to offset the high operational costs. AI algorithms can predict purchasing needs and suggest timely promotions or bulk buying options. This approach can be adapted to the apparel industry, where fashion retailers can use AI to analyze past purchases and browsing habits to suggest complementary items like accessories or footwear during checkout. This encourages a higher spend per transaction and enhances the customer's shopping experience by making it more personalized and responsive to their tastes.
Quick commerce companies also use AI-driven logistics to optimize delivery routes, ensuring timely and cost-effective deliveries. Predictive analytics enable these services to manage stock levels and forecast demand accurately, ensuring that customers receive their orders without delay.
Fashion retailers are already implementing similar technologies to enhance operations. AI-/ML-powered solutions like o9, Kinaxis, and Relex use advanced algorithms and forecasting models to predict demand trends and manage inventory accurately. Wipro has also helped retail clients develop custom applications that leverage IoT and AI to identify and optimize hot and cold spots within stores, improving visual merchandising. This kind of technology backbone is crucial for supporting the quick commerce model, ensuring that retailers have the capabilities necessary to meet consumer expectations for speed and convenience.
But fashion retail and grocery retail are not apples to apples. Fashion items often require a more personalized shopping experience than groceries. Consumers want to interact with clothes before they buy them—feel the material, try clothes on, and see how they fit. Advancements in augmented reality and AI for image generation enable more realistic virtual try-ons and detailed product visualizations, which can better replicate the in-store experience online.
The logistics of delivering fashion items can also be more complex than groceries. Although the number of SKUs at a given store may be fewer for apparel than for food, apparel comes in various colors, sizes, and styles, requiring meticulous inventory management. Ensuring that the correct items are available in the right quantities at localized dark stores is essential for meeting quick commerce delivery promises. AI/ML can play a significant role by analyzing consumer data and spending patterns, prioritizing inventory levels, and predicting which items will be in high demand.
The competitive edge in quick commerce is not just about fast delivery but also about maintaining the quality of the shopping experience. Retailers must ensure that the process, from browsing to receiving and potentially returning items, is seamless and satisfying.
It is crucial for new entrants to start with the basics, prioritizing integrating effective inventory management systems with quick commerce platforms. This involves leveraging AI to ensure real-time tracking of stock levels and smooth order processing. For instance, Wipro has assisted retail and CPG clients in implementing sophisticated inventory management systems that adjust stock levels based on real-time sales data, significantly reducing overstock and stockouts. These capabilities are critical to support the complex orchestration to fulfill quick commerce orders.
Establishing localized fulfillment centers or dark stores is another essential step. Retailers do not necessarily have to build new facilities; they can partner with established quick commerce platforms to leverage their infrastructure or convert underutilized retail spaces into fulfillment centers. Once the order is received through the quick commerce channel (either through a partner or the company’s channel), it can be routed to the nearest store where the apparel is available for fulfillment, and a nearby agent will be assigned to deliver the order to the customer.
A practical approach would be to start with a pilot program in select locations to fine-tune the processes and technologies before scaling up, allowing manageable adjustments and learning from initial experiences.
For early adopters, the focus should shift to optimizing existing systems and scaling operations. These retailers should invest in advanced AI tools and enhance technology integration further to streamline inventory management, demand forecasting, and delivery logistics. For example, Wipro has developed forecasting and planning solutions for retail clients that utilize machine learning algorithms to predict demand patterns more accurately, helping to maintain optimal stock levels and reduce waste.
Retailers can also use AI-driven solutions to optimize delivery routes, packaging, and shipping, helping to reduce the company’s carbon footprint and the strain on couriers.
Implementing innovative customer experience solutions is another key area for early adopters. Technologies like augmented reality for virtual try-ons and AI-driven personalized shopping experiences can differentiate the brand and attract more customers. By refining their operations and continuing to innovate, early adopters can maintain their competitive edge and set new standards in the market.
Fashion retailers can follow in the footsteps of quick commerce companies like Getir, which have succeeded by reimagining traditional retail delivery operations with the modern consumer in mind. Cutting-edge technologies like AI and data analytics are critical to this transition, making it easier for retailers to do more, including optimizing logistics for faster delivery, personalizing the customer experience, and staying agile to meet changing consumer demands. The key is not to be constrained by old methods but to reinvent them to create a seamless, satisfying shopping experience. In doing so, fashion retailers can thrive in today’s dynamic market.
Naveen Gowthamaraja
Managing Consultant
As a managing consultant within Wipro’s CPG and Retail Consulting practice, Naveen manages the sales and execution of supply chain transformation programs with special focus on supply chain planning, Omni Channel order management & Warehouse Mgmt. solutions.
Rakshit Gangwal
Senior Consultant
As a Senior Consultant in Wipro's CPG Practice, Rakshit has an extensive industry experience in product strategy development, demand and supply forecasting within the supply chain, market mapping, competitive intelligence, and product development.
Vinay Kavde
Senior Consulting Partner
As a consulting partner with Wipro, Vinay works with a portfolio of retail and consumer product clients in areas such as B2B and B2C eCommerce, omnichannel, Industry 4.0, and supply chain.
