Alenjith K Johny & Ajay Rag, Economic Times
Jul 27, 2025

Startups in the 60-minute fashion delivery segment are betting on features such as ‘try and buy’ and artificial intelligence (AI)-powered virtual try-ons to tackle high return rates, a key pain point in the segment. These tools are helping increase conversion rates and reduce returns while offering greater flexibility to buyers, said industry executives.

Mumbai-based Knot, which recently raised funding from venture capital firm Kae Capital, said partner brands that typically see return rates of about 20% on their direct-to-consumer websites are witnessing sub-1% returns through offline stores, a trend it is now replicating through these digital features.

“Our partner brands, which have offline stores, would typically witness 20% returns on their direct to consumer websites. But for the same purchases on offline stores, the returns are less than 1%. That is the idea. With the ‘try and buy’ feature, users can make a very decisive purchase at their doorstep,” Archit Nanda, CEO of Knot, told ET.

Return rates among users of the company’s virtual try-on feature are similarly much lower than the platform’s overall user base, he said.

Other venture-backed quick fashion delivery startups such as Bengaluru-based Slikk, Mumbai-based Zilo and Gurugram-based Zulu Club are also testing similar features to increase conversions and reduce returns.

“Returns play as big a part as maybe forward delivery does. Because these are expensive products, giving the customer his or her money back also plays a very critical role,” said Akshay Gulati, cofounder and CEO of Slikk.

Instant returns

Slikk is piloting an ‘instant returns’ feature where, like its 60-minute delivery service, returns are also completed within an hour. Once a return request is made on the app, a delivery partner picks up the product and refunds the amount instantly. The startup claims its return rate is 40-50% lower than that of traditional marketplaces and that it doesn’t charge customers any extra fees for returns.

Some users said they were satisfied with the delivery speed and trial window but pointed out that the app does not provide any return status updates until the product reaches the warehouse.

“I received my order within 60 minutes and had enough time to try it out. However, after returning the product, I didn’t receive any notification in the application until the delivery agent reached the warehouse,” said Mohammed Shibili, a working professional based in Bengaluru, who tried Slikk’s feature.

Investor interest

Investors tracking the segment estimate that try-and-buy and virtual try-on features can reduce return rates by 15-20 percentage points, translating into substantial cost savings for both platforms and brands.

“Features like try and buy are a huge cost save, not just for the platform but also for the brand. The brand otherwise would lose that inventory till it comes back and can’t make the sale on it. But now, that’s all getting quickly turned around. So, for the brand, it’s a win-win situation as well as for the customer where the money is not getting stuck till it gets the returns refunded,” said Sunitha Viswanathan, partner at Kae Capital.

Old model, new infrastructure

Flipkart-owned fashion etailer Myntra had introduced try and buy back in 2016 to attract traditional shoppers to online retail. However, the feature didn’t scale up due to supply chain limitations, according to industry executives.

“Back when Myntra launched ‘try and buy’, there was no hyperlocal delivery infrastructure. Deliveries were through national courier services. That model isn’t feasible to try and buy unless you have your own hyperlocal delivery fleet,” the founder of a fashion delivery startup said on condition of anonymity.

The founder added that while Myntra operated from large warehouses located on the outskirts of cities, the new-age supply chains are built within cities, allowing faster deliveries and enabling features like try and buy.

By the end of last year, Myntra had launched M-Now, an ultra-fast delivery service currently live in Bengaluru, Mumbai and Delhi, with pilots in other cities. The company said daily orders through M-Now doubled in the last quarter.

“Although it’s still early, our observations so far suggest that the quick delivery model, with its reduced wait time, attracts high-intent customers, leading to naturally lower return rates,” said a spokesperson for Myntra.

The etailer did not confirm whether the try-and-buy feature is being tested under M-Now.

Viability concerns persist

Despite the benefits, the long-term viability of these features is open to question, experts said.

“There is a cost to also providing these services (like try and buy), and whether that becomes viable at all is a question mark at this point of time. I think that’s what the concern is, and it has not been that viable,” said Devangshu Dutta, founder of Third Eyesight, a management consulting firm focused on consumer goods and retail industries.

He added that when platforms offer the try-and-buy feature, delivery executives have to wait while customers try on products, which increases the cost per delivery and reduces the number of deliveries that can be completed. Despite that, some items may still be returned, further impacting operational efficiency.

However, startups are experimenting with these features mainly on higher-margin products to offset operational costs, Dutta said, as return rates across fashion categories can range from under 10% to as high as 40% for certain items.

(Published in Economic Times)

At the recent Phygital Retail Convention in Mumbai, Devangshu Dutta anchored an engaging “Fireside Chat” with Bhavana Jaiswal of IKEA India and Kapil Makhija of Unicommerce , on retailers engaging with their customers across channels and formats, and the opportunities as well as challenges in managing experiences seamlessly across online and offline interfaces.

Watch the video at this link:

In this piece I’ll just focus on one aspect of technology – artificial intelligence or AI – that is likely to shape many aspects of the retail business and the consumer’s experience over the coming years.

To be able to see the scope of its potential all-pervasive impact we need to go beyond our expectations of humanoid robots. We also need to understand that artificial intelligence works on a cycle of several mutually supportive elements that enable learning and adaptation. The terms “big data” and “analytics” have been bandied about a lot, but have had limited impact so far in the retail business because it usually only touches the first two, at most three, of the necessary elements.

“Big data” models still depend on individuals in the business taking decisions and acting based on what is recommended or suggested by the analytics outputs, and these tend to be weak links which break the learning-adaptation chain. Of course, each of these elements can also have AI built in, for refinement over time.

Certainly retailers with a digital (web or mobile) presence are in a better position to use and benefit from AI, but that is no excuse for others to “roll over and die”. I’ll list just a few aspects of the business already being impacted and others that are likely to be in the future.

1. Know the customer: The most obvious building block is the collection of customer data and teasing out patterns from it. This has been around so long that it is surprising what a small fraction of retailers have an effective customer database. While we live in a world that is increasingly drowning in information, most retailers continue to collect and look at very few data points, and are essentially institutionally “blind” about the customers they are serving.
   However, with digital transactions increasing, and compute and analytical capability steadily become less expensive and more flexible via the cloud, information streams from not only the retailers’ own transactions but multiple sources can be tied together to achieve an ever-better view of the customer’s behaviour.
2. Prediction and Response: Not only do we expect “intelligence” to identify, categorise and analyse information streaming in from the world better, but to be able to anticipate what might happen and also to respond appropriately.
   Predictive analytics have been around in the retail world for more than a decade, but are still used by remarkably few retailers. At the most basic level, this can take the form of unidirectional reminders and prompts which help to drive sales. Remember the anecdote of Target (USA) sending maternity promotions based on analytics to a young lady whose family was unaware of her pregnancy?
   However, even automated service bots are becoming more common online, that can interact with customers who have queries or problems to address, and will get steadily more sophisticated with time. We are already having conversations with Siri, Google, Alexa and Cortana – why not with the retail store?
3. Visual and descriptive recognition: We can describe to another human being a shirt or dress that we want or call for something to match an existing garment. Now imagine doing the same with a virtual sales assistant which, powered by image recognition and deep learning, brings forward the appropriate suggestions. Wouldn’t that reduce shopping time and the frustration that goes with the fruitless trawling through hundreds of items?
4. Augmented and virtual reality: Retailers and brands are already taking tiny steps in this area which I described in another piece a year ago (“Retail Integrated”) so I won’t repeat myself. Augmented reality, supported by AI, can help retail retain its power as an immersive and experiential activity, rather than becoming purely transaction-driven.

On the consumer-side, AI can deliver a far higher degree of personalisation of the experience than has been feasible in the last few decades. While I’ve described different aspects, now see them as layers one built on the other, and imagine the shopping experience you might have as a consumer. If the scenario seems as if it might be from a sci-fi movie, just give it a few years. After all, moving staircases and remote viewing were also fantasy once.

On the business end it potentially offers both flexibility and efficiency, rather than one at the cost of the other. But we’ll have to tackle that area in a separate piece.

(Also published in the Business Standard.)

Retailers seem to be fighting a losing battle against the growth of ecommerce, and it is only the nature of the shopping activity, especially for fashion – interactive, social, and immersive as it is – that has kept many retailers relevant and in business.

However, the defensive stance is changing, and now they’re using technology to get the customers back into the store. Forward-thinking retailers are reimagining trial rooms, stores, business processes and entire business models. It’s not a physical versus virtual approach but an approach that integrates both sides. The idea is to create a more immersive experience than pure digital retail can be, using some of the same tools as ecommerce.

It is important to remember that the whole retail environment is a “suggestive” environment. Due to cost and other operational factors most retailers are ill-equipped to provide appropriate levels of excitement, suggestion and support during the browsing and buying process.

For many, the simplest move could be screens serving up their catalogue to customers within the store. For instance, US department store chain Kohl’s has initiated connected fitting rooms that identify products the customer is carrying, and bring up not only those items onscreen, but additional colours and sizes that are available. If the customer wants an alternative, a message goes to a sales associate who can fetch the requested option. Macy’s and Bloomingdales are using tablets in the trial rooms, while Nordstrom, Neiman Marcus and Rebecca Minkoff are attempting to boost their fashion sales using magic mirrors to provide similar enablement. These devices and the processes empower and involve the customer far more, while leaving store staff free for other activities.

A step up, Puma is using “virtual trials” for its apparel products by having a customer take images of herself in specific positions, and then mapping styles on their own images to visualise how they might look. While this needs more work and investment, this is still only a more developed product browser technique from the customer’s point-of-view.

The next level, augmented reality trials and virtual fit, are significantly more sophisticated at creating simulations of a selected garment image draping and falling on the customer’s body even as he or she moves normally. Imaging and texturing of the simulated garments is technically challenging and expensive, repeated for each new style and option. The imaging also needs to mimic the “wearer’s” movements. Nevertheless, retailers such as Polo Ralph Lauren are finding it worth their while to investigate these new technologies, as these reintroduce the much needed “theatre” that are integral to a successful retailer.

For the customer virtualisation expands the number of items “taken” into the trial room, and creates more convenient product discovery. More products can be seen in the same shopping time, and sharing of images and videos with friends and family, engages them in the shopping process as well.

For retailers, the benefits multiply. Inventory can be optimised, and there is reduced handling and shrinkage. Even without sales associates, it is feasible to prompt for alternatives and related products, improving conversion and transaction values, reducing space and costs of physical trial rooms, and increasing the number of customers serviced especially at peak traffic times.

A phenomenal advantage is the data captured that is relevant while the customer is in the store, but which can be linked to future promotions. Valuable intelligence, such as what is being tried and for how long, can help the retailer to quickly gauge demand patterns, and adjust pricing and promotions. Normally retailers only capture sales transactions (post-fact), and miss out the rich information on in-store behaviour that etailers do collect and analyse.

However, massive hurdles to virtualisation remain, including data input accuracy, product accuracy, and the technical capabilities of the tech solution adopted. A bigger concern is whether technology is intuitive and seamless, or whether it gets in the way of the shopping experience. Further, consumers do have privacy concerns about the images and other data collected.

Its important to remind ourselves that, on its own, technology is just a novelty – huge transformation of business processes, organisational capabilities and behaviours must happen as well.

That is perhaps the biggest mountain to climb.