MAM
Myntra unveils ‘My Stylist’: AI-based personal style assistant for EORS
Mumbai: Myntra, India’s leading destination for fashion, beauty, and lifestyle, has introduced a revolutionary feature on the platform, called ‘My Stylist’, an AI-powered end-to-end automated personal style guide for its customers. This unique concept is set to revolutionise shopping for fashion and lifestyle at large, with the digital style assistant recommending the right outfits and suggesting a complete look to the users. Ahead of the 18th edition of the highly-anticipated End of Reason Sale (EORS), its biannual fashion event, Myntra is all set to cater to the outfit styling aspirations of shoppers like never before and taking fashion to the next level with this next-gen product discovery and purchasing feature.
‘My Stylist’ has been created to tackle the daunting task of putting together the perfect outfit. Styling requires a deep understanding of a broad range of properties including colour, shape, pattern and fabric, along with awareness of current fashion trends and an individual’s style preferences, to be able to get that perfect outfit. ‘My Stylist’ fulfils these requirements of customers in a way that is scalable, automated, customisable and in real-time. ‘My Stylist’ utilizes Artificial Intelligence to offer customers complete shoppable outfit recommendations based on four different factors: uploaded images from their offline wardrobes, their purchase history on the app, browsing history on the app, and what’s currently trending on the platform, thus giving instant access to styling options and enhancing their shopping experience, making it visual, fun and instantly gratifying.
The pioneering product has been developed in-house and will be unique to the platform, while also being a one-of-its-kind offering at this scale in the Indian e-commerce fashion ecosystem. The feature throws recommendations from ~4.5 lakh styles from the fashion and lifestyle categories currently, which includes all top wear, bottom wear, footwear and accessories. Since the launch of the feature, ‘My Stylist’ has witnessed good adoption. It is currently the highest used feature on the floating action button or ‘M-Explore’ on the homepage of the app, with a high engagement rate and click-through rate (CTR) of over 65 per cent. The company expects nearly 80 per cent of its customers to engage with recommendations from the ‘My Stylist’ feature on trial by the end of this year.
How customers can use ‘My Stylist’
For customers who have shopped before on Myntra, My Stylist helps them find and shop for a complete look based on any apparel product they bought recently on the app, recently browsed products, or popular outfits on the platform based on which outfits are generating maximum engagement from users. For new customers, the feature also enables them to upload images of any product from their offline wardrobe and get pairing ideas. New and existing users can also play around with the feature by taking a picture or uploading an image from their phone gallery of any offline product from their wardrobe and checking out pairing ideas to complete an outfit. The feature lets users ‘shop the entire look’ at the click of a button.
The Tech Behind My Stylist
Myntra’s in-house artificial intelligence (AI) technology, machine learning (ML) models and computer vision algorithms, along with the expertise of subject matter experts in curation and styling, is used to power My Stylist’s ‘Fashion Object Detection’, ‘Image Search’, and ‘Outfit Recommendations’ features. These aspects use an AI algorithm that taps into the platform’s catalogue data via deep convolutional neural networks and is also trained using a Bi-LTSM model that expands the dataset from Myntra’s catalogue, which enables the feature to handle a high volume of requests.
Myntra chief product and technology officer Raghu Krishnananda said, “ The launch of ‘My Stylist’ takes our commitment to democratising fashion using technology, a notch higher! Powered by machine learning and AI-based technology, this first-of-its-kind innovation at scale in the fashion space in India is poised to further take the customers’ shopping experience to the next level by recommending looks with an understanding of fashion. Our team of experts has worked tirelessly to ensure that this feature is user-friendly and intuitive, and from what we have gathered since the launch, we are confident that our customers all across will love to have access to this expert stylist at their fingertips through the much loved Myntra app.”
Roadmap for ‘My Stylist’
The feature currently covers the fashion and lifestyle segments, with plans in the pipeline to include other categories including beauty and an increased number of styles as well. Myntra also plans to include elements like ‘Your Digital Wardrobe’ and ‘Save the Look’, along with additional customizable user options, which consider factors like trends, age, gender, body type, and personal style, in subsequent phases.
Users can access ‘My Stylist’ via a floating action button on Myntra’s homepage, with banners and picture-in-picture videos informing them about the same.
Digital
GUEST COLUMN: How AI is restructuring distributor and retailer motivation models
From incentives to intelligence, AI is redefining how brands engage channel partners
MUMBAI: Artificial intelligence is rapidly transforming how brands engage with their most critical yet often overlooked stakeholders: distributors, retailers, and last-mile influencers. For Abhinav Jain, co-founder and CEO of Almonds Ai, this shift marks a fundamental departure from traditional, transaction-led incentive models toward behaviour-driven, data-intelligent ecosystems. In this piece, Jain examines how AI is enabling brands to decode partner motivations, predict engagement patterns, and deliver personalised, scalable experiences—ultimately redefining channel relationships from transactional exchanges to long-term growth partnerships.
Across many sectors, there is increasing recognition that motivating those who bring products to market (distributors, retailers, last-mile influencers) poses a growing challenge.
Brands continue to invest significant marketing and digital resources to consumers, yet in many countries and the vast majority of emerging economies, these types of consumer-focused investment areas have had little impact on ultimate product delivery. Rather, it is still the case that traditional retail continues to make up most products sold.
So why is it that the systems built around motivating these channels have yet to evolve?
For decades, distributor and retailer engagement revolved around static schemes – quarterly targets, volume-based rewards, and occasional trade promotions. These programs were designed around transactions, not behaviour. The assumption was simple: if incentives increase, performance will follow.
Now, with the advent of artificial intelligence, the definition of performance is being challenged.
With the development of artificial intelligence, businesses can move beyond simply creating loyalty based on transactional-based models and toward models built on behaviours, the behaviours of channel partners that are intrinsic to their motivations in engaging with particular brands. As a result, the means by which businesses develop relationships within their distribution network are starting to evolve; thus, ultimately changing how brands interact with those within their distribution network.
Assessing engagement: Transitioning from transactional- to behavioural intelligence
Traditional loyalty systems refer to transactional activity (sales data). Although this data is valuable and important, it only provides a partial view of engagement across the channel partner.
For example, a retailer may have a high frequency of sales of a product, but their lack of engagement with the manufacturer would not reflect that they have true loyalty toward that brand. Conversely, a retailer who actively participates in training programmes, acts as brand advocates, and is engaged in learning with the supplier would exhibit more profound levels of loyalty but would have been invisible based on historical incentive programmes.
Artificial intelligence allows for the identification of behaviours that help to address this gap. Brands are able to use a variety of engagement data points, participate in learning programs, respond to communications, redeem behaviour and track platform use behaviour in order to identify motivation through behaviour.
McKinsey has stated that companies that leverage advanced analytics for their sales and distribution functions can achieve as much as a 15-20 per cent increase in productivity due to increased awareness of their behavioural trends throughout their networks.
This visibility of behavioural patterns within channel ecosystems can be transformational to brands as they can now view how partners engage on their path to purchasing products, instead of just measuring the sales revenue generated by those purchases.
Predicting motivations, not just measuring performance
Possibly, the largest contribution of Artificial Intelligence (AI) to helping brands engage with partners via channel ecosystems is its ability to predict future engagement versus simply measuring past performance.
Traditionally, brands only realised that a partner was disengaged (not likely to purchase products) once their sales performance had already declined. By then, the brand would have to use significant amounts of incentives or aggressive promotional activities to recovery their partner’s engagement level.
AI models can help organisations to detect early signs that a partner is becoming disengaged, such as declining participation in learning modules, declining interaction via the platform, or slower reward redemption rates. These indicators can help organisations to proactively engage with their partners before their sales performance begins to decline.
The practical application of AI and predictive analytics gives brands the ability to re-engage with their partners prior to their sales performance declines. For example, instead of developing and implementing broad-reaching incentive programs that provide a “one size fits all” incentive to all partners in an ecosystem, brands are able to develop targeted, engaging re-engagement programmes. This is how personalisation can be done on a large scale, such as across global distribution and retail networks.
The vast majority of distributor and retailer channels have thousands, if not millions, of individual channel partners. Historically, providing personalisation to such a large number of businesses has not been feasible.
However, with the advent of AI, personalisation at scale is becoming a reality.
Brands can now create tailored engagement journeys for all their partners, based on their partner profiles, through some combination of machine learning models and behavioural segmentation. For example, high-performing distributors might receive higher levels of leadership-based recognition and greater incentives to continue to grow. Emerging retailers, on the other hand, might be supported with training, onboarding rewards, and measurable performance milestones.
The shift towards personalisation of partner engagement echoes the direction that consumer marketing is already moving towards.
According to Salesforce’s report, over 70 per cent of customers expect personalisation in the way that brands engage with them. As such, there is a growing expectation for B2B ecosystems to have these same types of expectations from their channel partners.
Gamification and continuous engagement
AI is also radically changing how brands will engage with their channel partners through the use of gamification.
Many traditional incentive-based contests and leaderboards would spark temporary engagement among their participants, but they struggled to sustain engagement over time. With the use of AI, gamification mechanics are evolving dynamically based on historical and evolving participation patterns by their channel partners.
Challenges, rewards, and recognition structures can be modified continuously in order to sustain engagement with all of a brand’s partner segments. This will provide a greater opportunity to move away from episodic campaigns towards ongoing, continuous engagement experiences.
When channel partners receive motivation as part of their daily business activities through recognition, learning, and tracking their performance, long-term loyalty will be achieved.
Aligning motivation to broader impact
There is a growing trend within the channel ecosystem to integrate sustainability and socially responsible behaviours into the channel partner programmes of brands.
Increasingly, brands are motivating their partners to use sustainable practices in their operations, participate in sustainable practices like sustainability-related knowledge programmes, or promote products that are in line with their sustainability objectives.
Brands can use AI to monitor and measure these types of behaviours and incorporate them into their incentive frameworks so that brands can align their commercial objectives with broader social and environmental outcomes.
A shift in the way brands view their channel partners
AI is having the most significant impact on the way that brands are now viewing their channel partners, as it relates to the underlying philosophy of those fundamental relationships.
For the past several decades, many brands have viewed their channel partners as intermediaries in the supply chain. More and more brands are now beginning to view their channel partners as key ‘partners-in-growth,’ and their actions can have a direct impact on market performance.
In fact, all the channel ecosystems are using behavioural engagement platforms to design new models that reward not just transactional behaviour, but also create continuous engagement journeys for their partners, where their partners can receive recognition for their participation, learning, and continued engagement, thereby reinforcing long-term loyalty to the brand.
The future: Intelligent channel ecosystems
As we consider what the next phase of channel engagement may look like, many believe that it will be based on intelligent ecosystems, using AI to continuously monitor and adjust the engagement strategies used to engage their channel partners, in real time and based on the behaviours of those partners.
For brands operating in complex distribution networks, the ability to perform well will be determined both by whether products are available to their customers, as well as by the enthusiasm, expertise, and loyalty shown from each channel partner that represents the brand each and every day that they are working on behalf of the brand.
While AI clearly does not eliminate the human aspect of a brand’s relationship with its channel partners, it does allow brands to better understand and nurture that relationship.
In markets where the last mile will determine whether a sale is made, how one leverages the intelligence gained by using AI will ultimately be the difference between gaining a new, sustainable competitive advantage versus losing one.
