Urabnic wants to reshape how brands engage with customers and deliver personalised shopping experiences. At the forefront of this is harnessing AI to redefine the way consumers discover and engage with fashion. In an exclusive interview with Rahul Dayama, Founding Partner of Urbanic, we explore the profound impact of AI on Urbanic’s operations and the broader fashion retail sector. From personalised recommendations to curated wardrobes, Urbanic’s innovative use of AI algorithms has not only enhanced customer satisfaction but also revolutionised the traditional shopping experience.
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Join us as we delve into the intricacies of Urbanic’s AI-powered platform, uncovering how it analyses past purchases and preferences to offer tailored styling suggestions. We’ll uncover the challenges faced in integrating AI into the customer experience and the strategies employed to overcome them, shedding light on the journey towards seamless integration.
We also examine Urbanic’s commitment to transparency and data privacy, ensuring customers are informed and empowered in their interactions with AI-driven recommendations.
PD: How has the integration of AI technology impacted Urbanic’s ability to provide personalised recommendations and styling suggestions to customers? What key insights can the AI algorithms glean about customer preferences and lifestyles?
Rahul Dayama: At Urbanic, we have affiliated AI technology within our system effectively to evaluate customer information and enhance their experience. We have devised recommendation models that empower customers with customised style tips, predict their buying patterns and provide relevant product suggestions and information.
Our personalised recommendation models have been resourceful for customers and for us, as it help us make better business decisions. We have witnessed a healthy increase in the conversion rates post-deployment of the recommendation model.
Key insights that AI algorithms can glean about customer preferences and lifestyle:
AI analyses the browsing patterns of the app users to offer each of them customised product recommendations that are best suited to their taste, increasing the urge in the customers to purchase.
AI can also figure out gaps in consumer wardrobe and handpick styles and suggest them that complement their wardrobe.
AI can regularly send messages to clients regarding new and bumper deals and discounts based on their wishlists, viewed products, and other preferences.
AI bots can place orders on behalf of customers. These bots also help customers handpick items that will complete their overall outfit. These bots can reduce the workload on customer support executives as well, by providing information, addressing simple and initial queries by the customers regarding a purchase, etc.
PD: Can you explain how Urbanic is using AI to curate customised wardrobes and outfits for shoppers? How does the technology analyse past purchases and preferences to suggest complementary pieces?
Rahul Dayama: Urbanic has also invested in generative AI that works towards customer experience. For instance, we have personalised recommendation models that are real-time predictive models that feed our customer’s apps with relevant products that align with their past purchases and preferences. Further, it also makes a significant number of innovations and revamps possible in its designs and styles since they are directly recommended by top customers and style experts. This, basically, helps the brand forecast a more accurate demand and thus saves efforts.
PD: What changes has Urbanic seen in customer engagement and shopping behaviour since rolling out the AI-powered platform? Have you seen increased customer satisfaction, conversion rates, or other metrics?
Rahul Dayama: Since implementing Urbanic’s AI-powered platform, notable results include higher customer satisfaction and conversion rates, higher customer retention with personalised recommendation and AI-style bots, quick and satisfactory resolution through virtual bot agents, data-driven insights due to AI’s tracking and analysis of customer data, and seamless shopping experience for customers due to new and improved trends available in the app.
PD: What were some of the biggest challenges in integrating AI into Urbanic’s customer experience? How did you overcome those challenges?
Rahul Dayama: Urbanic encountered several hurdles when incorporating AI into their customer service:
First, the transition to self-service digital channels, accelerated by the pandemic, led to complexity. Customers now prefer digital channels as their first point of contact. This shift increased the demand for contact centres and chat functions for more intricate needs. Customers experienced successful results from digital channels in remote tasks. But as a result, they started expecting the same outcome from these channels for more complex tasks.
The labor market was also thin, hence finding a skilled team to preside over AI-driven customer interaction was also a task.
However, to overcome these bottlenecks on the way, Urbanic adopted the route to investment and learning. A five-level maturity model was introduced with advanced and highly skilled companies being given the charge of handling 95% of AI-based engagement operations. Urbanic revamped its interface and improved customer services with personalised IVR and chat. To keep up with the AI revolution, we gave more time and capital to introduce conversational AI services, prompt nudges, and predictive engines in our app. All this aligned with customer preferences thereby increasing their satisfaction.
PD: How does Urbanic ensure transparency with customers around how their data is used to power AI recommendations? Are there any privacy concerns to be aware of?
Rahul Dayama: While we build our technological infrastructures that support business operations and advancements. We are conscious of factors like dependency, security & ethics and usage. For us, utility determination of AI deployment is critical at every stage. We adhere to strict data policies and prioritise gatekeeping information of all nature- sensitive, nonpublic personal etc.
Presently we have the necessary security framework involving audit systems, patches, firewalls and encryption. Additionally, we also educate our employees with structured modules and training on data security and breaches.
PD: What’s next for Urbanic when it comes to leveraging AI and other emerging technologies to improve fashion e-commerce? Are there any future capabilities or innovations you are excited about?
Rahul Dayama: Urbanic aims to continue renovating in utilising AI, such as using large language models (LLM) for designs and AIGC-based creatives. We want to expand our supply chain with new designs but at the same time ensure sustainability. Hence, we will be expanding the Urbanic Oasis Project. We will also further develop our AI-driven design processes to improve customer experience and personalisation with predictive analysis.
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