Urbanic’s AI-powered journey into fashion e-commerce: a conversation with founder Rahul Dayama

Urabnic aims to reshape the way brands interact with customers and deliver personalized shopping experiences. At the forefront of this is the use of AI to redefine the way consumers discover and interact with fashion. In an exclusive interview with Rahul Dayama, Founding Partner of Urbanic, we explore the profound impact of AI on Urbanic’s business and the broader fashion retail sector. From personalized recommendations to curated wardrobes, Urbanic’s innovative use of AI algorithms has not only increased customer satisfaction but also revolutionized the traditional shopping experience.

Join us as we delve into the intricacies of Urbanic’s AI-powered platform and discover how it analyzes past purchases and preferences to provide customized styling suggestions. We will explore the challenges faced in integrating AI into the customer experience and the strategies used 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 personalized recommendations and styling suggestions to customers? What key insights can the AI ​​algorithms gather about customer preferences and lifestyles?

Rahul Dayama: At Urbanic, we have effectively integrated AI technology into our system to evaluate customer information and improve their experience. We’ve developed recommendation models that provide customers with tailored style tips, predict their purchasing patterns, and provide relevant product suggestions and information.

Our personalized recommendation models have been resourceful for customers and for us, because they help us make better business decisions. We have witnessed a healthy increase in conversion rates after implementing the recommendation model.

Key insights that AI algorithms can gather about customer preferences and lifestyles:

AI analyzes the browsing patterns of the app users to provide each of them with tailor-made product recommendations that best suit their tastes, increasing the customers’ urge to buy.

AI can also discover gaps in consumers’ wardrobes and hand-pick styles and suggest them to complement their wardrobe. AI can regularly send messages to customers about new offers and discounts, based on their wish lists, products viewed and other preferences.

AI bots can place orders on behalf of customers. These bots also help customers choose items that complete their overall outfit. These bots can also reduce the workload for customer support executives by providing information, answering simple and initial customer questions about a purchase, etc.

PD: Can you explain how Urbanic uses AI to create custom wardrobes and outfits for shoppers? How does the technology analyze previous purchases and preferences to suggest additional pieces?

Rahul Dayama: Urbanic has also invested in generative AI that works on customer experience. For example, we have personalized recommendation models that are real-time predictive models that provide our customers’ apps with relevant products that match their past purchases and preferences. Furthermore, it also enables a significant number of innovations and innovations in its designs and styles as these are directly recommended by top customers and style experts. This essentially helps the brand predict more accurate demand and thus saves efforts.

PD: What changes has Urbanic seen in customer engagement and shopping behavior 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 personalized recommendations and AI-style bots, fast and satisfying resolution via virtual bot agents, data-driven insights thanks to the tracking and AI customer data analysis and a seamless shopping experience for customers thanks 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 integrating AI into their customer service:

First, the transition to digital self-service channels, accelerated by the pandemic, led to complexity. Customers now prefer digital channels as their first point of contact. This shift increased demand for contact centers and chat capabilities for more complex needs. Customers experienced successful results from digital channels in remote tasks. But as a result, they began to expect the same result from these channels for more complex tasks. The job market was also thin, so finding a skilled team to lead the AI-driven customer interaction was also a task.

However, to overcome these bottlenecks along the way, Urbanic chose the investment and learning route. A five-level maturity model was introduced, where advanced and highly skilled companies were given the responsibility to handle 95% of AI-based engagement activities. Urbanic has revamped its interface and improved customer service with personalized IVR and chat. To keep pace with the AI ​​revolution, we’ve spent more time and capital introducing conversational AI services, quick nudges, and predictive engines into our app. All this is tailored to the customer’s preferences, increasing their satisfaction.

PD: How does Urbanic ensure transparency with customers about how their data is used to power AI recommendations? Are there any privacy concerns to consider?

Rahul Dayama: As we build our technological infrastructures that support business operations and developments. We are aware of factors such as dependency, safety, ethics and use. For us, determining the usability of AI implementation at each stage is critical. We adhere to a strict data policy and prioritize gatekeeper information of all kinds: sensitive, non-public personal, etc.

We currently have the necessary security framework in place, including audit systems, patches, firewalls and encryption. We also train 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 opportunities or innovations that you are excited about?

Rahul Dayama: Urbanic wants to continue to innovate in the use of AI, such as using large language models (LLM) for design and AIGC-based creatives. We want to expand our supply chain with new designs, but at the same time guarantee sustainability. That is why we are going to expand the Urbanic Oasis Project. We will also further evolve our AI-driven design processes to improve customer experience and personalization with predictive analytics.