Urabnic wants to reimagine the way brands engage with customers and deliver personalized shopping experiences. At the forefront of this is leveraging his AI to redefine the way consumers discover and engage with fashion. In this exclusive interview with Urbanic's founding partner Rahul Dayama, we explore the huge impact AI will have on his Urbanic operations and the broader fashion retail sector. From personalized recommendations to curated wardrobes, Urbanic's innovative use of AI algorithms not only improves customer satisfaction, but also revolutionizes the traditional shopping experience.
Delve into the intricacies of Urbanic's AI-powered platform, revealing how it analyzes past purchases and preferences to provide customized styling suggestions. We uncover the challenges faced when integrating AI into customer experiences, the strategies adopted to overcome them, and chart a path towards seamless integration.
We also explore Urbanic's commitment to transparency and data privacy to ensure customers are informed and empowered when using 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 have AI algorithms gained into customer preferences and lifestyles? Can it be collected?
Rahul Dayama: At Urbanic, we are effectively integrating AI technology within our systems to evaluate customer information and improve the customer experience. We have devised a recommendation model that provides customers with customized style tips, predicts their purchasing patterns, and provides relevant product suggestions and information.
Our personalized recommendation models are beneficial to both our customers and us because they help us make better business decisions. We have seen a healthy increase in conversion rates after implementing recommendation models.
Key insights that AI algorithms can glean about customer preferences and lifestyles:
AI analyzes the browsing patterns of app users and recommends products customized to each user's preferences, increasing the customer's desire to purchase.
AI can also spot gaps in consumers’ wardrobes, curate styles, and suggest complements to their wardrobes. AI can periodically send messages to customers about new deals and discounts based on their wishlist, viewed products, and other preferences.
AI bots can place orders on behalf of customers. These bots also help customers curate items that complete their entire outfit. These bots also reduce the workload of customer support executives by providing information and responding to customers' simple initial questions about purchases and more.
PD: Can you explain how Urbanic uses AI to curate customized wardrobes and outfits for shoppers? How does this technology analyze past purchases and preferences? Will they suggest complementary products?
Rahul Dayama: Urbanic is also investing in generative AI to enable customer experience. For example, we have a personalized recommendation model, which is a real-time predictive model that feeds a customer's app with relevant products tailored to the customer's past purchases and preferences. Plus, it's directly recommended by top customers and style experts, allowing for many innovations and improvements in design and style. This essentially helps brands predict demand more accurately, saving them effort.
PD: What changes has Urbanic seen in customer engagement and shopping behavior since deploying the AI-powered platform? Have you seen an increase in customer satisfaction, conversion rates, or other metrics?
Rahul Dayama: Since implementing Urbanic's AI-powered platform, notable results include increased customer satisfaction and conversion rates, increased customer retention with personalized recommendations and AI-style bots, and improved customer retention with virtual bot agents. fast and satisfying resolutions, and data-driven insights with AI tracking and analysis. A seamless shopping experience for your customers with customer data and new and improved trends available in the app.
PD: What were the biggest challenges in integrating AI into Urbanic's customer experience? How did you overcome those challenges?
Rahul Dayama: Urbanic faced several hurdles when incorporating AI into customer service.
First, the shift to self-service digital channels, accelerated by the pandemic, has introduced complexity. Customers now prefer digital channels as their first point of contact. This change has increased the demand for contact centers and chat capabilities that address more complex needs. Our customers have experienced successful results from digital channels for remote tasks. But as a result, we have come to expect the same results from these channels, even for more complex tasks. The labor market was also thin, so finding a skilled team to oversee AI-powered customer interactions was also a challenge.
But to overcome these bottlenecks along the way, Urbanic adopted a path of investment and learning. A five-level maturity model was introduced, giving advanced and highly skilled companies the responsibility of handling 95% of AI-based engagement operations. Urbanic revamped its interface and improved customer service with personalized IVR and chat. To keep up with the AI revolution, we've spent more time and capital bringing conversational AI services, prompt nudges, and predictive engines to our apps. All of this was done according to customer preferences and increased customer satisfaction.
PD: How does Urbanic ensure transparency with customers about how data is used to power AI recommendations? Are there any privacy considerations?
Rahul Dayama: We build the technology infrastructure that supports business operations and progress. We are conscious of factors such as dependencies, security and ethics, and usage. For us, determining the usefulness of AI deployment is important at every stage. We adhere to strict data policies and prioritize gatekeeping information of all nature, including confidential and non-public personal information.
We now have the necessary security framework in place, including auditing systems, patches, firewalls, and encryption. Additionally, we educate our employees with structured modules and training on data security and breaches.
PD: What's next for Urbanic in terms of leveraging AI and other emerging technologies to improve fashion e-commerce? Are there any features or innovations you're looking forward to in the future?
Rahul Dayama: Urbanic aims to continue innovating with AI, including the use of Large Language Models (LLM) for design and AIGC-based creative. We want to expand our supply chain with new designs, but we also want to ensure sustainability. Therefore, we will expand the Urban Oasis Project. We will also further develop our AI-driven design processes to improve customer experience and personalization through predictive analytics.
