Urbanic wants to reshape the way brands interact with customers and deliver personalized shopping experiences. At the forefront of this is leveraging AI to redefine the way consumers discover and interact with fashion. In an exclusive interview with Urbanic Founding Partner Rahul Dayama, we explore the profound impact of AI on Urbanic's operations and the wider fashion retail sector. From personalized recommendations to curated wardrobes, Urbanic's innovative use of AI algorithms has not only improved customer satisfaction but also revolutionized the traditional shopping experience.
Join us as we delve into the intricacies of Urbanic's AI-powered platform, discovering how it analyzes past purchases and preferences to deliver tailored style suggestions. We'll uncover the challenges facing integrating AI into the customer experience and the strategies used to overcome them, shedding light on the journey to seamless integration.
We also examine Urbanic's commitment to transparency and data privacy, ensuring that customers are informed and empowered in their interactions with AI-powered recommendations.
PS: How has the integration of AI technology affected Urbanic's ability to provide personalized recommendations and style suggestions to customers? What key insights can AI algorithms glean about customer preferences and lifestyles?
Rahul Dayama: At Urbanic, we affiliate AI technology into our system effectively to evaluate customer information and improve their experience. We build recommendation models that empower customers with personalized style advice, predict their buying patterns, and provide relevant product information and suggestions.
Our personalized recommendation models have been nifty for customers and for us as they help us make better business decisions. We have seen a healthy increase in conversion rates after implementing the referral model.
Key insights that AI algorithms can gain about customer preferences and lifestyle:
Artificial intelligence analyzes the browsing patterns of app users to provide each of them with personalized product recommendations that best suit their taste, increasing customers' desire to buy.
AI can also discover gaps in consumers' wardrobes and hand-pick styles and suggest them to complement their wardrobe.
Artificial intelligence can regularly send messages to customers about new and high-level 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 handpick items that will complement their overall outfit. These bots can also reduce the workload of customer service executives by providing information, addressing simple and initial customer inquiries about a purchase, etc.
PS: Can you explain how Urbanic is using artificial intelligence to create personalized clothing and apparel for shoppers? How does the technology analyze past purchases and preferences to suggest complementary pieces?
Rahul Dayama: Urbanic has also invested in generative AI that works on customer experience. For example, we have personalized recommendation models which are real-time predictive models that feed our customers' applications with relevant products that align with their past purchases and preferences. In addition, it also makes possible a significant number of innovations and renovations in their designs and styles as they are directly recommended by top customers and style experts. This basically helps the brand to forecast a more accurate demand and thus saves efforts.
PS: What changes has Urbanic seen in customer engagement and purchasing behavior since launching 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, fast and satisfactory resolution through virtual bot agents, information data-driven due to AI tracking and analysis of customer data and a seamless shopping experience for customers due to new and improved trends available in the app.
PS: What have been some of the biggest challenges in integrating AI into Urbanic's customer experience? How did you overcome those challenges?
Rahul Dayama: Urbanic encountered several obstacles when incorporating AI into its customer service:
First, the transition to self-service digital channels, accelerated by the pandemic, has led to complexity. Customers now prefer digital channels as their first point of contact. This change has increased the demand for contact centers and chat functions for more complicated needs. Clients have 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 labor market was also tight, so finding a qualified team to preside over AI-driven customer interaction was also a task.
However, to overcome these roadblocks, Urbanic has taken the path of investment and learning. A five-tier maturity model was introduced with advanced and highly skilled companies tasked with managing 95% of AI-based engagement operations. Urbanic has revamped its interface and improved customer services with personalized IVR and chat. To keep up with the AI revolution, we've put more time and capital into introducing conversational AI services, quick nudges, and predictive engines into our app. All this aligned with the customer's preferences thus increasing their satisfaction.
PS: How does Urbanic ensure transparency with customers about how their data is used to drive AI recommendations? Are there any privacy issues to consider?
Rahul Dayama: As we build our technology infrastructures that support operations and business advancements. We are aware of factors such as dependency, security and ethics and use. For us, determining the utility of AI implementation is critical at every stage. We adhere to strict data policies and prioritize gatekeeping information of any sensitive nature, non-public personal, etc.
We currently have the necessary security framework in place including auditing systems, patching, firewalls and encryption. In addition, we also educate our employees with structured modules and training on data security and breaches.
PS: 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 that excite you?
Rahul Dayama: Urbanic aims to continue to innovate the use of AI, such as the use of large language models (LLM) for AIGC-based designs and creatives. We want to expand our supply chain with new designs but at the same time ensure sustainability. Therefore, we will expand the Urban Oasis Project. We will also continue to develop our AI-driven design processes to improve customer experience and personalization with predictive analytics.