AI Shopping: The New Frontier for Holiday Shoppers

With the holiday shopping season just around the corner, exciting developments are emerging in the world of AI shopping features. This past week, both OpenAI and Perplexity unveiled tools designed to enhance the shopping experience through their existing chatbots, aiming to assist users in researching potential purchases. Here’s a closer look at these advancements and what they could mean for the holiday retail landscape.

Enhancing the Shopping Experience

OpenAI’s latest feature allows users to engage with ChatGPT for more informed shopping. Imagine posing a query like, “Find a new laptop suitable for gaming under $1000 with a screen over 15 inches.” Alternatively, if you have your eye on a high-end garment, you can share a photo and request recommendations for similar items at a more budget-friendly price. This tailored approach streamlines the search for shoppers, making the process more engaging and user-friendly.

On the other end, Perplexity emphasizes the power of its chatbot’s memory to personalize the shopping experience further. Users can obtain recommendations tailored to their unique preferences, leveraging information that the bot has previously gathered about them, such as their location or profession. This capability makes shopping feel less daunting and more intuitive.

The Surge of AI in Online Shopping

According to Adobe, AI-assisted online shopping is projected to witness a staggering 520% growth this holiday season. With this exponential rise, AI shopping startups like Phia, Cherry, and Deft (now rebranded as Onton) might find themselves amid fierce competition. The entrance of giants like OpenAI and Perplexity into this arena raises critical questions: Are established players in the AI shopping space in imminent danger?

The Niche Advantage

Zach Hudson, CEO of Onton, believes that specialized AI shopping startups will continue to provide superior experiences compared to broader tools like ChatGPT and Perplexity. Hudson points out that the efficacy of any model hinges on the quality of its data sources. Currently, large language models like ChatGPT and Perplexity rely on existing search indexes such as Bing or Google, making them dependent on the best results returned from such sources.

Julie Bornstein, CEO of Daydream and a veteran in e-commerce, shares Hudson’s sentiment. She argues that search functionality has often been overlooked in the fashion industry, particularly since traditional methods have struggled to cater to specific customer needs. “Fashion is uniquely nuanced and emotional,” Bornstein states. Finding the perfect dress is a vastly different experience compared to selecting a new TV; achieving the former demands a heightened understanding of styles, fabrics, occasions, and how people curate outfits.

Data-Driven Insights

AI shopping startups are developing their datasets to train their tools on higher-quality data, which is considerably more feasible when focusing on specific sectors like fashion or furniture, as opposed to attempting to catalog the entire internet’s knowledge. Hudson highlights how Onton has created a refined data pipeline to manage hundreds of thousands of interior design products. This meticulous approach enables the company to train its internal models with superior data.

Without such specialization, Hudson warns, these startups risk being overshadowed by larger companies with a more generalized approach. “If you’re using only off-the-shelf LLMs and a conversational interface, it’s very hard to see how a startup can compete with the larger companies,” he asserts.

Strategic Partnerships with Retailers

A significant advantage for OpenAI and Perplexity lies in their existing customer bases and partnerships with major retailers. Unlike startups such as Daydream and Phia, which direct users to external retailer websites to finalize purchases, OpenAI and Perplexity are forging deals with platforms like Shopify and PayPal. This integration facilitates seamless checkouts directly within their conversational interfaces, enhancing user experience and convenience.

The road to profitability for these AI-driven companies, which require vast computational resources, remains a challenge. Similar to tech giants like Google and Amazon, there’s potential for these companies to explore e-commerce opportunities. They could establish models where retailers sponsor advertisements within search results. However, this approach may inadvertently amplify existing frustrations customers have with standard search functionalities.

The Future of AI Shopping

Experts like Bornstein argue that vertical models, whether in fashion, travel, or home goods, are likely to outperform their more generalized counterparts. “They’re tuned to real consumer decision-making,” she states, emphasizing the necessity for AI shopping solutions to remain closely aligned with user intentions.

In a rapidly evolving landscape, the interplay between AI advancements and shopping behavior will undoubtedly continue to shape the future of e-commerce, especially as consumers increasingly seek personalized and efficient shopping experiences.

Additional reporting by Ivan Mehta.

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