Lily AI Launches Customer-Centric Content Generation to Improve Product Discovery and Drive Sales

MOUNTAIN VIEW, Calif., Jan. 09, 2024 (GLOBE NEWSWIRE) -- Lily AI ("Lily'' or the "Company”), a retail technology platform specializing in AI solutions for retailers and brands, today announced the launch of AI-powered, customer-centric content generation. This solution combines Lily AI’s existing computer vision, natural language processing, and machine learning with vertical-specific large language models (LLMs) to empower teams to create high-performing product and marketing copy. Now, retailers and brands can inject the consumer perspective directly into product pages and romance copy across e-commerce and marketplace listings.

With Lily AI’s Content Generation, merchants and marketers alike can launch collections faster and drive full-price sales by automating copy for titles and descriptions that are search-optimized, data-driven, on-brand, and, importantly, deliver more helpful and successful shopping experiences. Further, this solution can be harnessed by site merchandisers, copywriters, and other digital creatives to improve efficiency and accelerate the production of email and advertising campaigns.

Specifically, retailers and brands who utilize Lily AI content generation can easily ensure all copy is:

• Automatically SEO-optimized by leveraging the most accurate and customer-centric product attribution available
• Consistently standardized and customized per product
• Highlighting each item’s uniqueness, benefits, and function
• Conversion-optimized by integrating analysis of a retailer’s first-party online data
• Consumer-oriented by harnessing “real customer speak” to describe products, including subjective terms like occasions, styles, and trends
• Easy to proof, integrate, and distribute to different systems, including PIMs and e-commerce, marketplace, advertising, and marketing platforms.

“Those who know us know that we are passionate about making consumers feel great when they shop. We’re equally committed to uplifting retailers and brands too and helping them work smarter, not harder, which is why we are excited to launch our newest solution to improve content generation across sales and marketing,” said Purva Gupta, Co-founder and Chief Executive Officer of Lily AI.

“Advances in Generative AI are transforming every industry, and retail is no exception,” said Sowmiya Chocka Narayanan, Co-Founder and Chief Technology Officer of Lily AI. “I am thrilled to expand our retail AI suite and further integrate LLMs in order to provide high-impact solutions for our retail customers. Generative AI, with its advanced natural language capabilities, will continue to impact the future of work, and it’s critical to empower teams to capably meet this remarkable moment in technological history.”

Within the apparel industry alone, McKinsey estimates that generative AI could add up to $275 billion to the operating profits of the apparel, fashion, and luxury sectors in the next three to five years. McKinsey further estimates that generative AI could increase the productivity of the marketing function by 5 to 15 percent of total marketing spend by improving areas such as: efficient and effective content creation, SEO optimization, and product discovery and search personalization.

About Lily AI

Lily AI is a female-founded retail AI company seeking to bring humanity to shopping by empowering retailers and brands to bridge the gap between merchant-speak and customer-speak. Leveraging computer vision, natural language processing, machine learning, and vertical-specific large language models (LLMs), Lily AI enhances customer shopping experiences by injecting consumer-centric language throughout the retail technology ecosystem. Interoperable with leading eCommerce platforms, Lily AI maximizes existing tech investments to deliver upwards of 9-figure revenue lift through improved product attribution, enhanced discovery, and higher customer conversion. Learn more at www.lily.ai.

Media Contact:
Berns Communications Group
Carly Burns
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