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Retail jargon confuses shoppers—AI can fix it
The online shopping experience has become increasingly complex, and retailers must adapt to the evolving needs of their customers. One major obstacle is the overwhelming amount of technical and marketing-speak product descriptions that confuse and deter consumers from making a purchase. According to Lily AI’s research, nearly 40% of shoppers have used new and emerging AI-powered search engines (such as ChatGPT or Perplexity) to assist them in online shopping.
In today’s era, it is crucial for retailers to prioritize machine-friendly product content that can be easily accessed by AI-powered search engines. Gupta warns that if retailers do not make their product content “machine” friendly, they risk declines in organic web traffic and sales, as well as reduced brand visibility and market share.
On the other hand, AI has the potential to bridge the communication gap between consumers and retailers. Product content optimization (PCO) is an automated, dynamic process of enriching product data with consumer, merchant, marketer, and machine-friendly product information to enhance product discoverability. This enables shoppers to find products more easily and increases their chances of making a purchase.
In fact, Lily AI’s PCO implementation for one fashion retailer enabled 129% more sales and boosted non-brand search by 88%. For another large multi-brand retailer, the company layered granular product attribution data into their existing recommendations solution and saw an overall increase in revenue per visit (RPV), average order size (AOS), and overall demand.
As AI becomes increasingly influential in online shopping, retailers must prioritize machine-friendly product content to stay ahead of the competition.
Source: https://www.forbes.com/sites/garydrenik/2025/05/01/retail-jargon-confuses-shoppers-ai-can-fix-it/
