Customer Intent: the Universal Language That Supercharges the E-Commerce Stack

When retailers consistently invest in understanding customer context and intent, they’re setting themselves up for success in a number of key ways.  

By Lily AI CEO Purva Gupta

For years, fragmented and disjointed e-commerce stacks have been missing a universal language that can help present every unique consumer with exactly what they’re looking for in real time. It’s clear why this is now reaching a critical state.

Retailers and their customers now live in a hyper-personalized world in which those customers expect to be shown what they want online before they even know they want it. With digital transformation having been rapidly accelerated by Covid, the e-commerce market is now two to five times bigger than it was in 2019. Yet, it’s clear that many retailers are failing to digitally transform and keep their customers – and those rapidly-changing expectations – at the center of the experience.

Because of outdated stacks and a reliance on simple tools like text-based keyword search and spreadsheet-based demand forecasting, retailers are still making bad guesses about shoppers & inventory, with a 2% industry-average conversion rate, a 50% return rate and average unsold inventory of 30%. This unfortunate status quo leaves behind both disappointed shoppers and a great amount of wasted money on the table.

We are now in a world in which the success of an e-commerce customer discovering what they’re looking for is rooted in a retailer’s deep understanding of both their own product attributes and their ability to predict customer’s intent. When retailers consistently invest in understanding customer context & intent, they set themselves up for increased conversion, larger order sizes and a future in which consumers return to their sites time and time again.

This is the challenge that Lily AI and our customer intent platform has been built to solve.

We’ve now launched the first customer intent platform that provides the depth and scale of attribution that no other solution can match, enabling a far deeper understanding of their own products and customers. It turns qualitative product attributes into a universal mathematical language at a high volume with unprecedented accuracy. By using robust, AI-powered image recognition to extract product attributes, Lily AI allows retailers to configure 10x more attributes for each product – from fit to style, to embellishments, occasion, and much more.

It doesn’t end there, however. Because we’ve pioneered and automated this universal language to describe products, we’re also in a unique position to understand consumers in this same language of attributes. We do this in order to understand the why behind what they like and dislike in a completely unique way that’s never done before by AI.

We have not only automated the deepest product attribute extraction, but also the styling intelligence technology that understands how each attribute relates to all others in this universal language. By matching deep tagging of products (with 15,000+ attributes) with deep profiling of consumer motivations, Lily AI can help present every unique consumer with the item they’re truly looking for in real time. This results in a step-change in metrics across search engines, recommendation systems, demand prediction models and item set-up processes. What Customer Data Platforms (CDPs) did for customer data, Lily AI is now doing for customer intent.

We believe every retail stack will require a customer intent platform to be the central system of record of customer intent, and that quickly sends this important data to all destination systems in the retail stack. It’s already leading to eight- to nine-digit boosts in additional revenues for our major retailing customers. Lily AI’s customers include Bloomingdale’s, thredUP, Backcountry and more.

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