Reimagining the Commerce Funnel for an AI World

The applications of generative AI are often so expansive that an equal challenge to product creation is product prioritization. So goes the future of the consumer commerce funnel, where the transformation of the entire customer journey – the way consumers search, discover, consider products – seems boundless. We imagine a world where natural language translates nuanced queries directly into SKU recommendations or AI assistants negotiate down price as consumers browse in real-time or computer vision models convert low-fidelity images or social media screenshots into an exact product match. To ground these possibilities in empirical data, we leveraged our consumer panel to investigate which types of purchases and stages of the customer journeys within those purchases can generative AI most support.

Understanding where consumers want assistance

We asked our panel of consumers living in the United States to imagine they could have professional assistance across different purchasing scenarios. Each scenario represented a broader attribute of a customer journey – specifically, in which the product selection is unfamiliar, expensive, technical, high-stakes, or cluttered. When we asked our customers to select which scenario assistance would be most valuable (using examples to bring the scenarios to life), our panel resoundingly preference assistance for cluttered journeys. This is somewhat counterintuitive. In the physical retail world, professional assistance typically correlates with ticket size – high-end appliance stores and luxury boutiques offer personalized service, for example, while fast-fashion outlets and discount warehouses often leave consumers to their own devices.

This selection underscores a broader opportunity that cuts across the intersection of AI and e-commerce – that is, democratizing the luxury of bespoke, contextual assistance across the entire spectrum of consumer experiences.

II. Understanding when AI can assist buyers

To pinpoint where AI assistance could add the most value within the customer journey, we asked consumers to reflect on their last furniture purchase (furniture as a case study, because it's typically a high-consideration product). Participants ranked which stages of their journey would have benefited most from professional shopping assistance.

The results were also counterintuitive. Our hypothesis was consumer indication of utility would be front-loaded in the funnel – in product discovery where expertise of a product category can direct consumers in the right direction. The results challenged our assumption. Interest in assistance was relatively distributed across the entire journey and, in fact, the most popular use cases were in consideration and even in post-purchase.

III. Opportunities at the Intersection of AI and Commerce

Our research underscores several opportunities in transforming the customer journey:

1. Semantic Product Enrichment: We asked consumers to articulate how they would describe a recent purchase to peers in one sentence. Second only to its price, style and aesthetic descriptors ranked second. This underscores that market leaders won't merely be conversationalizing traditional search queries (e.g., “I’m looking for a black couch under $600”), but architecting genuinely intelligent dialogues that mirror human-to-human interactions

2. Pricing Optimization: It is unsurprising, especially in an inflationary macro environment, that price is the key purchasing criteria for our panel. On the surface, that criteria is disappointing; virtually any pre-AI storefronts or marketplaces will have an effective price sorting function. We imagine ways to advance those analog mechanisms into a new chapters:

   1. Multi-objective optimization algorithms to balance cost against other personalized criteria, ideally informed by contextual user data

   2. Computer vision models for visual similarity-based price comparisons

   3. Integrations with financial apps to factor in real-time budget constraints

3. Consumer knowledge graph: Success in this domain will hinge on the ability to not just acquire data on both sides of the transaction – from brands, retailers, and marketplaces, but also from the consumer. We believe the most effective platforms will develop sophisticated incentive mechanisms to encourage users to share zero-party and first-party contextual data and employ transfer learning techniques to leverage insights across disparate product categories (e.g., purchasing household appliances to travel accommodations)

IV. Companies at the frontier
Several startups are already pushing the boundaries of what's possible at the intersection of AI and commerce search:

• Daydream is building the future of multi-modal commerce search in which conversational generative AI replaces a cluttered, fractioned, and outdated journey

• Velou is transforming e-com with AI-powered product detail page enrichment. Their product doesn't just generate descriptions; it optimizes them for SEO, emotional resonance, and conversion rate to continuously refine their language model's outputs

Reach out to dlay@nea.com and hworland@nea.com continue the conversation. Subscribe to read the next edition of the Current.