The Current #8: Reimagining the Commerce Funnel for an AI World
New Enterprise Associates (NEA)
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The Current is a new series from NEA on the developments impacting consumer technology. Each installment examines a trend, disruption, or opportunity with consumer data. Posts are concise, informative, and always current.
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:
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
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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:
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:
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Disclaimer
This consumer survey was conducted among a representative sample of 150 adults living in the United States. The survey was fielded using the Pollfish platform during August 2024. Pollfish partners directly with app developers; the developer defines an appropriate and specific non-cash incentive in exchange for completed surveys that benefit real consumers but doesn’t motivate them to become career panelists. Please note that as with all survey research, there is a potential for sampling error and other forms of bias. Results should be interpreted as an indication of sentiment among the target population rather than an exact measure.
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