Idea
Conversational search platform improving product discovery accuracy and reducing user abandonment through dynamic attribute-based preference elicitation.
Research Paper
Core Innovation
This paper introduces Dialogue to Discovery (D2D), an attribute-oriented preference elicitation framework that dynamically prioritizes informative queries and strategically times recommendations. Unlike prior work, D2D leverages product attribute structures to steer conversations efficiently, reducing premature suggestions and improving target-finding accuracy and user engagement.
Why It Matters
E-commerce platforms struggle with limited screen space and user frustration from prolonged or ineffective product searches. This solution improves search precision and engagement by efficiently eliciting user preferences, reducing session abandonment and accelerating discovery. It scales across product categories, enhancing user satisfaction and conversion rates in conversational commerce.
Market Size (TAM)
$20–50B TAM for conversational commerce and product search; $2–10B SAM from e-commerce platforms and conversational AI providers. Driven by rising adoption of voice assistants and demand for personalized shopping experiences.
Potential Customers & Pain Points
- E-commerce platforms – Need to improve product search accuracy and reduce user drop-off
- Conversational AI providers – Need to enhance dialogue efficiency and user satisfaction
- Retailers – Need to increase conversion rates through better product recommendations
- Customer support services – Need to reduce interaction time while maintaining quality.
Business Model
SaaS subscription model targeting e-commerce platforms and conversational AI providers, with tiered pricing based on usage and customization levels.
Competitive Landscape
- Google Shopping Assistant
- Amazon Alexa Shopping
- eBay ShopBot
- Shopify Chatbot
Implementation Challenges
- Integration complexity with existing e-commerce platforms
- User adaptation to conversational interfaces
- Data privacy concerns in preference elicitation
Validation Strategy
- Pilot deployment with mid-sized e-commerce platforms to measure engagement and conversion improvements
- User studies to assess satisfaction and efficiency gains
- A/B testing against existing search assistants to quantify abandonment reduction and accuracy improvements
Research Paper Overview
Dialogue to Discovery: Attribute-Aware Preference Elicitation for Conversational Product Search Assistants
Summary
Conversational product search assistants offer a more expressive, natural, and interactive alternative to traditional keyword-based product search. With limited screen space, showing only a few items increases the need for precise preference elicitation, which can prolong conversations, leading to user frustration and session abandonment. Conversely, rushing to recommend items without a clear understanding of preferences risks poor matches and a degraded user experience. We present Dialogue to Discovery (D2D), an attribute-oriented preference elicitation framework that dynamically exploits the structure of product attributes to efficiently steer conversations toward the user's desired item. D2D adaptively prioritizes the most informative queries and strategically times product recommendations, reducing premature or off-target suggestions that harm engagement. To evaluate D2D, we curate three datasets from the Amazon Reviews corpus. In simulated conversations modelled using a multi-factor utilitarian patience framework, D2D achieves a 22.2-29.9% improvement in target-finding accuracy, 6.6-16.1% reduction in abandonment, and 27.5% shorter average conversations over the state-of-the-art baselines. A complementary user study further confirms significant gains in both user satisfaction and perceived efficiency.