An analytics-driven simulation exploring click-through behavior, intent match quality, and monetization opportunities for AI-native product suggestions.
One-Pager Summary:
Prompt to Purchase: Executive Summary
Large Language Model (LLM) chatbots/providers like ChatGPT, Gemini, and Claude are rapidly replacing traditional search for product discovery and decision-making. From general queries like “Top Bluetooth Headphones for Running” to hyper-personalized prompts like “As a long-distance trail-runner with a large head and a $200 budget, which earbuds should I buy?”, LLMs not only inform users, they’re guiding final purchase decisions.
In this rapidly evolving landscape, LLMs are not just search engines; they’re taking on the role of trusted informers and advisors. This makes LLMs prime real estate for product placement, opening up a new era in online marketing and recommendation optimization.
Consider a world where every product-related LLM response is a Google Ads-style battleground or a high-stakes auction. Sellers would compete to place their products in the optimal spot by matching metadata, price, and messaging with targeted user intent. The product’s relevance, rank, and trustworthiness would become levers that ultimately drive user action like clicks and time spent browsing the linked product pages. As a result, LLM providers and sellers would need to develop methods to check if products meet the user’s needs accurately (i.e. intent matching) and whether this, along with the resultant rankings, drive engagement.
This project attempts to simulate aspects of the matching system, build metrics, and use various tools to test how these factors influence engagement. These findings would point to how monetization could work in the future, while maintaining trust and ethics.
This project’s goal is to create a framework based on synthetic data that helps measure certain levers, specifically match success and rank, and how these affect engagement, through click-through rates (CTR) and time a user spends on a product’s linked page (TS). Other independent variables or confounding variables like price, category, ratings, etc. will also be introduced to better emulate reality.
The analysis will shift from a rudimentary simulation of measuring user intent matching success and its direct possible associations with engagement, to a more typical analytical exercise around rankings and user interactions while introducing additional independent/confounding variables. Finally, the relationship between the levers and engagement will be statistically tested for significance.
We can build a hypothesis that attempts to simplify and highlight these interactions:
Controlling general product attributes, two important drivers of engagement on LLM outputs are: 1. The accuracy of products matching user-intent, and.. 2. Where the LLM consequently decides to rank the product within the response.
This can be rewritten more scientifically as the following alternative hypotheses:
The hypothesis, analysis and subsequent findings decode how LLM providers and product sellers can approach data and optimize product recommendations for maximum engagement.