Conversational Search Engines

• Conversational search engines are AI-powered systems that provide direct, dialogue-driven responses by synthesizing information from multiple sources.
• C-SEO techniques aim to improve content citation prominence via LLM scoring, yet current white-hat methods yield marginal ranking gains compared to traditional SEO.
• Benchmarking with C-SEO Bench shows task- and domain-dependent effectiveness, with early adopter advantages diminishing in a zero-sum competitive environment.

A conversational search engine (CSE) is an AI-powered information retrieval system that provides direct, dialog-driven answers to user queries, often synthesizing content from multiple sources and maintaining state over multi-turn interactions. Unlike conventional search engines that return ranked lists of hyperlinks, CSEs—frequently powered by LLMs—aim to interpret, integrate, and articulate responses conversationally, while supporting contextual understanding, clarification, and information synthesis.

1. Transformation from Traditional to Conversational Search

The integration of LLMs fundamentally alters the architecture and interaction paradigm of search. Classic engines surfaced links in response to keyword queries; CSEs deliver context-rich, synthesized answers, with inline citations referencing underlying web sources (e.g., Perplexity.ai, Google AI Overviews, ChatGPT Search). This realignment positions content providers in a new competitive landscape: rather than optimizing solely for retrieval rank, they must also ensure their content is cited, positioned prominently, and rendered favorably within the generated response. Citational mechanisms within LLM outputs amplify the significance of content structure, surface salience, and consistency with prevalent LLM training distributions (Puerto et al., 6 Jun 2025).

2. Optimization Strategies: Conversational SEO (C-SEO) Versus Traditional SEO

Conversational Search Engine Optimization (C-SEO) comprises content modification techniques intended to improve the likelihood, prominence, and quality of citations by CSEs. The underlying optimization problem is to maximize the implicit LLM scoring function $f(q,d_i)$—the probability that document $d_i$ is cited for query $q$. Analytically:

$\max_{d_i} f(q, d_i)$

or equivalently, minimize its position in the cited list: $\min_{d_i} \text{rank}(c_i)$

C-SEO extends beyond the classical SEO paradigm, which primarily manipulates retrieval order. While the two can overlap, the optimization focus of C-SEO is the post-retrieval transformation—specifically, how the LLM cites and summarizes content after retrieval (Puerto et al., 6 Jun 2025).

3. Benchmarking and Evaluation: The C-SEO Bench Framework

C-SEO Bench was developed to address the lack of comprehensive, multi-domain, and multi-actor benchmarking for C-SEO methods. The benchmark encompasses two major CSE tasks (question answering, product recommendation) across six distinct domains (news, debate, general web, retail, video games, books). It features more than 1,900 queries and over 16,000 documents, significantly exceeding the breadth of prior datasets.

A critical innovation is the simulation of "multi-actor dynamics," supporting varying degrees of C-SEO adoption (adoption rate $\alpha$) by competing content providers. The evaluation protocol measures rank improvement ($\Delta_{q,d}$):

$$\Delta_{q, d} = \text{rank}_d^0 - \text{rank}_d^\alpha$$

where $\text{rank}_d^0$ is the baseline citation rank and $\text{rank}_d^\alpha$ its rank given C-SEO adoption rate $\alpha$.

Aggregate metrics, including the area under the curve (AUC) for rank improvement as $\alpha$ varies, and statistical significance tests (Wilcoxon signed-rank with Holm-Bonferroni correction), formalize the empirical protocol. The framework enables comparison with traditional SEO strategies by measuring the effect of explicit document reordering in the LLM context (Puerto et al., 6 Jun 2025).

4. Experimental Outcomes: Comparative Effectiveness and Zero-Sum Dynamics

Empirical results derived from C-SEO Bench demonstrate:

• Ineffectiveness of Current C-SEO: In nearly all settings, white-hat C-SEO methods (stylistic changes, added references, enhanced authority, LLM-friendly summaries) yield statistically insignificant gains in citation ranking. Across 54 test settings, significant improvements are observed in only 3, with most average improvements being less than 0.2 (standard deviations $>$1).
• Dominance of Traditional SEO: Manipulating document position in the LLM input context produces far greater citation gains (e.g., rank improvements of 2.77 for SEO versus 0.36 for C-SEO in retail).
• Zero-Sum Adopter Congestion: Increasing the number of C-SEO adopters sharply reduces per-actor citation benefit, indicating a congested, zero-sum environment; early adopters see brief advantage, but diffusion among competitors nullifies aggregate gains.
• Task and Domain Variability: Only specific C-SEO strategies (chiefly LLM guidance, combined content improvement) offer moderate positive effect, and primarily in product recommendation, not question answering. This effect vanishes with stronger LLMs or in highly competitive adoption scenarios.

These findings stand in contrast to prior literature, which often reported substantial C-SEO gains using single-actor, limited-domain benchmarks (Puerto et al., 6 Jun 2025).

5. Implications for Content Development and System Design

The evidence from C-SEO Bench leads to several tangible recommendations and open questions:

• Prioritization of SEO Over C-SEO: Optimizing retrieval ranking and document position within the LLM context stream remains the most impactful strategy for ensuring citation in CSE outputs.
• C-SEO as a Complementary, Not Replacement, Approach: Current white-hat, content-centric C-SEO techniques provide, at best, marginal benefit and cannot substitute for SEO in securing visibility in LLM-generated answers.
• Competitive Arms Race and Diminishing Returns: As C-SEO techniques propagate across content providers, their collective efficacy diminishes, paralleling zero-sum and congested equilibria observed historically in classical SEO.
• Future Research Needs: Advancements may require joint optimization targeting both retrieval and LLM summary salience or the development of fundamentally novel techniques for enhancing content prominence in LLM-powered search pipelines.
• Scope for Adversarial/Black-Hat Tactics and Multilingual Scenarios: Current findings are constrained to white-hat methods and mainstream commercial LLMs; the potential impact (and defense) against adversarial methods remains an open area.

6. Conclusions and Forward-looking Directions

CSEs driven by LLMs inaugurate a new optimization landscape, where traditional SEO strategies retain overwhelming influence on information citation and surfacing, and most C-SEO approaches currently fail to deliver measurable visibility improvement. For practitioners, this mandates continued investment in retrieval-oriented optimization and a cautious, supplemental approach to C-SEO, pending further methodological breakthroughs. The comprehensive evaluation framework and open datasets provided by C-SEO Bench establish new standards for empirical rigor and benchmarking in this rapidly evolving field.

References, code, and datasets for reproducing these results are available at the project repositories (Puerto et al., 6 Jun 2025).