Surfaced by AI,
Converted by Design
＋50%
AI Organic Traffic
＋78%
Conversion Rate
6mo
Time to Results
GEO + CRO
Dual Architecture
CDiscount operates one of the largest multi-category e-commerce platforms in France, millions of SKUs across electronics, home, fashion, and grocery, served against the structural advantage of Amazon and the rising pressure of cross-border marketplaces. The brand had built credible traffic from classical search. It had not built credibility with the new layer of discovery that now sits in front of it.
Buyer behavior had already shifted. A meaningful share of high-intent product research was happening inside ChatGPT, Perplexity, Google AI Overviews, and Gemini, generative engines that summarize, compare, and recommend without ever surfacing a traditional ten-blue-link result. CDiscount appeared in those answers inconsistently, often unfavorably, and frequently not at all. Competitors were being cited as authoritative; CDiscount was being paraphrased away.
The conversion side carried a parallel problem. Sessions that did arrive, across both classical and AI-referred channels, were leaking value at predictable but unaddressed friction points: category overwhelm, trust deficit at the commitment moment, and a mobile checkout architecture that had accumulated complexity faster than it had shed it. The two problems were typically owned by two different teams. We treated them as one system.
CDiscount appeared in fewer than 9% of high-intent generative answers across ChatGPT, Perplexity, and Google AI Overviews, well below its classical SERP share of voice. Competitors with weaker organic positions were being cited as authoritative sources.
Product, brand, and category entities lacked the structured semantic definitions that large language models rely on to extract and cite information. The catalogue was readable to crawlers but unintelligible to retrievers.
Category landing pages displayed up to 80 results above the fold without comparison architecture, decision-support modules, or progressive disclosure, actively penalizing users with high intent but low product expertise.
Mobile checkout contained 6 mandatory steps with 4 distinct trust interrupts. Drop-off concentrated at the payment selection stage, where alternative payment options were buried below the fold.
Aggregate review scores, seller ratings, and return guarantees were architecturally invisible at the purchase decision moment, surfacing only after the user had committed, where they no longer drove conversion.
Structured semantic definitions deployed across every product, brand, and category. Schema.org Product, Offer, AggregateRating, Brand, and PropertyValue graphs wired into the catalogue so generative engines could extract and cite CDiscount as the canonical source.
Category and product pages restructured around generative retrieval patterns: clear comparison tables, decision-stage Q&A blocks, and authoritative buying guides that LLMs preferentially cite when answering high-intent queries.
Category landing rebuilt with progressive disclosure, comparison view, and decision-support modules. Filter UX shifted from technical attribute to use-case-driven, matching how customers actually frame purchases.
Six steps collapsed to three. Payment options elevated above the fold. Trust signals (return guarantee, secure payment, seller rating) repositioned at the moment of commitment, not after it.
Aggregate ratings, return policy, and shipping guarantees integrated into the purchase decision UI directly, persistent micro-components surfacing the trust signals customers actually evaluate before clicking buy.
Six months after rollout, citation share inside ChatGPT, Perplexity, Gemini, and Google AI Overviews moved from below 9% to above 28% on tracked high-intent queries.
Behavioral CRO architecture turned the new AI-referred traffic into measurable revenue. Conversion gains held against classical-search holdout cohorts.
The GEO and CRO workstreams were operated as one team, not handed off between disciplines. The compound effect was structurally available because the two architectures were designed together.
The Strategic Diagnostic is the entry point.
Apply for Diagnostic