Strategic Planning
AI Search Is Shrinking the Shelf. Ranking Now Decides Repeat Sales
AI search compresses CPG choice into a 1–3 product shortlist, so small ranking gains now drive outsized repeat orders. LMO7 makes your PDPs and D2C pages machine-readable, aligns them to real buyer prompts, and runs weekly optimisation loops so you stay in the answer set.
27 October 2025
9 min read
1) The shift: AI turns the shelf into a shortlist
In Consumer Packed Goods, the answer has become the shelf. Replenishment behaves like search loyalty: if you’re not in the answer set, you’re effectively invisible. AI surfaces like Rufus, ChatGPT, Gemini, and Claude compress choice to one–three options, normalising attributes (e.g., “non-greasy,” “sensitive skin,” “lasts 8 hours”) and presenting a micro-shelf at the exact moment of intent. That’s why small rank lifts have outsize, compounding effects: moving from “outside the answer” to “on the shortlist” not only wins the first basket but often locks in the next ten. Think of it as basket math in motion, tiny improvements in answer position yield durable gains in repeat orders.
2) What “ranking” means now and how to win it
Ranking in 2025 is answer-fit, not keyword stuffing. Your Amazon PDPs and your D2C site must tell the same machine-readable story with clean, consistent attributes and language that mirrors how buyers actually ask. That means titles, bullets, A+ and images map to queryable properties; your brand site carries the same truths in structured data (Product, FAQ, HowTo); and your proof (certifications, test results, usage occasions) is explicit and verifiable. When a parent asks for “a sensitive, non-sticky kids’ sunscreen that won’t sting in salt water,” models should find those exact concepts across your PDP, Q&A, reviews, and brand pages. Do this and you convert AI’s compressed shelf into your advantage: the shortlist becomes predictable and repeatable.
Proof points (context for why this matters): Amazon holds ~38% of US e-commerce across categories, so it’s already where your buyers shop; Shopify GMV reached ~$292B in 2024, making D2C a vast surface that also needs to be AI-readable; most CPG sales remain offline ~79% US, 2024, which makes online ranking a high-leverage growth edge; and across categories, Share of Search approximates ~83% of Share of Market on average (elasticity varies by category and competitive intensity).
Sources: DemandSage; Uptek; Grand View Research; IPA (ipa.co.uk).
3) How LMO7 operationalises ranking (and keeps it)
We start with a free AI Search Audit that shows which products models recommend today for your priority prompts and exactly where your signals fall short. Then we run weekly optimisation loops: closing attribute gaps on Amazon PDPs, aligning D2C schema and copy with real prompts, and seeding crisp, factual answers across surfaces LLMs read. Finally, we pipe those visibility insights into media (Amazon Ads, PMax) so paid efficiency compounds your organic answer position rather than fighting it. The result is simple: you appear on the shortlist more often, win the first basket more reliably, and convert replenishment into retention.
Learn more about our service here
In Consumer Packed Goods, the answer has become the shelf. Replenishment behaves like search loyalty: if you’re not in the answer set, you’re effectively invisible. AI surfaces like Rufus, ChatGPT, Gemini, and Claude compress choice to one–three options, normalising attributes (e.g., “non-greasy,” “sensitive skin,” “lasts 8 hours”) and presenting a micro-shelf at the exact moment of intent. That’s why small rank lifts have outsize, compounding effects: moving from “outside the answer” to “on the shortlist” not only wins the first basket but often locks in the next ten. Think of it as basket math in motion, tiny improvements in answer position yield durable gains in repeat orders.
2) What “ranking” means now and how to win it
Ranking in 2025 is answer-fit, not keyword stuffing. Your Amazon PDPs and your D2C site must tell the same machine-readable story with clean, consistent attributes and language that mirrors how buyers actually ask. That means titles, bullets, A+ and images map to queryable properties; your brand site carries the same truths in structured data (Product, FAQ, HowTo); and your proof (certifications, test results, usage occasions) is explicit and verifiable. When a parent asks for “a sensitive, non-sticky kids’ sunscreen that won’t sting in salt water,” models should find those exact concepts across your PDP, Q&A, reviews, and brand pages. Do this and you convert AI’s compressed shelf into your advantage: the shortlist becomes predictable and repeatable.
Proof points (context for why this matters): Amazon holds ~38% of US e-commerce across categories, so it’s already where your buyers shop; Shopify GMV reached ~$292B in 2024, making D2C a vast surface that also needs to be AI-readable; most CPG sales remain offline ~79% US, 2024, which makes online ranking a high-leverage growth edge; and across categories, Share of Search approximates ~83% of Share of Market on average (elasticity varies by category and competitive intensity).
Sources: DemandSage; Uptek; Grand View Research; IPA (ipa.co.uk).
3) How LMO7 operationalises ranking (and keeps it)
We start with a free AI Search Audit that shows which products models recommend today for your priority prompts and exactly where your signals fall short. Then we run weekly optimisation loops: closing attribute gaps on Amazon PDPs, aligning D2C schema and copy with real prompts, and seeding crisp, factual answers across surfaces LLMs read. Finally, we pipe those visibility insights into media (Amazon Ads, PMax) so paid efficiency compounds your organic answer position rather than fighting it. The result is simple: you appear on the shortlist more often, win the first basket more reliably, and convert replenishment into retention.
Learn more about our service here