Content Strategy
The Lmo7 Experiment Playbook: Fast Feedback Loops for Amazon and AI Search
The brands that win in an AI-mediated world aren’t the ones with the loudest story. They’re the ones that learn the fastest. At Lmo7, experimentation isn’t a side project – it’s how we run eCommerce.
9 December 2025
5 min read
Rather than chasing “perfect” campaigns or rewriting a whole website once a year, we build tight feedback loops across Amazon, DTC and AI search. Small tests, clear hypotheses, quick reads, and decisive action.
The five-step experiment loop
Every test runs through the same simple playbook:
Hypothesis – one clear belief we want to test.
Setup – a minimal, controlled change.
Run – enough time and spend to get a clean signal.
Read – what changed, and is it statistically or commercially meaningful?
Scale or scrap – roll out, iterate, or park it.
If we can’t write the hypothesis on one line, we don’t run the test.
Experiments on Amazon
On Amazon, this might mean:
Title and image variants for a hero SKU aimed at increasing click-through on core keywords.
Ad structure tests comparing tightly themed campaigns vs broad catch-alls.
Price and pack tests to see where AI-driven shoppers convert best, not just where they click.
We use automation to handle the boring bits e.g. setting up variants, logging dates, pulling results, but humans interpret the context. If a competitor ran out of stock halfway through the test, we factor that in. If a retailer promo skewed results, we know.
Experiments in AI search
AI search is where it gets interesting. We’ll set up controlled prompts that reflect real consumer questions and track how often your brand appears, how it’s described, and which competitors are mentioned alongside you.
Tests here might include:
Adjusting on-site schema and content to see how models update their understanding of your products and claims.
Seeding new Q&A and reviews on Amazon to influence how LLMs talk about your benefits.
Reframing your positioning (e.g. “for shift workers” vs “for busy professionals”) and watching which audiences models route you towards.
Again, the loop is the same: hypothesis, change, observe, learn, repeat.
Making feedback truly fast
Fast feedback isn’t just about speed of testing. It’s about speed of decision.
We standardise reporting so that every experiment lands in a simple, repeatable format:
- What we tested
- What we saw
- What we’re doing next
No 40-slide decks, no mystery metrics. Just enough detail for a marketing lead, a founder or a commercial director to say “yes, scale it” or “no, park it”.
Why this matters now
AI-powered platforms change quickly. Waiting for quarterly reviews or annual planning cycles means you’re always reacting late to shifts in the way customers search and buy.
The Lmo7 experiment playbook keeps you in motion: continuously probing what works in Amazon, DTC and AI search, then folding those learnings back into your strategy.
It’s less about being “data-driven” and more about being decision-driven: running only the tests that lead to clear actions, and doing it often enough that you compound small wins over time.
The five-step experiment loop
Every test runs through the same simple playbook:
Hypothesis – one clear belief we want to test.
Setup – a minimal, controlled change.
Run – enough time and spend to get a clean signal.
Read – what changed, and is it statistically or commercially meaningful?
Scale or scrap – roll out, iterate, or park it.
If we can’t write the hypothesis on one line, we don’t run the test.
Experiments on Amazon
On Amazon, this might mean:
Title and image variants for a hero SKU aimed at increasing click-through on core keywords.
Ad structure tests comparing tightly themed campaigns vs broad catch-alls.
Price and pack tests to see where AI-driven shoppers convert best, not just where they click.
We use automation to handle the boring bits e.g. setting up variants, logging dates, pulling results, but humans interpret the context. If a competitor ran out of stock halfway through the test, we factor that in. If a retailer promo skewed results, we know.
Experiments in AI search
AI search is where it gets interesting. We’ll set up controlled prompts that reflect real consumer questions and track how often your brand appears, how it’s described, and which competitors are mentioned alongside you.
Tests here might include:
Adjusting on-site schema and content to see how models update their understanding of your products and claims.
Seeding new Q&A and reviews on Amazon to influence how LLMs talk about your benefits.
Reframing your positioning (e.g. “for shift workers” vs “for busy professionals”) and watching which audiences models route you towards.
Again, the loop is the same: hypothesis, change, observe, learn, repeat.
Making feedback truly fast
Fast feedback isn’t just about speed of testing. It’s about speed of decision.
We standardise reporting so that every experiment lands in a simple, repeatable format:
- What we tested
- What we saw
- What we’re doing next
No 40-slide decks, no mystery metrics. Just enough detail for a marketing lead, a founder or a commercial director to say “yes, scale it” or “no, park it”.
Why this matters now
AI-powered platforms change quickly. Waiting for quarterly reviews or annual planning cycles means you’re always reacting late to shifts in the way customers search and buy.
The Lmo7 experiment playbook keeps you in motion: continuously probing what works in Amazon, DTC and AI search, then folding those learnings back into your strategy.
It’s less about being “data-driven” and more about being decision-driven: running only the tests that lead to clear actions, and doing it often enough that you compound small wins over time.