We run controlled experiments to understand why AI isn't including your ecommerce brand in its recommendations, then deliver a research-backed plan to help you show up more often in the answers that matter.
Buyers are increasingly asking AI what to buy before they search, browse, or compare on their own. The brands that show up in those responses are the ones that get the first shot at the sale. If your brand isn't there, it doesn't matter how strong your onsite experience is — demand may already be flowing to competitors before a session ever begins.
Across categories, we observe the same pattern: brands that are cited more often in AI answers tend to share a consistent set of characteristics. These aren't traditional ranking factors — they're the conditions that make a brand more likely to be selected or included.
01
A clear category identity. If AI can't quickly place what you are, you're less likely to be included in relevant answers.
02
Credibility beyond your own site. Brands cited by reviews, press, experts, and other trusted sources tend to appear more often than brands relying only on owned content.
03
Language that maps to buyer intent. Brands that use the words buyers actually use when asking questions tend to be included more often than brands describing themselves in internal marketing terms.
We treat AI visibility as something you test, not something you guess at. Each PickMeLabs experiment runs a structured set of buyer-style queries across multiple AI platforms to see which brands get selected most often — and which signals appear to move that frequency.
Inputs
A structured query set covering broad, constrained, and comparison-based questions — the kinds buyers actually ask. We run them across relevant AI platforms (ChatGPT, Claude, Gemini, Perplexity) including your brand and the competitors that matter in your category.
Variables
How your brand's category framing, buyer-language fit, third-party validation, and entity mentions compare to brands that are being selected. We look at what's present in the brands that consistently appear and what's absent from the ones that don't.
Outputs
How often each brand is cited or selected across the query set, how it is described, how often competitors appear instead, and which patterns correlate with higher inclusion in your category.
Everything we publish comes from experiments run on live queries across major AI platforms — not theory or opinion. Each study documents observed differences in how often brands are selected or cited, and what appears to drive those differences.
Across 5 queries on ChatGPT, one brand appeared in 4 out of 5 responses — others appeared once or not at all. The experiment maps which characteristics correlated with higher inclusion, and how constrained queries changed the brand pool entirely.
The same queries run from two different locations returned completely different brands. No single brand appeared consistently across query types. The experiment maps how regional context and price-tier positioning shape which brands get included.
Beauty is a category where third-party validation appears to carry significant weight. We're mapping which brands get cited most often, and what distinguishes them from brands with comparable awareness that aren't being selected.
Most ecommerce brands have no clear picture of how often they show up in AI answers — or why competitors are being included instead. This experiment maps the selection patterns in your category, surfaces what's likely affecting your inclusion, and delivers a prioritised plan your team can act on.
A 2–3 week research sprint to understand how often your brand appears in AI answers, which positioning, authority, and language characteristics may be affecting that frequency, and what to change to increase how often you're included across the queries that matter.
Established ecommerce brands in competitive categories with a team that can act on a strategic roadmap — not pre-revenue brands or teams looking for a quick checklist.
Five working deliverables designed to help your team make clearer decisions across positioning, content, authority, and technical execution.
Documents observed selection patterns in your category and where your brand stands relative to what's consistently surfacing.
Shows which query types you appear in, which you don't, and which brands are taking the positions you're missing.
Identifies the specific differences between your current positioning and what tends to surface in your category.
Prioritized moves across positioning, content, authority signals, and technical improvements — ranked by expected impact.
A presentation with your team covering every finding, what it means for your category, and how to use the roadmap.
Start with a conversation. We'll ask a few questions about your category and current visibility, and tell you whether an experiment makes sense.