Ecommerce AI Recommendation Tracker: Does ChatGPT Recommend Your Products?

Short version: an ecommerce AI recommendation tracker tells you whether ChatGPT, Perplexity, or Google AI Overviews name your products when a shopper asks for a recommendation. Eight tools already do some version of it. Here's what they cost, which one to start with if you have this problem today, and why we decided not to build a ninth.

The oddly specific problem

A growing, high-intent traffic channel that is structurally invisible to every measurement tool most merchants already pay for.

You run a small ecommerce store. You've done the SEO thing. You rank third for your main category term, your product pages load fast, your reviews are respectable. Your dashboard says organic traffic is flat-to-slightly-down, which everyone tells you is normal now.

Then a friend mentions she asked ChatGPT for a recommendation in exactly your category. It named two brands. Neither was yours.

You open your rank tracker to check. It has nothing to say, because rank trackers track URLs and positions, and AI assistants don't do either of those things. They name brands. They name model numbers. They summarize, cite one or two sources, and move on. There is no position eleven. There is no "you were almost recommended." You either got named or you did not exist for that shopper.

There is no position eleven. There is no "you were almost recommended."

This is the actual shape of the problem: a growing, high-intent traffic channel that is structurally invisible to every measurement tool most merchants already pay for. Marketers on r/digital_marketing have started circling it, the general observation being that search now starts before the query, in communities, reviews, and AI tools. Which is a nice insight and also deeply unhelpful if what you want is a number.

Wait, is this actually a problem?

The channel data is genuinely striking.

Per Adobe, AI-referred traffic to ecommerce sites grew 393% year over year in Q1 2026. Conductor/TheStacc research found AI referral traffic to US retail sites grew 693% year over year during the 2025 holiday season, with ChatGPT referral visits alone up 1,079% in twelve months.

And it's good traffic. Visitors arriving from ChatGPT, Perplexity, and Google AI Overviews convert 42% better than paid search, email, or affiliate traffic, and spend 37% more per visit. A BCG survey of 9,000 consumers across nine countries found over 60% report high trust in generative AI shopping results.

1-2 brands get cited when someone asks a shopping question. That's the whole shelf.

Now the brutal part: when someone asks a shopping question, typically one or two brands get cited. There's no page two to scroll to, no long tail to farm. And product pages built for conversion, big images, minimal copy, prominent buy button, hand AI engines almost nothing to cite.

AI recommendation tracker tools compared

Pricing as of research; these tools reprice frequently, so verify before you buy.

The gap is real. There is nothing affordable, ecommerce-specific, and product-catalog-aware below roughly $189/month for usable prompt volume. A Shopify-native tracker that monitored individual SKUs rather than just brand mentions would be genuinely differentiated.

We looked hard at that gap. Then we backed away from it.

So why isn't this a slam dunk?

Because the gap exists for structural reasons, not because eight companies simultaneously failed to notice it.

The product is an API reseller with a dashboard. Strip away the marketing and the mechanism is: send prompt to LLM API, get response, parse whether the brand appears, store result. Every feature is a variation on that loop. There's no proprietary dataset, no network effect, no accumulating advantage. Otterly already does this. So can anyone with a weekend and a credit card.

Google AI Overviews have no API. Tracking them means running a headless browser fleet against a company that actively degrades scraping access and changes its DOM without warning. That's not a build cost, it's a permanent staffing commitment. Larger players can absorb an engineer whose job is "fix the scraper again." A lean team cannot.

The margins are a mirage. The seductive pitch is 93% SaaS gross margin. The real math on a hypothetical $39/month starter tier with 100 prompts across four engines is 400 calls, roughly $4 in blended API cost, plus $3-5 per user in infrastructure. That's $7-10 COGS on a $39 plan before a single support ticket. A power user who reruns prompts daily makes that tier margin-negative. Realistic steady-state blended margin lands closer to 65-75%, workable for a funded company, tight for a small one, and fatal when combined with the next problem.

LLM costs scale with usage, not seats. Traditional SaaS pricing works because marginal cost per user is near zero. Here, every price tier is a bet on usage behavior you don't control. And your entire cost structure sits at the mercy of vendors who can change pricing or terms overnight while you have exactly zero leverage.

Push upmarket to fix the economics and you land directly on Otterly Standard and Profound, better funded, already established, already selling to agencies. There's no clean lane.

Real problem. Real gap. Bad business.

What we're watching

Three things would change the math:

1. A stable, official AI visibility API, from OpenAI, Perplexity, or a neutral aggregator. If measuring citations stops requiring scraping, the maintenance liability disappears and the build becomes genuinely lean.
2. Merchant awareness crossing the search threshold. When "why doesn't ChatGPT recommend my products" becomes a high-volume query rather than a niche marketing conversation, the education tax drops and distribution gets cheap.
3. A Shopify App Store-native distribution path. Product-level SKU tracking inside the platform merchants already live in would sidestep the acquisition-cost problem entirely, assuming the unit economics survive the platform's cut.

If Otterly's Lite plan gets meaningfully more generous, the remaining gap closes on its own and this stops being a question. We'll check back.