AI Visibility Analytics Platforms for CPG and Retail Brands: What to Measure and Compare

AI visibility analytics platforms for CPG and retail brands have to answer four questions that a generic brand tracker mixes together: is the category recommending you, is the brand described correctly, do individual products make the shortlist, and which retailers the answer sends the shopper to. Most monitoring products answer the first two. A smaller group adds product-level tracking from a catalog or retailer-share views, and one vendor ties AI answers to retail sales data. Pick on which layer you must prove, not on how many engines a plan lists.

This guide uses each vendor's own product pages and documentation as they read on October 17, 2026. It compares published specifications and is not a hands-on test of any product. Where we add professional judgement, we say so. Prime AI Visibility publishes this site and is evaluated on the same criteria, with its own limits.

The four layers CPG and retail teams need to separate

AI search visibility for a consumer-goods company is not one number. A brand can be praised in an answer while a competitor's product fills the shortlist, or a product can appear with no mention of who makes it. Our guide to e-commerce brand visibility explains the shopper questions behind this. For measurement, we split the problem into four layers.

1. Category. Does an answer to "best [category] for [need]" include your brand at all?
2. Brand. When the brand is named, is it described accurately, and which sources does the answer cite?
3. Product. Which specific products appear, in what position, at what price, against which rival products?
4. Retailer. Which stores or marketplaces does the answer point to, and does that match where you sell?

In our judgement, the first mistake CPG teams make is buying a product for layer 1 and then reporting layer 3 to a retail buyer. The second is treating one engine's shopping card as a market share figure.

AI visibility analytics platforms for CPG and retail brands: the short answer

• Category and brand layers: any monitoring product that saves full answers and the sources they cite can cover these. See our guide on how to choose between monitoring products for the base criteria.
• Product layer: Peec AI and Scrunch publish product-level shopping views. Otterly flags ChatGPT shopping cards per prompt. Profound lists ChatGPT Shopping on Enterprise.
• Retailer layer: Peec AI and Scrunch both publish views of which retailers answers favor. SPINS describes a platform that adds retail point-of-sale data.
• Today's limit: the product-level views we found are built around ChatGPT's shopping experience, so coverage of other engines is narrower.

What the six platforms publish, layer by layer

The table lists what each vendor says on its own pages. A blank cell in our reading is "not stated", not "not available".

Platform	Product layer	Retailer layer
Peec AI	Catalog upload	Retailers AI favors
Scrunch	Product tracking	Retailer share
Otterly	Shopping cards	Not stated
Profound	Enterprise only	Not stated
SPINS	Product attribution	Retail POS data

Prime AI, held to the same layers:

Layer	Prime AI
Category	Saved answers
Brand	Names and links
Product	No SKU table
Retailer	No share view

Methodology note: each cell describes what a vendor publishes, not measured accuracy or outcomes. Prime AI's engines are ChatGPT, Claude, Perplexity, Gemini and an AI Overview-style preview built from Google-grounded answers, not the live AI Overview panel. Prime AI is our own product, held to the same layers, with limits listed below.

What each vendor documents

Peec AI. Its docs say AI Shopping works at product level: you upload a catalog, Peec matches products to your tracked conversations and shows visibility, ranking and competitor products, updated daily. Today it is based on ChatGPT, and "other AI models may follow". The overview also lists shopping queries and the web searches the AI runs while writing an answer, and the product page lists tracking of the retailers the AI recommends most and a comparison-gap view. Its docs advise reading win rate alongside visibility.

Scrunch. Its Shopping page describes tracking which products AI recommends, how many and where they rank, comparing share against competitors, and a retailer landscape and share view of "who gets the sale". It also shows which prompts surface your products.

Otterly. Its help page, dated October 5, 2026, says ChatGPT Shopping cards are tracked inside brand reports on every plan: whether your products surface, where competitors win placement and how the experience looks to a buyer. The same page says advertiser reporting is currently paused.

Profound. Its pricing page lists ChatGPT Shopping as "No" on the Trial and "Yes" on Enterprise, alongside Agent Analytics for AI-sourced traffic. Its pages do not describe a catalog upload.

SPINS. A September 22, 2026 press release announces Agentic Discovery, which combines AI visibility signals with SPINS category intelligence, product attribution and retail point-of-sale data to prioritize content fixes. That is the vendor's own description, and we found no public pricing or engine list.

In our judgement, the divide is data source. The first four measure what answers say. SPINS adds what shoppers bought, which is the only way on this list to relate answers to sales, and its claims should be checked in a demo.

Catalog maths: how big does a CPG test get?

This calculation is illustrative, not a vendor limit. Suppose a brand has 12 product lines and writes 8 questions per line, one for each family from our shopper-question guide: category, constraint, comparison, trust, price, where to buy, usage and gifting.

• 12 lines × 8 questions = 96 prompts.
• 96 prompts × 3 engines = 288 answers per run.
• Weekly runs for a month (4) = 1,152 answers.
• Daily runs for 30 days = 8,640 answers.

Two points follow. A product-level view needs enough questions per product to separate signal from noise, and a catalog of hundreds of SKUs multiplies the count quickly. Ask each vendor what counts as a prompt, how many runs it makes, and whether product tracking draws from the same allowance as brand tracking.

Mention, citation, recommendation, referral and conversion

Keep five signals separate in every report.

1. Mention. An answer names your brand or product.
2. Citation. An answer links to a page you own.
3. Recommendation. An answer puts the product on a shortlist for the shopper's need.
4. Referral. A visitor arrives from an AI tool. Check whether the platform reads your site analytics or relies on your own.
5. Conversion. The visit becomes a sale. For retail brands this may happen at a retailer, outside your analytics, so ask where conversion data comes from.

The metrics page defines each measure used on this site.

A four-week pilot for a CPG or retail brand

1. Week 1: freeze the questions. Write 40 to 60 shopper questions across the families, with no brand names in the category questions. Tag each by product line.
2. Week 1: save a baseline. Run them yourself and keep every answer with its links.
3. Week 2: give each vendor the same questions and the same competitor list, then export the results.
4. Week 3: audit ten answers per layer. For product claims, check the named product, price and retailer against your own catalog and the retailer's page.
5. Week 4: rerun the baseline. Answers vary from run to run. Treat only large, repeated gaps as findings.
6. Ask the closing questions: which engines drive each view, how a product is matched to a catalog entry, what happens when a retailer listing changes, and what you can export.

Who each option fits

Peec AI and Scrunch fit brands that need product shortlists and retailer views and can accept an engine mix centered on ChatGPT's shopping experience. Limitation: product views depend on how well the platform matches catalog entries to answers.

Otterly fits teams that want shopping cards flagged inside a broader monitoring plan. Limitation: its page describes visibility of cards, not catalog-matched product metrics.

Profound fits enterprise brands that want shopping tracking alongside crawler and referral analytics. Limitation: shopping tracking is listed on Enterprise only, and no price is published.

SPINS fits CPG brands that already buy its data and want AI visibility joined to retail sales. Limitation: the offer is new, described in a press release, and its engine coverage and pricing are not public.

None of them fits a brand that only needs a saved record of category and brand answers. For broader shortlists, see our comparison of monitoring options for B2B companies and our explainer on what these products measure.

Where Prime AI fits, and where it does not

Affiliation: Prime AI Visibility is our own product. It fits the category and brand layers: you write the shopper questions, and it saves each answer with the names mentioned and the links shown across ChatGPT, Claude, Perplexity, Gemini and an AI Overview-style preview. Flash is free: five questions across ChatGPT, Claude and Perplexity, up to 15 answers a month, no card. Starter at $69 a month covers all five. The features page lists the rest.

Limitations: Prime AI Visibility has no product-level or SKU table, no catalog upload, no retailer share view and no shopping-card capture. It does not report sentiment or rank, and it does not record crawler visits, traffic, sales or conversions, so those must come from your analytics and retail data. It does not track Microsoft Copilot, Grok, DeepSeek, Meta AI or Google AI Mode, and it does not grade whether a fact is correct. Checks run monthly by default, with weekly, temporary daily and on-demand checks on paid plans. PyraBuilds' Prime AI agent profile describes the product in Pyra's agent catalogue.

When the next job is implementation

A dashboard shows where products stand. Fixing product data, earning coverage on the review sites and retailers that answers cite, and rewriting pages are separate work. Percepture, founded in 2004 and Inc. 5000-recognized, offers generative engine optimization services and digital PR for the third-party coverage answers rely on. Fit: a team that wants search, answer-engine and earned-media work planned together. Limitation: Percepture is a services firm, not a measurement platform, so it pairs with one. Prime AI Visibility has a commercial relationship with Percepture.

Teams with many product pages to rewrite can also look at Pyra's AI agents for marketing and sales, which include human-approved content operations agents. Pyra's anonymised AI visibility case study covers a different industry and is a published client result, not a benchmark for retail. Pyra is also commercially connected to Prime AI Visibility.