What is an AI visibility score, and how is it calculated?

A visibility score condenses a messy question, whether AI assistants recommend your products, into a single number you can track over time. Understanding what goes into that number makes it much easier to know which fixes are worth prioritizing.

What is an AI visibility score?

An AI visibility score is a number, usually on a 0 to 100 scale, that summarizes how likely a product is to be recommended when someone asks an AI assistant a relevant shopping question. It plays a similar role to a search ranking, but for conversational answers instead of results pages, and it's built from several underlying factors rather than a single raw count.

The three core factors behind a visibility score

Most visibility scores are built from three measurable components:

• Recommendation frequency - how often a product appears across relevant shopping prompts. A product that shows up in most of the checks it's eligible for has a high recommendation frequency; one that rarely appears has a low one.
• Prompt coverage - how many of the shopping questions in a category a product shows up for at all, regardless of how often. A product might perform well on a handful of prompts but be completely absent from others, which prompt coverage captures.
• Visibility share - how a product compares to competitors when both are eligible to be recommended for the same question. This is what turns an isolated frequency number into a competitive measure.

Together, these three factors give a fuller picture than any single metric alone. A product could have decent recommendation frequency on the few prompts it covers, but low prompt coverage overall, which points to a very different fix than a product with broad coverage but weak visibility share against competitors.

Product-level versus store-level scores

Visibility scoring generally works at two levels:

• Product level. Each monitored product gets its own score, built from its own recommendation frequency, prompt coverage, and visibility share. This is where the most actionable detail lives, since recommendations are tied to a specific product's gaps.
• Store level. A store-level score aggregates scores across all monitored products into a single trend indicator. It's useful for tracking overall direction, but it should always be calculated as a rollup of product-level data, not as an independent, separate measurement.

Why a single visibility score isn't the whole picture

A visibility score tells you where you stand, but not always why. That's why it's typically paired with a separate readiness signal, covering whether a product's content and structured data are set up well, since two products with similar visibility scores today can have very different reasons behind them and very different paths to improving. See the AI readiness checklist for the semantic, technical, and commerce factors that typically explain a low score.

How to use a visibility score in practice

A visibility score is most useful as a trend, not a one-time snapshot. Track it after making changes like adding structured data, rewriting a description, or building comparison content, and watch whether recommendation frequency, prompt coverage, or visibility share moves. A score that isn't moving after a real change usually points to a different underlying issue than the one you just fixed.

Hoko calculates recommendation frequency, prompt coverage, and visibility share for every monitored product automatically, and rolls them up into a store-level trend you can track over time. See our pricing to run your first scan, or read why AI shopping visibility matters now for the bigger picture behind this shift.