How specific should use cases be?

Specific enough to exclude someone. A use case that describes every possible customer provides no matching signal. 'For Shopify stores doing €100K+/month in paid media' is specific. 'For businesses that want better analytics' is not. The goal is precise intent-matching, not broad appeal.

Should I have a dedicated Use Cases page or include them on the product page?

Both. A section on the main product page covers the primary use cases for broad queries. Dedicated landing pages per use case (e.g., /for/ecommerce-brands, /for/marketing-agencies) target specific intent queries with more depth and can include relevant social proof, case studies, and features relevant to that segment.

How do use cases relate to personas or buyer segments?

Use cases map directly to buyer segments. Each use case should correspond to a real segment of your customer base. If you have 3 distinct buyer types, you should have at minimum 3 use cases. The use case language should mirror how that segment describes their own problem — use the vocabulary they use, not internal product team vocabulary.

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Lead Gen GEO Signals · Published Mar 31, 2026

Use Cases

Explicit statements of who the product is for and what problems it solves.

TL;DR — AI assistants matching products to user intent need explicit use case signals. "TrustData is for e-commerce brands that need first-party attribution" is directly indexable. "A powerful analytics platform" tells the model nothing about fit.

Why Use Cases Matter for AI Engines

AI engines answering product recommendation queries — "what analytics tool should I use for my Shopify store?", "best attribution software for DTC brands" — are performing intent-matching. They map the user's specific situation to the product descriptions they've indexed. The match is strongest when the product page explicitly states who it's for and what specific problem it solves.

Generic product descriptions ("a powerful, flexible analytics platform for businesses") do not contain the entity signals needed for intent-matching. "TrustData is built for e-commerce brands running €50K+/month in paid media who need accurate first-party attribution to recover conversions invisible to GA4" is directly intent-matchable to a specific user query.

Use cases also serve as the basis for long-tail query targeting. Each use case is implicitly a keyword cluster: "first-party tracking for Shopify", "GA4 alternative for DTC brands", "conversion recovery for Facebook Ads". By explicitly listing use cases, you're indexing your product against each of these queries.

How to Implement

A dedicated "Who is this for?" or "Use cases" section with an explicit <h2> heading
Structure as a list: each use case as an <h3> heading with a <p> description
Include: the customer type + the specific problem + the outcome your product delivers
Be specific enough to exclude — "it's for everyone" is not a use case

<h2>Who TrustData is for</h2>

<h3>DTC e-commerce brands</h3>
<p>Brands running €20K+/month in Meta and Google Ads that are losing 30–40% of conversion signals to ad blockers and iOS privacy restrictions. TrustData recovers those conversions via server-side tracking, improving ROAS and reducing CPAs.</p>

<h3>Marketing agencies</h3>
<p>Agencies managing tracking and attribution for multiple clients who need a unified view of data completeness across their entire client portfolio.</p>

Common Mistakes

Use cases that describe features, not outcomes — "supports 50+ integrations" is a feature; "connects your ad platforms directly to verified conversion data" is an outcome-oriented use case
A single generic use case — "for any business that wants better data" describes everyone and helps AI engines match nothing; list 3–5 specific use cases
Hiding use cases behind a "Learn more" link — the use case text needs to be in the page HTML, not on a secondary page that may not be crawled