Case Studies

TL;DR — AI engines answering "does product type work?" look for pages with documented, measurable outcomes. "Our customer saw 40% more conversions" with a named customer is citable. "Our customers love us" is not.

Why Case Studies Matter for AI Engines

AI engines are frequently asked whether a product or approach "actually works." These queries — "does server-side tracking recover missing conversions?", "is TrustData effective for e-commerce attribution?" — are best answered with documented evidence. Case studies provide exactly that: real customers, real problems, real measurable outcomes.

The difference between a citable case study and an uncitable one is specificity. "Maison Blanc, a French DTC fashion brand, reduced their GA4 attribution gap from 38% to 4% after deploying TrustData's server-side tracking. Their Meta ROAS improved from 1.8x to 2.6x over 90 days" is a claim with named entity, specific metrics, and a timeframe. It is directly citable. "One of our customers saw huge improvements in their tracking" is not citable — it contains no specific, verifiable claims.

Case studies also serve a second function: they establish the category of problem your product solves. Each case study is an implicit answer to a specific query ("how did company fix their attribution problem?"). The more specific and varied your case studies are, the more query types your content can answer.

How to Implement

• Structure as: client name → industry → challenge → solution → measurable result
• Include specific numbers: "reduced reporting time by 6 hours per week", "recovered €40,000 in invisible conversions in month 1"
• Add a schema.org/Article with the case study subject named in the headline
• Create a dedicated URL for each case study: /case-studies/maison-blanc
• Include a one-paragraph summary at the top — this is the most citable excerpt

Common Mistakes

• Anonymous case studies — "A leading French fashion brand" provides no entity signal; named customers are significantly more citable (with their permission)
• Results without timeframes — "40% improvement" with no timeframe is less credible; "40% improvement in 90 days" is specific and verifiable
• Narrative-only case studies with no summary — AI engines need a concise, extractable summary; bury the result in a 2,000-word narrative and it may never be extracted

Sources

• schema.org/Article
• Princeton GEO Paper (2024)