AI Advantage Assessment

What makes for a worthwhile AI tech case study?
I read 50 case studies on AI in Marketing in last month (on top of the hundreds I read/tested at Informa TechTarget). Vendors are missing the mark on case studies .

What’s wrong with many AI in Marketing case studies.
Many read like the worst type of AI content – Clickbait titles, filled with AI generalities and irrelevant customer details. (e.g. multiple paragraphs of customer’s business history). They lack the relevant detail that a prospect expects.

• Some seem to think the game has shifted to “reviews” (e.g. G2, Capterra, TrustRadius), stars and comment volume. Review content is a relevant part of buying processes, but delivers a tiny fraction of what prospects want from a case study.
  
  I get the difficulties. Permissions are brutal. Each customer has a narrow view of the value delivered. Clients are reluctant to give ROI numbers. If legal gets involved, it’s game over. But case studies are the first thing prospects ask BDR for. That shows how important they are AND that prospects haven’t been able to find them.
  
  What makes for a great case study?
  Detailed customer quotes from roles involved in the implementation process – Prospects are looking for “people like them” to share their product experiences. Many case studies won’t have a customer quote or the person quoted doesn’t align to the majority of prospects doing research. Most web research is done by people at Director level and below, so a quote from a CEO/CMO can have less relevancy in a case study.
  
  
• Initial pain point – Many case studies have no discussion of the pain point or discuss a corporate point (e.g. efficiency). Case study readers are trying to understand if a solution really aligns to their pain. If the case study isn’t specific about what triggered the buy, readers will feel the solution to be less relevant.
• Tech and implementation details – Everyone wants their solution to appear simple. But enterprises know architecture, implementation and integration are part of a solution building and need be considered. A competitor’s case study with those details will be regarded more highly. Amazon Web Services (AWS) ML blog does this well https://lnkd.in/eH56nFjD.
• Grounded, detailed benefit statements – Numbers are hard to come by, but case studies with granular descriptions of the functional benefit are more credible than numbers that project out 3 year productivity gains. This level of detail matters to prospects and shows your solution has in-market relevancy.
• Customer Video clip- In the great-to-have category. Quotes are effective, but a customer that is willing to have their face/voice associated to your solution shows a prospect that the customer really believes in your solution (way more than any quick review. It feels like they are staking their reputation on your solution (and that is what prospects fear about new products).
  
  Let me know of good AI in Marketing case studies