SalesSDRbeginner 20 min saved

B2B Case Study LinkedIn Hook Generator

Generate five professional, high-converting LinkedIn hooks for your case studies in seconds.

Customise & copy

Add the context you have. The prompt updates as you work.

Customer Industry or Company Profile *

The industry or type of company featured in the case study.

Core Problem or Pain Point Solved *

The main challenge the customer faced before using the product.

Key Metric, Result, or ROI Achieved *

The primary quantifiable outcome or ROI.

Product or Solution Category *

The category of the product or solution used.

Live prompt

4 fields

Act as an expert B2B copywriter. Write exactly five distinct LinkedIn hooks for a customer case study based on the inputs below. Each hook must test a different messaging angle (e.g., metric-first, contrarian, problem-agitation, before-and-after, peer-comparison). Keep each hook under two lines. Use a professional, anti-hype tone free of clickbait, deceptive claims, politeness padding, or conversational filler. Rely strictly on the provided information without fabricating metrics or details.

Customer Industry/Profile: Enterprise SaaS
Core Problem/Pain Point: High customer churn during onboarding due to manual setup processes.
Key Metric/Result: Reduced onboarding time by 60% and increased retention by 25%.
Product/Solution Category: Customer Onboarding Automation Platform

Proof before you use it

A real example

Tested on gemini-3.1-flash-lite on 2026-09-02

{
  "key_metric": "Reduced onboarding time by 60% and increased retention by 25%.",
  "core_problem": "High customer churn during onboarding due to manual setup processes.",
  "customer_profile": "Enterprise SaaS",
  "solution_category": "Customer Onboarding Automation Platform"
}

A small ritual that works

How to use it

01Input your customer profile, core problem, key metric, and solution category.
02Run the prompt to generate five distinct messaging angles.
03Select the hook that best aligns with your current campaign strategy.
04Copy, paste, and add your case study link.

Why it works

The prompt enforces a strict structural constraint (exactly five hooks under two lines using specific angles) and relies strictly on provided template inputs without fabricating data.

Where it fails

The model outputs (Test Output A and Test Output B) both fail by including introductory filler text outside the five hooks and inventing unsupplied causal claims, peer comparisons, and financial impacts not present in the input variables.

Customisation tips

Ensure the prompt explicitly forbids introductory filler text in the system instructions and tightly constrains the generation to map strictly to the provided input variables without inferring external claims.

Watch-outs

Do not allow models to add conversational filler or introductory sentences when an exact item count is requested.
Watch out for models extrapolating unverified causal relationships ('primary cause', 'primary driver') or comparative peer benchmarks ('most companies...') that are absent from the input data.

Works in

ChatGPTClaudeGemini

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