Case Study Analyzer & Persona Mapper

This workflow is designed to scrape and analyze case studies from a website, organize them by persona, and extract key value propositions. It can be used in multiple ways, from simply as a reference resource for sales to providing access to a database that other AI agents can mine to create hyper-personalized campaigns.

Tool(s) used

Here's how it works:

1. Search Phase:

• Google Search performs a targeted search on the specified website for case studies and customer stories

• Uses site-specific search parameters and excludes blog content

2. Content Collection:

• Iteration processes each search result

• Within each iteration, Web Page Scrape extracts the content from each case study URL

3. Analysis Phase:

• Iteration processes each scraped case study

• Inside this iteration:

  • LLM analyzes each case study using GPT-4 Turbo to extract:

    • Customer name

    • Target persona (from a predefined list)

    • URL

    • Value propositions

    • Key results/ROI

  • Grid Write saves the analyzed data to a grid table

The workflow takes several inputs:

• Page URL: The website to search for case studies

• Num Posts: Number of results to process

• Persona 1-4: List of target personas to categorize the case studies

The final output is stored in a grid table with organized case study data, including customer names, personas, URLs, value propositions, and results.

This workflow effectively automates the process of finding, collecting, and analyzing customer case studies. It organizes them by target persona for easier reference and use.