---
id: "framework-persona-research-automation"
type: "framework"
source_timestamps: ["00:11:53", "00:12:11", "00:13:31"]
tags: ["audience-research", "workflow-automation", "prompt-engineering"]
related: ["concept-claude-cowork", "entity-gamma", "entity-ridge-wallet", "concept-inferred-target-personas"]
steps: ["\"Scrape for Reviews: Direct the AI agent to navigate to a target website and scrape a large volume (e.g.", 3, "000-5", "000) of verified customer reviews into a CSV file.\"", "\"Break Data into Personas: Prompt the AI to analyze the CSV and extract core buyer personas. Require the AI to identify emotional triggers", "pain points", "demographics", "and pull 2-3 verbatim quotes per persona.\"", "\"Put Data into Finalized Deck: Feed the synthesized persona document into an AI presentation tool (like Gamma or via Canva connector) to automatically generate a visual", "slide-based research deck.\""]
sources: ["dara"]
sourceVaultSlug: "claude-cowork-creative-strategy-2026May14"
originDay: 6
---
# Automated Persona Research Deck Creation

## Overview

Building comprehensive buyer persona decks traditionally requires days of qualitative research, reading through reviews, and manual formatting. This framework, executed via [[concept-claude-cowork|Claude Cowork]], compresses that into minutes.

## Step 1 — Scrape For Reviews

Direct the AI agent to navigate to a target website and scrape a large volume of **verified customer reviews** into a CSV file.

- Volume target: **3,000–5,000 reviews** (the speaker used 5,000 from [[entity-ridge-wallet|Ridge Wallet]]).
- Output format: structured CSV.
- Prerequisite: [[prereq-chrome-connector|Chrome connector]] enabled so Claude can read rendered pages.

## Step 2 — Break Data Into Personas

Prompt the AI to analyze the CSV and extract core buyer personas. The prompt **must require** the AI to output, per persona:

- A **persona name** (e.g., 'The Upgrader').
- **Demographic data.**
- An **'emotional narrative'** — what triggered the purchase.
- **Core pain points.**
- **2–3 verbatim quotes** from the reviews that encapsulate that persona's experience.

Requiring verbatim quotes is the critical anti-hallucination step: it grounds personas in actual customer voice rather than AI-generated stereotypes.

## Step 3 — Put Data Into Finalized Deck

Feed the synthesized persona document into an AI presentation tool — the speaker uses [[entity-gamma|Gamma]] (or Claude's Canva connector).

- Specify visual requirements (e.g., a **4×4 grid layout** for personas).
- The AI converts the text into a presentation deck automatically.

## Strategic Payoff

This framework compresses days of research and design work into minutes, allowing the strategist to focus entirely on **how to apply the insights** — e.g., comparing these review-based personas against [[concept-inferred-target-personas]] from the brand's ad library to find creative gaps.

## Quality Controls

Per adjacent literature (SUNY, APA, Mammen et al. 2024):

- Spot-check sampled reviews against assigned personas.
- Manually read a sample from each cluster.
- Watch for stereotype drift — verbatim quotes are the safeguard.


## Related across days
- [[arc-content-pipeline-archetypes]]
- [[concept-junior-strategist-paradigm]]
- [[arc-skill-mutability-compounding]]