Audience Analysis

Analyze and understand your audience using Apify Actors to extract follower demographics, engagement patterns, and behavior data from multiple platforms.

When to Use

• You need audience demographics, engagement patterns, or follower behavior from social platforms.
• The task is to choose and run Apify Actors for audience analysis across Facebook, Instagram, YouTube, or TikTok.
• You need structured extraction plus a summarized interpretation of audience findings.

Prerequisites

Verify prerequisites before any paid run.

• .env file with APIFY_TOKEN
• Node.js 20.6+ (for native --env-file support)
• An approved Apify connector or Console access to inspect the chosen Actor and its schema; the bundled runner uses REST directly.
• Keep .env untracked and private; configure the token locally without pasting it into chat or command arguments.

Workflow

Copy this checklist and track progress:

Task Progress:
- [ ] Step 1: Identify market research type (select Actor)
- [ ] Step 2: Fetch Actor schema via an approved connector or Console
- [ ] Step 3: Ask user preferences (format, filename)
- [ ] Step 4: Run the analysis script
- [ ] Step 5: Summarize findings

Step 1: Identify Audience Analysis Type

Select the appropriate Actor based on analysis needs:

Step 2: Fetch Actor Schema

Inspect the current Actor details and input schema using the authenticated Apify connector available in the session, or its Console page. Discover actual tool names and parameters before calling them. Do not parse .env with shell substitution or pass a token as a CLI header argument. Treat the Actor table as discovery candidates, not verified availability or schemas.

This returns:

• Actor description and README
• Required and optional input parameters
• Output fields (if available)

Step 3: Ask User Preferences

Before running, ask:

1. Output format:

• Quick answer - Display top few results in chat (no file saved)
• CSV - Bounded export, union of returned fields
• JSON - Bounded export in JSON format

1. Number of results: Based on character of use case

Step 4: Run the Script

Quick answer (display in chat, no file):

node --env-file=.env "<resolved-skill-directory>/reference/scripts/run_actor.js" \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT'

CSV:

node --env-file=.env "<resolved-skill-directory>/reference/scripts/run_actor.js" \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.csv \
  --format csv

JSON:

node --env-file=.env "<resolved-skill-directory>/reference/scripts/run_actor.js" \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.json \
  --format json

Step 5: Summarize Findings

After completion, report:

• Number of results found
• File location and name
• Key market insights
• Suggested next steps (deeper analysis, validation)

Error Handling

APIFY_TOKEN not found - Ask user to create .env with APIFY_TOKEN=your_token Connector unavailable - Inspect the schema through the Console; do not claim an unavailable tool was called. Actor not found - Check Actor ID spelling Run FAILED - Ask user to check Apify console link in error output `T