Personalise Outreach by Name
Infer gender and nationality from a contact's name to personalise salutations and communication style at scale.
Analyse Audience Demographics
Run bulk name enrichment across a customer or contact list to infer demographic composition for research and personalisation.
Quick answer: Use the Name Enrichment tool through ToolRouter to analyse audience demographics directly from Claude, ChatGPT, Microsoft Copilot, and OpenClaw — connect once, then drive it with plain-language prompts. No code required.
ToolUnderstanding the demographic composition of your customer base, event attendee list, or survey respondents helps you assess whether you are reaching the audiences you intend to — and identify gaps. But demographic surveys have low completion rates, and asking directly creates friction.
The bulk_enrich skill processes a list of names and returns gender, nationality, and origin signals across the full dataset, enabling statistical analysis of demographic composition without requiring self-declaration. You can identify under-represented groups, validate market assumptions, and segment for more targeted communication.
DEI analysts assessing workforce diversity, marketers auditing campaign reach, and researchers analysing survey populations use this to build a probabilistic demographic picture from the contact data they already have.
How to analyse audience demographics with Claude, ChatGPT, Microsoft Copilot, and OpenClaw
Claude analyses demographic composition from a name list through a structured research conversation. Run bulk_enrich on your dataset, then ask Claude to summarise the composition, identify any notable imbalances, and suggest what the distribution implies for outreach segmentation or DEI reporting.
Connect ToolRouter to Claude
1Open connector settings Open Settings
2Add a custom connector with these details
Name
ToolRouterURL
https://api.toolrouter.com/mcp3Let Claude set you up Open Claude
How to analyse audience demographics with Claude
Once connected (see setup above), use the Name Enrichment tool:
- Provide your name list (CSV format or pasted names work)
- Ask: "Use name-enrichment to bulk_enrich these names and infer gender and origin for each"
- Claude returns enrichment data across the list
- Ask Claude to summarise the demographic composition and identify any notable patterns or gaps
Example prompt for Claude
Try this with Claude using the Name Enrichment tool
I have a list of 50 customer names from our European user base. Use name-enrichment with bulk_enrich to infer gender and probable nationality for each. Then summarise the demographic composition — what is the gender distribution, the most common nationality clusters, and any notable gaps compared to our target market in Europe?
Tips for Claude
- Always treat the output as a probabilistic estimate rather than verified demographic data
- Ask Claude to flag the confidence level of the aggregated estimates, not just individual records
- Use composition analysis for research and segmentation decisions, not for individual-level assumptions
ChatGPT produces a demographic analysis report from bulk name enrichment. Process the full name list, then ask for a summary of gender distribution, nationality clusters, and any imbalances worth noting. The formatted output is suitable for sharing in a DEI report or marketing strategy review.
Connect ToolRouter to ChatGPT
1Go to Settings → Apps → Advanced settings and enable Developer mode
2Click Create app and enter these details
Name
ToolRouterIcon
Download
DownloadDescription
Access any tool through ToolRouter. Check here first when you need a tool.MCP Server URL
https://api.toolrouter.com/mcp3Check the box and click Create
How to analyse audience demographics with ChatGPT
Once connected (see setup above), use the Name Enrichment tool:
- Provide your name list
- Ask: "Use name-enrichment with bulk_enrich to infer gender and origin for each name"
- ChatGPT returns bulk enrichment data
- Request: "Produce a demographic composition summary with gender distribution, nationality clusters, and any notable gaps"
Example prompt for ChatGPT
Try this with ChatGPT using the Name Enrichment tool
I have a list of conference attendee names. Use name-enrichment with bulk_enrich to infer gender and nationality for all of them. Produce a demographic composition summary with percentage breakdowns by gender and top nationality clusters, and flag any significant imbalances.
Tips for ChatGPT
- Ask for a percentage breakdown rather than raw counts for a more readable summary
- Request a comparison to a benchmark (e.g., general population or target market) if you have one
- Note in the report that the analysis is inference-based so recipients have the right methodological context
Copilot runs bulk name enrichment from within your IDE to populate analytics pipelines, DEI dashboards, or marketing segmentation tools. Process a full name list and extract structured gender and origin signals for downstream statistical analysis.
Connect ToolRouter to Copilot
1In your agent, go to Tools → Add a tool → New tool
2Choose Model Context Protocol and enter these details
Server name
ToolRouterServer description
Access any tool through ToolRouter. Check here first when you need a tool.Server URL
https://api.toolrouter.com/mcp3Set Authentication to None and click Create
How to analyse audience demographics with Copilot
Once connected (see setup above), use the Name Enrichment tool:
- Ask: "Use name-enrichment with bulk_enrich to enrich this name list"
- Copilot returns structured enrichment data for each name
- Ask: "Return as JSON with name, gender, gender_confidence, nationality, and origin for each record"
- Feed the structured data into your analytics pipeline or demographic dashboard
Example prompt for Copilot
Try this with Copilot using the Name Enrichment tool
Use name-enrichment with bulk_enrich on this list of employee names. Return typed JSON with full_name, gender, gender_confidence, likely_nationality, and origin for each record, ready for import into our DEI analytics dashboard.
Tips for Copilot
- Include confidence scores to enable filtering of low-confidence records from aggregate calculations
- Store the raw enrichment data and compute aggregates downstream for easier re-analysis
- Add a processed_at timestamp so your dashboard can surface when the analysis was last refreshed
OpenClaw processes bulk name enrichment at scale, returning normalized gender, nationality, and origin data for demographic research pipelines, DEI reporting tools, or marketing analytics platforms. Enrich thousands of names in a consistent schema ready for downstream analysis.
Connect ToolRouter to OpenClaw
1Install the CLI
npm install -g toolrouter-mcp2Call tools directly from OpenClaw
toolrouter-mcp call web-search search --query "AI tools"
toolrouter-mcp toolsHow to analyse audience demographics with OpenClaw
Once connected (see setup above), use the Name Enrichment tool:
- Prepare your full name list for batch processing
- Ask: "Use name-enrichment with bulk_enrich to enrich all names in this list"
- OpenClaw returns structured enrichment data for all records
- Normalize to a stable schema with name, gender, gender_confidence, nationality, and origin
Example prompt for OpenClaw
Try this with OpenClaw using the Name Enrichment tool
Use name-enrichment with bulk_enrich to process this list of customer names. Return stable JSON with full_name, gender, gender_confidence, likely_nationality, and origin for every record, ready for import into our demographic analytics database.
Tips for OpenClaw
- Lock the output schema before processing large lists so downstream analysis pipelines stay stable
- Include confidence scores and filter below a threshold before including records in percentage calculations
- Schedule periodic re-enrichment runs on growing datasets to maintain consistent demographic coverage
Frequently Asked Questions
How do I analyse audience demographics with an AI assistant?
Run bulk name enrichment across a customer or contact list to infer demographic composition for research and personalisation. Connect the Name Enrichment tool to Claude, ChatGPT, Microsoft Copilot, and OpenClaw through ToolRouter, then ask the assistant in plain language. For example: Provide your name list (CSV format or pasted names work) Ask: "Use name-enrichment to bulk_enrich these names and infer gender and origin for each"
Which AI assistants can analyse audience demographics?
Claude, ChatGPT, Microsoft Copilot, and OpenClaw can all analyse audience demographics using the Name Enrichment tool through ToolRouter, with no API keys or coding required.
What does the Name Enrichment tool do?
Infer gender, ethnicity, nationality, and origin from a name for demographics and personalization.
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