recommend-contacts

What this skill does

# Recommended Contacts

Get ML-ranked contact recommendations at a target company, personalized to your ZoomInfo usage and CRM data.

## Input

The user will provide via `$ARGUMENTS`:
- A company name, domain, or ZoomInfo company ID (required)
- Optionally: a use case — "prospecting", "deal acceleration", or "renewal" (defaults to PROSPECTING)
- Optionally: how many results they want (defaults to 25, max 100)

## Workflow

1. **Lookup metadata first** — before calling any other MCP tool, use `lookup` to load reference data for any fields relevant to the request. Use the returned `id` values (not display names) in all subsequent API calls. This ensures accurate parameter resolution and result interpretation.

2. **Resolve the company** if the user provided a name or domain:
   - Use `search_companies` with `companyName` or `companyWebsite` to find the company — use lookup `id` values for any filters.
   - Extract the ZoomInfo company ID from the result.

3. **Enrich the company** using `enrich_companies` with the resolved `companyId` to get firmographic context (industry, size, revenue, business model). This context is used to interpret the recommendations.

4. **Map the use case** to the correct enum value:
   - "prospecting" or default → `PROSPECTING` (based on contacts you've viewed, copied, or exported on the ZoomInfo platform; has cold-start support)
   - "deal acceleration" or "new business" → `DEAL_ACCELERATION` (based on contacts in closed-won CRM opportunities for new business)
   - "renewal", "growth", or "expansion" → `RENEWAL_AND_GROWTH` (based on contacts in closed-won CRM opportunities for renewals)

5. **Get recommendations** using `get_recommended_contacts` with:
   - `ziCompanyId`: the resolved ZoomInfo company ID
   - `useCaseType`: the mapped enum value
   - `pageSize`: user-specified count or 25

6. **Enrich the top contacts** using `enrich_contacts` on the top 10 results (batch of 10) to get full contact details including email, direct phone, and accuracy scores.

## Output Format

### Target Company
One-line summary: [Company Name] — [Industry], [Employee Count] employees, [Revenue], [HQ Location]

### Use Case
State which use case was used and what it means:
- **PROSPECTING**: "Recommendations based on contacts similar to those you've recently viewed, copied, or exported in ZoomInfo."
- **DEAL_ACCELERATION**: "Recommendations based on contact patterns from your CRM's closed-won new business deals."
- **RENEWAL_AND_GROWTH**: "Recommendations based on contact patterns from your CRM's closed-won renewal deals."

### Recommended Contacts

| Rank | Name | Title | Department | Management Level | Email | Direct Phone | Accuracy | Score |
|------|------|-------|------------|-----------------|-------|-------------|----------|-------|
| 1 | | | | | | | | |
| 2 | | | | | | | | |

For each contact, use the `meta` field from the recommendation response to explain WHY they were recommended. The meta describes the reference person the recommendation was based on. Present this as a "Why Recommended" note below the table or as an additional column.

### Recommendation Analysis

Group the recommended contacts by pattern:
- **By Department**: Which departments are most represented? (e.g., "8 of 25 are in Sales, 6 in Marketing")
- **By Seniority**: What management levels dominate? (e.g., "Heavily weighted toward Director and VP")
- **By Function**: What job functions appear most? (e.g., "Strong signal toward revenue-facing roles")

Use the resolved lookup values to categorize accurately — do not guess department or management level labels.

### Engagement Priority

Rank the top 5 contacts to engage first, with reasoning:
- Who has the highest combined relevance (recommendation score) and reachability (accuracy score)?
- Who is the likely entry point vs. the likely decision-maker?
- Suggested outreach sequence

### Next Steps
- Use `/zoominfo:enrich-contact` to deep-dive on any specific person
- Use `/zoominfo:find-buyers` if you need to filter by specific persona criteria beyond what recommendations provide
- If recommendations are sparse, note that PROSPECTING recommendations improve as you use ZoomInfo more (view, copy, export contacts). DEAL_ACCELERATION and RENEWAL_AND_GROWTH require CRM integration.

### Important Notes on Scores
- The `score` (general similarity) and `reRankingScore` (propensity-adjusted) are not directly comparable to each other
- Higher scores indicate stronger fit but do not guarantee response rates
- Recommendations refresh daily based on your latest platform and CRM activity