sales-prospect-list

What this skill does

# Build a Targeted Prospect List

Help the user build a targeted B2B prospect list — from ICP definition through filter strategy, list sizing, segmentation, and quality checks. This skill is platform-agnostic but references Apollo.io as the primary tool. The same frameworks apply to ZoomInfo, LinkedIn Sales Navigator, Clay, Lusha, or any data provider.

## Step 1 — Gather context


If `references/learnings.md` exists, read it first for accumulated knowledge.

Ask the user:

1. **Who are you trying to reach?**
   - A) I have a clear ICP — I know the titles, industries, and company sizes
   - B) I have a rough idea — need help refining it
   - C) Starting from scratch — help me define who to target
   - D) I have an existing list that needs filtering/cleaning

2. **What's the campaign goal?**
   - A) Cold outbound — net-new pipeline generation
   - B) ABM — targeted outreach to named accounts
   - C) Event follow-up — building a list around a conference/webinar
   - D) Expansion — finding new contacts at existing customer accounts
   - E) Competitive displacement — targeting competitors' customers
   - F) Other — describe it

3. **What data provider(s) do you have access to?**
   - A) Apollo.io
   - B) LinkedIn Sales Navigator
   - C) ZoomInfo
   - D) Clay
   - E) Lusha / Cognism / other
   - F) Just a CRM with existing data
   - G) None yet — recommend one

4. **How many prospects do you need?** (rough target — 100? 500? 5,000?)

**If the user's request already provides most of this context, skip directly to the relevant step.** Lead with your best-effort answer using reasonable assumptions (stated explicitly), then ask only the most critical 1-2 clarifying questions at the end — don't gate your response behind gathering complete context.

## Step 2 — Define the ICP

Build a structured ICP using four dimensions:

### Firmographic filters (company-level)
| Filter | Questions to ask | Example values |
|---|---|---|
| **Industry** | What industries are your best customers in? | SaaS, Financial Services, Healthcare |
| **Company size** | How many employees? What revenue range? | 50-500 employees, $10M-$100M revenue |
| **Geography** | Where are your target companies? | US, UK, DACH region |
| **Funding stage** | Do you target by funding? | Series A-C, bootstrapped, public |
| **Technology** | What tech stack signals a good fit? | Uses Salesforce, runs Kubernetes, has React in stack |

### Demographic filters (person-level)
| Filter | Questions to ask | Example values |
|---|---|---|
| **Job title** | What titles do you sell to? | VP Engineering, Head of Sales, CTO |
| **Seniority** | What level of decision-maker? | Director+, VP+, C-suite |
| **Department** | Which function? | Engineering, Sales, Marketing, Finance |
| **Job function** | What do they actually do? | People management, IC, operations |

### Behavioral/signal filters
| Filter | Questions to ask | Example values |
|---|---|---|
| **Hiring signals** | Are they hiring for roles your product replaces/supports? | Posting for SDRs, data engineers |
| **Funding** | Recent funding that creates budget? | Raised Series B in last 6 months |
| **Job changes** | New leaders who bring new tools? | Started VP role in last 90 days |
| **Intent** | Researching topics related to your product? | Intent topics in your category |
| **Tech install** | Recently adopted complementary/competitive tech? | Added Snowflake, removed competitor |

### Negative filters (who to exclude)
- Existing customers
- Companies in active deals
- Do-not-contact list
- Companies too small or too large
- Industries you don't serve
- Geographies you can't support

## Step 3 — Build the list

### Filter strategy by persona

For each persona in the ICP, design the filter combination:

**Primary persona** (highest priority):
- Filters: [specific combination]
- Expected list size: [estimate]
- Quality check: Does every record on this list make sense as a prospect?

**Secondary persona** (if applicable):
- Filters: [adjusted combination]
- Expected list size: [estimate]

### List sizing guidance

| Campaign type | Recommended list size | Why |
|---|---|---|
| Hyper-targeted ABM | 25-100 | Quality over quantity — deep personalization per account |
| Focused outbound | 200-500 | Manageable for Level 3+ personalization |
| Scaled outbound | 500-2,000 | Balanced scale with Level 2 personalization |
| High-volume outbound | 2,000-10,000 | Volume play — Level 1-2 personalization, A/B testing at scale |

The right size depends on your personalization capacity. A list of 5,000 is useless if your team can only personalize 50 emails per week.

For platform-specific list-building workflows, search filters, import/export procedures, and API prospecting instructions, see `references/platforms.md`.

## Step 4 — Segment and prioritize

Split the list into tiers for sequencing:

| Tier | Criteria | Volume | Approach |
|---|---|---|---|
| **Tier 1 — Hot** | Matches ICP + has buying signal (intent, job change, funding) | 10-20% of list | Highest personalization, multi-channel, fastest follow-up |
| **Tier 2 — Warm** | Matches ICP, no signal but strong fit | 30-40% of list | Standard personalization, proven sequence |
| **Tier 3 — Broad** | Partial ICP match, worth testing | 40-50% of list | Template-based, A/B test messaging |

### Lead qualification scoring

Use a weighted scoring model to rank prospects numerically, not just by tier. This gives you a concrete starting point that you can tune based on conversion data.

| Criterion | Weight | What to score | Example: 80+ = Tier 1 |
|---|---|---|---|
| **Title/role match** | 30% | How closely does the prospect's title match your buyer persona? | VP Engineering = 30, Director = 20, Manager = 10 |
| **Company size fit** | 25% | Does the company fall within your ICP's employee/revenue range? | Sweet spot = 25, adjacent = 15, edge = 5 |
| **Industry match** | 20% | Is the company in a target industry? | Primary industry = 20, adjacent = 10, other = 0 |
| **Role recency** | 15% | How recently did they start this role? (New leaders buy) | <90 days = 15, 90-365 days = 10, >1 year = 5 |
| **Buying signals** | 10% | Intent data, hiring signals, funding, tech changes | Active signal = 10, weak signal = 5, none = 0 |

**Total: 100 points max.** Tier 1 = 80+, Tier 2 = 50-79, Tier 3 = 30-49, Skip = <30.

Tune these weights for your motion: outbound-heavy teams should increase company size and industry weight; signal-rich teams (PLG, intent-driven) should increase buying signals weight. Review and recalibrate quarterly using actual reply/meeting rates per score band.

### Personalization angles

Capture personalization angles during list building — not later at outreach time. This bridges the gap between list building and sequence writing and makes the handoff to `/sales-cadence` much smoother.

| Tier | What to capture | Why it matters |
|---|---|---|
| **Tier 1 — Hot** | Recent company news, specific tech stack observations, mutual connections, recent job change context, funding details | These prospects get the most personalized outreach — you need concrete hooks |
| **Tier 2 — Warm** | Industry pain points, company growth stage, hiring patterns, competitive tool usage | Personalization at the segment level — "companies like yours" not "your specific situation" |
| **Tier 3 — Broad** | Industry vertical, company size band, persona-level pain points | Template-level personalization — enough to feel relevant, not enough to be bespoke |

For each Tier 1 prospect, note at least 2 personalization angles during list building. For Tier 2, note 1 per prospect or 1 per segment. Tier 3 gets segment-level angles only.

### Segmentation variables

Beyond tiers, consider segmenting by:
- **Industry** — messaging should reflect industry-specific pain points
- **Persona** — a VP of Engineering and a CTO need different messaging
- **Company size** — SMB and enterprise have different buying processes
- **Geography** — timezone, langua