Home/ Skills/ linkedin-account-research

general linkedin-account-research

linkedin-account-research

This skill should be used when the user asks to "research accounts on LinkedIn", "use LinkedIn for account research", "research a company on LinkedIn", "do LinkedIn research for ABM", "mine LinkedIn for account data", "research a prospect company on LinkedIn", "use LinkedIn for account intelligence", "do account research using LinkedIn Sales Navigator", "research a target account on LinkedIn", or any variation of using LinkedIn to research target accounts for B2B SaaS sales and ABM.

Download .md

LinkedIn Account Research

LinkedIn is the most accessible source of real-time account intelligence for B2B sales. Unlike enrichment databases (which are snapshots), LinkedIn data is updated by the people themselves. Every employee maintains their own profile. Every company maintains its own page. Hiring signals appear in real time. Org structure is visible through team search. Leadership posts reveal strategic priorities. The data is fresh, self-reported, and free to access.

The principle: LinkedIn research is not "look at the company page for 30 seconds." It's a structured extraction of 5 data layers that produce actionable intelligence for outbound personalization, ABM targeting, and deal preparation. Budget 5-15 minutes per account depending on the tier.

The 5 LinkedIn Research Layers

Layer	What to extract	Where to find it	Time
1. Company overview	Size, growth, industry, HQ, about description	Company page	1 min
2. Team composition	Who works there, by department. Titles, seniority distribution	Company page → People tab	2 min
3. Hiring signals	Open roles, hiring velocity, new departments being built	Jobs tab + recent hires in People tab	2 min
4. Key contacts	Champion, EB, technical evaluator profiles. Tenure, background	People search filtered to the company	3-5 min
5. Content signals	What leaders are posting about. Priorities, opinions, challenges	Activity feeds of key contacts	2-3 min

Total time per account: 5-10 minutes for standard research. 10-15 for deep ABM research.

Layer 1: Company Overview (1 minute)

What to capture from the Company Page

Data point	Where on LinkedIn	What it tells you
Employee count	Company page header	Company size. Growth trajectory (compare to 6 months ago)
Industry	Company page → About	Vertical classification. ICP fit check
HQ location	Company page → About	Geography for routing and timezone
Founded year	Company page → About	Maturity. Startup (< 5 years) vs established
About section	Company page → About	What they do. Who they serve. How they describe themselves
Website URL	Company page	Domain for enrichment and research
Follower count	Company page header	Brand awareness indicator. Small follower count on a large company = new LinkedIn presence
Recent posts (company)	Company page → Posts	What the company is promoting publicly. Product launches, events, milestones

Quick-read the About section

The About section reveals their self-described identity. In 30 seconds, extract:

Who they sell to (their ICP = your context for positioning)
What they do (one sentence you can reference in outbound)
How they describe their growth stage ("fast-growing," "enterprise-grade," "early-stage")

Layer 2: Team Composition (2 minutes)

Mapping the team on LinkedIn

Company page → People tab
  → Filter by Department: Sales, Marketing, Operations, Engineering
  → Filter by Seniority: Director+
  → Note: team size per department, title patterns, recent hires

What team composition reveals

Signal	What it means	Outreach implication
15 SDRs, 0 RevOps	Outbound is running without ops infrastructure	Angle: "15 SDRs with no ops means someone is duct-taping the sequencing layer"
VP Sales + VP Marketing but no CRO	GTM is split between sales and marketing. May lack unified pipeline view	Angle: "Unifying pipeline visibility across sales and marketing"
5 engineers, 1 salesperson	Product-led or founder-led sales	Angle: "When the founder stops selling, the first sales hire needs infrastructure"
Rapid growth (20+ new hires in last 90 days)	Scaling aggressively. Processes breaking at scale	Angle: "Growing this fast usually breaks 3 things in the first 6 months"
New department appearing (first RevOps, first CS team)	Building a new function from scratch	Angle: "First [function] hire is usually the most important tool decision"

Team composition rules

Check the People tab, not just the company page metrics. The company page shows total headcount. The People tab shows who's actually there, their titles, and when they joined
Filter by department relevant to your product. If you sell to sales teams, filter by Sales. Count SDRs, AEs, managers, VPs. This gives you the team size and structure
Note title patterns. If every sales title is "Account Executive" but there's no "SDR" or "BDR," the company may do AE-driven prospecting (different messaging than SDR-driven)

Layer 3: Hiring Signals (2 minutes)

Finding hiring signals on LinkedIn

Company page → Jobs tab
  → View open roles
  → Filter by department (Sales, Marketing, Ops, Engineering)
  → Note: role titles, number of openings, job descriptions

What job postings reveal

Job posting signal	What it means	How to use in outbound
"Head of RevOps" (first RevOps hire)	Building ops from scratch. Will buy 3-5 tools	Per hiring-signals-revops skill
5 SDR roles at once	Aggressive outbound scaling. Need sequencing infrastructure	"5 SDRs starting without infrastructure = 3 months of chaos"
VP Sales (new)	Leadership change. Stack audit incoming	Per leadership-change-signals skill
"Experience with [your competitor]"	They know the category. May be evaluating	"I see you're looking for [competitor] experience. Curious how you're thinking about that choice"
"Experience with [tool you integrate with]"	Their stack includes your integration partner	Reference the integration: "Since you're on [tool], our integration does [specific thing]"
No relevant job postings	Not actively building the team your product serves	Weaker signal. May not be a buying moment. Consider nurture instead of outbound

Hiring research rules

Read the job description, not just the title. The requirements section reveals their tech stack, their priorities, and their gaps. "Experience with Salesforce, HubSpot, and Outreach" tells you their current stack in one line
Check posting date. A posting from 2 months ago that's still open may mean the role is hard to fill (good: you can help them fill the gap with a tool) or the posting is stale (bad: the company may have deprioritized)
Count open roles by department. 10 open roles in sales vs 2 in engineering signals a sales-driven growth phase. This tells you where the company is investing and where your pitch should land

Layer 4: Key Contacts (3-5 minutes)

Finding the right people

Company page → People tab
  → Search by title: "VP Sales" OR "Head of Sales" OR "CRO"
  → Search by title: "RevOps" OR "Sales Operations"
  → Search by title: "Director Marketing" OR "VP Marketing"
  ↓
For each key contact, capture:
  - Name, title, LinkedIn URL
  - Tenure at company (start date)
  - Previous company and role
  - Recent LinkedIn activity (posts, comments)
  - Mutual connections

What to extract per contact

Data point	Where	Why it matters
Title	Profile header	Role mapping. Champion vs EB vs technical evaluator
Tenure (start date)	Experience section	< 6 months = new broom signal. > 3 years = likely loyal to incumbent tools
Previous company	Experience section	If they came from a company that uses your product or competitor, that's context for outreach
Headline (custom)	Profile header	If they wrote a custom headline, it reveals their priorities: "Building the outbound engine at Acme"
About section (first 2 lines)	About	Self-described priorities and goals
Recent posts	Activity tab	What they care about right now. Best personalization source
Mutual connections	Sales Nav or connections tab	Warm intro path. Mention in outreach if strong
Skills and endorsements	Skills section	Tech skills listed confirm stack familiarity

Contact research rules

Find 3-5 contacts per Tier 1-2 account. Champion + EB + technical evaluator minimum. Per buying-committee-mapping skill
Prioritize contacts with recent activity. A contact who posted last week is more likely to respond to outreach than one who hasn't posted in 6 months
Check tenure carefully. A VP Sales who started 2 months ago is a completely different outreach target than one who's been there 5 years. New = buying window. Long tenure = entrenched
Note previous company for every VP+. Where they came from predicts what tools they know and may bring. A VP Sales who came from a company using Outreach may want Outreach at the new company

Layer 5: Content Signals (2-3 minutes)

Mining LinkedIn activity for intelligence

For each key contact:
  → Click their profile → Activity → Posts
  → Scan last 5-10 posts (if they post regularly)
  → Note: topics, opinions, questions, frustrations

What content reveals

Content type	What it signals	Outreach use
Post about a problem your product solves	Active awareness of the pain. Thinking about it publicly	Reference the post directly in your opener
Post about a competitor	They know the category. May be evaluating or using a competitor	Position against the competitor they mentioned
Post about a new initiative (scaling outbound, rebuilding attribution)	Strategic priority. Budget may follow	Reference the initiative: "Your post about rebuilding attribution matches what we see at similar teams"
Question about a tool or process	Actively seeking advice. Open to input	Answer the question in a DM or email. Add value, not a pitch
Post celebrating a win (deal closed, milestone hit)	Good timing. Positive energy. Receptive to new ideas	"Congrats on [win]. Teams that hit milestones like that usually start looking at [next challenge]"
No posts (inactive profile)	Can't mine content signals	Fall back to profile data (headline, about section, tenure, previous company)

Content research rules

Only reference posts from the last 30 days. Older posts feel stale. "Your post from 8 months ago" is creepy, not thoughtful
Reference their opinion, not just the topic. "Loved your post about attribution" is generic. "Your point about attribution being a data problem, not a tool problem" is specific and shows you read it
If they don't post, check what they comment on. Some people don't post but actively comment on others' posts. Their comments reveal interests without them being a "creator"
Don't reference likes. What they published = fair game. What they consumed (likes, shares of others' content) = feels like surveillance

Research Templates by Account Tier

Tier 1 (ABM 1-to-1): 10-15 minutes per account

Layer	Time	Depth
Company overview	2 min	Full about section. Recent company posts. Funding history via Crunchbase
Team composition	2 min	Full department mapping. Count reps, ops, leadership
Hiring signals	2 min	Read job descriptions for relevant roles. Extract stack and priorities
Key contacts	4 min	5+ contacts profiled. Tenure, background, posts. Mutual connections checked
Content signals	3 min	Read 3-5 recent posts per key contact. Extract talking points

Tier 2 (1-to-few / standard outbound): 5-7 minutes per account

Layer	Time	Depth
Company overview	1 min	Size, industry, stage. Quick about-section scan
Team composition	1 min	Quick count of relevant department. Note team size
Hiring signals	1 min	Check for relevant open roles. Note but don't deep-read descriptions
Key contacts	2 min	2-3 contacts. Name, title, tenure. One post reference if available
Content signals	1 min	Check champion's last 3 posts only

Tier 3 (1-to-many / volume outbound): 1-2 minutes per account

Layer	Time	Depth
Company overview	30 sec	Size and industry only
Key contacts	1 min	1 contact. Name, title, email (from enrichment). No deep profiling
Content signals	0 min	Skip. Use segment-level personalization instead

LinkedIn Research Output

The research note (per account)

Account: [Company Name]
Tier: [1/2/3]
Researched: [Date]

Company: [Size] employees. [Industry]. [Stage]. [HQ].
Team: [Relevant dept size]. [Notable hires/gaps].
Hiring: [Relevant open roles or "none"].
Stack: [Known tools from job postings].

Key contacts:
1. [Name] - [Title] - [Tenure] - [One personalization note]
2. [Name] - [Title] - [Tenure] - [One note]
3. [Name] - [Title] - [Tenure] - [One note]

Signals: [What's happening right now that creates urgency]
Angle: [The outreach angle based on this research]

Store this in CRM (account notes field) or in a shared research doc per campaign.

Measurement

Metric	Definition	Target	Frequency
Research coverage	% of Tier 1-2 accounts with completed LinkedIn research	> 80% before outbound starts	Per campaign
Research time per account	Minutes spent researching	5-10 min for Tier 2. 10-15 for Tier 1	Per campaign
Personalization quality (from research)	% of outbound emails with a LinkedIn-sourced personalization token	> 70% for Tier 1-2	Per campaign
Reply rate: researched vs not	Reply rate on emails with LinkedIn research vs without	Researched should be 1.5-2x	Monthly

Anti-Pattern Check

Spending 30 minutes researching a Tier 3 account. Tier 3 accounts get 1-2 minutes of research. Segment-level personalization is sufficient. Deep research is for Tier 1-2 only
Only looking at the company page. The company page shows what the company wants you to see. The people, their posts, and their hiring tell you what's actually happening. Research all 5 layers
Ignoring the People tab. The People tab reveals team size, composition, recent hires, and departures. It's the most underused research source on LinkedIn
Not reading job descriptions. Job postings contain the prospect's tech stack, their priorities, and their gaps in one document. Reading the full description of 1-2 relevant postings takes 2 minutes and provides outreach-grade intelligence
Referencing stale content. "Your post from last year about pipeline" is not personalization. It's archaeology. Reference content from the last 30 days only
Research without an output. 15 minutes of browsing LinkedIn profiles without noting findings is entertainment, not research. Always produce a structured research note that feeds into the email template
Same depth for every account. Tier 1 accounts deserve 10-15 minutes of deep LinkedIn research. Tier 3 accounts deserve 1-2 minutes. Match the effort to the deal value

Want agents that use skill files like this?

We customize skill files for your brand voice and methodology, then run content agents against them.

Book a call

# LinkedIn Account Research

## The 5 LinkedIn Research Layers

| Layer | What to extract | Where to find it | Time |
|-------|----------------|-----------------|------|
| 1. Company overview | Size, growth, industry, HQ, about description | Company page | 1 min |
| 2. Team composition | Who works there, by department. Titles, seniority distribution | Company page → People tab | 2 min |
| 3. Hiring signals | Open roles, hiring velocity, new departments being built | Jobs tab + recent hires in People tab | 2 min |
| 4. Key contacts | Champion, EB, technical evaluator profiles. Tenure, background | People search filtered to the company | 3-5 min |
| 5. Content signals | What leaders are posting about. Priorities, opinions, challenges | Activity feeds of key contacts | 2-3 min |

**Total time per account:** 5-10 minutes for standard research. 10-15 for deep ABM research.

---

## Layer 1: Company Overview (1 minute)

### What to capture from the Company Page

| Data point | Where on LinkedIn | What it tells you |
|-----------|------------------|-------------------|
| Employee count | Company page header | Company size. Growth trajectory (compare to 6 months ago) |
| Industry | Company page → About | Vertical classification. ICP fit check |
| HQ location | Company page → About | Geography for routing and timezone |
| Founded year | Company page → About | Maturity. Startup (< 5 years) vs established |
| About section | Company page → About | What they do. Who they serve. How they describe themselves |
| Website URL | Company page | Domain for enrichment and research |
| Follower count | Company page header | Brand awareness indicator. Small follower count on a large company = new LinkedIn presence |
| Recent posts (company) | Company page → Posts | What the company is promoting publicly. Product launches, events, milestones |

### Quick-read the About section

The About section reveals their self-described identity. In 30 seconds, extract:
- **Who they sell to** (their ICP = your context for positioning)
- **What they do** (one sentence you can reference in outbound)
- **How they describe their growth stage** ("fast-growing," "enterprise-grade," "early-stage")

---

## Layer 2: Team Composition (2 minutes)

### Mapping the team on LinkedIn

```
Company page → People tab
  → Filter by Department: Sales, Marketing, Operations, Engineering
  → Filter by Seniority: Director+
  → Note: team size per department, title patterns, recent hires
```

### What team composition reveals

| Signal | What it means | Outreach implication |
|--------|-------------|---------------------|
| 15 SDRs, 0 RevOps | Outbound is running without ops infrastructure | Angle: "15 SDRs with no ops means someone is duct-taping the sequencing layer" |
| VP Sales + VP Marketing but no CRO | GTM is split between sales and marketing. May lack unified pipeline view | Angle: "Unifying pipeline visibility across sales and marketing" |
| 5 engineers, 1 salesperson | Product-led or founder-led sales | Angle: "When the founder stops selling, the first sales hire needs infrastructure" |
| Rapid growth (20+ new hires in last 90 days) | Scaling aggressively. Processes breaking at scale | Angle: "Growing this fast usually breaks 3 things in the first 6 months" |
| New department appearing (first RevOps, first CS team) | Building a new function from scratch | Angle: "First [function] hire is usually the most important tool decision" |

### Team composition rules

- **Check the People tab, not just the company page metrics.** The company page shows total headcount. The People tab shows who's actually there, their titles, and when they joined
- **Filter by department relevant to your product.** If you sell to sales teams, filter by Sales. Count SDRs, AEs, managers, VPs. This gives you the team size and structure
- **Note title patterns.** If every sales title is "Account Executive" but there's no "SDR" or "BDR," the company may do AE-driven prospecting (different messaging than SDR-driven)

---

## Layer 3: Hiring Signals (2 minutes)

### Finding hiring signals on LinkedIn

```
Company page → Jobs tab
  → View open roles
  → Filter by department (Sales, Marketing, Ops, Engineering)
  → Note: role titles, number of openings, job descriptions
```

### What job postings reveal

| Job posting signal | What it means | How to use in outbound |
|-------------------|---------------|----------------------|
| "Head of RevOps" (first RevOps hire) | Building ops from scratch. Will buy 3-5 tools | Per hiring-signals-revops skill |
| 5 SDR roles at once | Aggressive outbound scaling. Need sequencing infrastructure | "5 SDRs starting without infrastructure = 3 months of chaos" |
| VP Sales (new) | Leadership change. Stack audit incoming | Per leadership-change-signals skill |
| "Experience with [your competitor]" | They know the category. May be evaluating | "I see you're looking for [competitor] experience. Curious how you're thinking about that choice" |
| "Experience with [tool you integrate with]" | Their stack includes your integration partner | Reference the integration: "Since you're on [tool], our integration does [specific thing]" |
| No relevant job postings | Not actively building the team your product serves | Weaker signal. May not be a buying moment. Consider nurture instead of outbound |

### Hiring research rules

- **Read the job description, not just the title.** The requirements section reveals their tech stack, their priorities, and their gaps. "Experience with Salesforce, HubSpot, and Outreach" tells you their current stack in one line
- **Check posting date.** A posting from 2 months ago that's still open may mean the role is hard to fill (good: you can help them fill the gap with a tool) or the posting is stale (bad: the company may have deprioritized)
- **Count open roles by department.** 10 open roles in sales vs 2 in engineering signals a sales-driven growth phase. This tells you where the company is investing and where your pitch should land

---

## Layer 4: Key Contacts (3-5 minutes)

### Finding the right people

```
Company page → People tab
  → Search by title: "VP Sales" OR "Head of Sales" OR "CRO"
  → Search by title: "RevOps" OR "Sales Operations"
  → Search by title: "Director Marketing" OR "VP Marketing"
  ↓
For each key contact, capture:
  - Name, title, LinkedIn URL
  - Tenure at company (start date)
  - Previous company and role
  - Recent LinkedIn activity (posts, comments)
  - Mutual connections
```

### What to extract per contact

| Data point | Where | Why it matters |
|-----------|-------|---------------|
| Title | Profile header | Role mapping. Champion vs EB vs technical evaluator |
| Tenure (start date) | Experience section | < 6 months = new broom signal. > 3 years = likely loyal to incumbent tools |
| Previous company | Experience section | If they came from a company that uses your product or competitor, that's context for outreach |
| Headline (custom) | Profile header | If they wrote a custom headline, it reveals their priorities: "Building the outbound engine at Acme" |
| About section (first 2 lines) | About | Self-described priorities and goals |
| Recent posts | Activity tab | What they care about right now. Best personalization source |
| Mutual connections | Sales Nav or connections tab | Warm intro path. Mention in outreach if strong |
| Skills and endorsements | Skills section | Tech skills listed confirm stack familiarity |

### Contact research rules

- **Find 3-5 contacts per Tier 1-2 account.** Champion + EB + technical evaluator minimum. Per buying-committee-mapping skill
- **Prioritize contacts with recent activity.** A contact who posted last week is more likely to respond to outreach than one who hasn't posted in 6 months
- **Check tenure carefully.** A VP Sales who started 2 months ago is a completely different outreach target than one who's been there 5 years. New = buying window. Long tenure = entrenched
- **Note previous company for every VP+.** Where they came from predicts what tools they know and may bring. A VP Sales who came from a company using Outreach may want Outreach at the new company

---

## Layer 5: Content Signals (2-3 minutes)

### Mining LinkedIn activity for intelligence

```
For each key contact:
  → Click their profile → Activity → Posts
  → Scan last 5-10 posts (if they post regularly)
  → Note: topics, opinions, questions, frustrations
```

### What content reveals

| Content type | What it signals | Outreach use |
|-------------|----------------|-------------|
| Post about a problem your product solves | Active awareness of the pain. Thinking about it publicly | Reference the post directly in your opener |
| Post about a competitor | They know the category. May be evaluating or using a competitor | Position against the competitor they mentioned |
| Post about a new initiative (scaling outbound, rebuilding attribution) | Strategic priority. Budget may follow | Reference the initiative: "Your post about rebuilding attribution matches what we see at similar teams" |
| Question about a tool or process | Actively seeking advice. Open to input | Answer the question in a DM or email. Add value, not a pitch |
| Post celebrating a win (deal closed, milestone hit) | Good timing. Positive energy. Receptive to new ideas | "Congrats on [win]. Teams that hit milestones like that usually start looking at [next challenge]" |
| No posts (inactive profile) | Can't mine content signals | Fall back to profile data (headline, about section, tenure, previous company) |

### Content research rules

- **Only reference posts from the last 30 days.** Older posts feel stale. "Your post from 8 months ago" is creepy, not thoughtful
- **Reference their opinion, not just the topic.** "Loved your post about attribution" is generic. "Your point about attribution being a data problem, not a tool problem" is specific and shows you read it
- **If they don't post, check what they comment on.** Some people don't post but actively comment on others' posts. Their comments reveal interests without them being a "creator"
- **Don't reference likes.** What they published = fair game. What they consumed (likes, shares of others' content) = feels like surveillance

---

## Research Templates by Account Tier

### Tier 1 (ABM 1-to-1): 10-15 minutes per account

| Layer | Time | Depth |
|-------|------|-------|
| Company overview | 2 min | Full about section. Recent company posts. Funding history via Crunchbase |
| Team composition | 2 min | Full department mapping. Count reps, ops, leadership |
| Hiring signals | 2 min | Read job descriptions for relevant roles. Extract stack and priorities |
| Key contacts | 4 min | 5+ contacts profiled. Tenure, background, posts. Mutual connections checked |
| Content signals | 3 min | Read 3-5 recent posts per key contact. Extract talking points |

### Tier 2 (1-to-few / standard outbound): 5-7 minutes per account

| Layer | Time | Depth |
|-------|------|-------|
| Company overview | 1 min | Size, industry, stage. Quick about-section scan |
| Team composition | 1 min | Quick count of relevant department. Note team size |
| Hiring signals | 1 min | Check for relevant open roles. Note but don't deep-read descriptions |
| Key contacts | 2 min | 2-3 contacts. Name, title, tenure. One post reference if available |
| Content signals | 1 min | Check champion's last 3 posts only |

### Tier 3 (1-to-many / volume outbound): 1-2 minutes per account

| Layer | Time | Depth |
|-------|------|-------|
| Company overview | 30 sec | Size and industry only |
| Key contacts | 1 min | 1 contact. Name, title, email (from enrichment). No deep profiling |
| Content signals | 0 min | Skip. Use segment-level personalization instead |

---

## LinkedIn Research Output

### The research note (per account)

```
Account: [Company Name]
Tier: [1/2/3]
Researched: [Date]

Company: [Size] employees. [Industry]. [Stage]. [HQ].
Team: [Relevant dept size]. [Notable hires/gaps].
Hiring: [Relevant open roles or "none"].
Stack: [Known tools from job postings].

Key contacts:
1. [Name] - [Title] - [Tenure] - [One personalization note]
2. [Name] - [Title] - [Tenure] - [One note]
3. [Name] - [Title] - [Tenure] - [One note]

Signals: [What's happening right now that creates urgency]
Angle: [The outreach angle based on this research]
```

Store this in CRM (account notes field) or in a shared research doc per campaign.

---

## Measurement

| Metric | Definition | Target | Frequency |
|--------|-----------|--------|-----------|
| Research coverage | % of Tier 1-2 accounts with completed LinkedIn research | > 80% before outbound starts | Per campaign |
| Research time per account | Minutes spent researching | 5-10 min for Tier 2. 10-15 for Tier 1 | Per campaign |
| Personalization quality (from research) | % of outbound emails with a LinkedIn-sourced personalization token | > 70% for Tier 1-2 | Per campaign |
| Reply rate: researched vs not | Reply rate on emails with LinkedIn research vs without | Researched should be 1.5-2x | Monthly |

---

## Anti-Pattern Check

- Spending 30 minutes researching a Tier 3 account. Tier 3 accounts get 1-2 minutes of research. Segment-level personalization is sufficient. Deep research is for Tier 1-2 only
- Only looking at the company page. The company page shows what the company wants you to see. The people, their posts, and their hiring tell you what's actually happening. Research all 5 layers
- Ignoring the People tab. The People tab reveals team size, composition, recent hires, and departures. It's the most underused research source on LinkedIn
- Not reading job descriptions. Job postings contain the prospect's tech stack, their priorities, and their gaps in one document. Reading the full description of 1-2 relevant postings takes 2 minutes and provides outreach-grade intelligence
- Referencing stale content. "Your post from last year about pipeline" is not personalization. It's archaeology. Reference content from the last 30 days only
- Research without an output. 15 minutes of browsing LinkedIn profiles without noting findings is entertainment, not research. Always produce a structured research note that feeds into the email template
- Same depth for every account. Tier 1 accounts deserve 10-15 minutes of deep LinkedIn research. Tier 3 accounts deserve 1-2 minutes. Match the effort to the deal value