# Lead Qualification Checklist: What LinkedIn Answers and What to Ask on the Call

A lead qualification checklist that separates fit from readiness – which criteria LinkedIn can answer, what to ask on the call, and how an AI agent checks fit.

A lead qualification checklist answers one practical question: should a rep spend time on this lead now? It works best when it separates two checks that most lists blur together – whether the lead **fits** your customer profile, and whether they are **ready** to buy. This guide gives you a copyable checklist, marks which lines LinkedIn can answer and which only a conversation can, compares the common frameworks, and shows how an AI agent can gather the LinkedIn evidence for you.

> **The short version.** Qualifying a lead means two checks. **Fit** asks whether they could buy: the person's role and time in it, as they report it, and the company's industry, size and location – most of which you can read on LinkedIn. **Readiness** asks whether they are worth working now: a confirmed need, who decides, budget, timing and how the decision gets made – which come from a conversation. Use the checklist below, let an AI agent collect the LinkedIn evidence for each lead with a source behind every claim, treat anything it cannot see as unknown, and review the result before anyone reaches out. A lead is qualified only when it fits and the conversation shows it is ready.

## The lead qualification checklist

Copy this table into your CRM or a sheet and set your own thresholds. The last column says where the answer comes from.

| # | Check | What to look for | Evidence |
| --- | --- | --- | --- |
| 1 | Company fit | Industry, headcount band and location match your customer profile | **LinkedIn answers** – the company's industry, location and headcount on LinkedIn |
| 2 | Role | A title in the function you sell to | **LinkedIn answers** – the title as the person reports it |
| 3 | Seniority | Senior enough to own the problem | **LinkedIn hints** – inferred from the title |
| 4 | Time in role | A recent start, or long enough to have influence | **LinkedIn answers** – experience dates, by month |
| 5 | Disqualifiers | A student, an intern, a "former" title, an employee of a competitor; an existing customer or open deal | **LinkedIn + your CRM** |
| 6 | Need | Posts about the problem, open roles, company specialties – then confirmed on the call | **LinkedIn hints**, **confirm on the call** – a hypothesis until they say it |
| 7 | Authority | Can they buy, or bring in who can? – then confirmed on the call | **LinkedIn hints**, **confirm on the call** – a title proxy and the company's senior people; a title is not purchasing authority |
| 8 | Budget | Money set aside for this problem | **Ask on the call** |
| 9 | Timing | When they need it solved | **Ask on the call** |
| 10 | Decision process | Who else decides, and how | **Ask on the call** |
| 11 | **Fit verdict** | **exclude / fit / strong fit / unknown** | From the LinkedIn evidence in rows 1–7. "Unknown" means a required fit field could not be read |
| 12 | **Readiness** | **advance** with an agreed next step / **nurture** until a named trigger or date / **unresolved** – confirm the missing answer | From the confirmed answers in rows 6–10, after the conversation |

Two rules keep the verdicts honest:

- **A missing budget or timeline does not make fit unknown.** Fit and readiness are separate decisions; a lead can be a strong fit and still need a conversation.
- **A fit verdict alone is not sales qualification.** A lead is qualified when it fits *and* the readiness disposition is "advance" – which requires a confirmed need, a decision maker or an agreed route to one, and answers on budget, timing and process.

LinkedIn does not show revenue on a company page – Sales Navigator offers revenue only as a search filter band – so budget signals always come from the conversation. For mapping everyone involved in a decision, see our [B2B buying committee guide](/guides/b2b-buying-committee); for tiering whole accounts rather than single leads, see [account-based selling](/guides/account-based-selling); and to keep titles and companies current after hand-off, see [CRM hygiene](/guides/crm-hygiene).

## Fit vs qualified: two different questions

HubSpot defines it simply: "Lead qualification is the process of determining whether a lead or prospect is a fit for a product or service." In practice, that splits into two questions answered by different evidence:

| | Fit | Readiness |
| --- | --- | --- |
| **The question** | Could they buy? | Are they worth working now? |
| **Evidence** | Public profile and company data | What they tell you |
| **Who checks it** | A rep, an SDR or an AI agent, before outreach | A rep, in the conversation |
| **Typical outcome** | exclude / fit / strong fit / unknown | advance / nurture / unresolved |

Keeping them apart stops two common mistakes: chasing a perfect-fit lead with no project, and dropping a good fit because nobody has asked about budget yet.

## Lead qualification frameworks compared

| Framework | Letters | Where it comes from | What LinkedIn can evidence |
| --- | --- | --- | --- |
| **BANT** | Budget, Authority, Need, Timeframe | IBM. Its 2008 BANT Criteria Template uses "Timeframe" and warns that "Opportunity records lacking descriptions of a Customer's Budget, Authority, Need and Timeframe (BANT) will be rejected as incomplete." | Budget – no. Authority – a proxy. Need – a hypothesis. Timeframe – no |
| **CHAMP** | Challenges, Authority, Money, Prioritization | Described by HubSpot as a framework that "prioritizes Challenges over Authority" | Challenges – a hypothesis. Authority – a proxy. Money and Prioritization – no |
| **MEDDIC** | Metrics, Economic Buyer, Decision Criteria, Decision Process, Identify Pain, Champion | Developed at PTC in the 1990s ([MEDDICC's history](https://meddicc.com/resources/who-created-meddic)) | Economic Buyer and Champion – candidates only. The rest – no |
| **GPCTBA/C&I** | Goals, Plans, Challenges, Timeline; Budget, Authority; Negative Consequences, Positive Implications | Developed internally at HubSpot | Goals and Challenges – hints at most. The rest – no |

*Our editorial pick: BANT works as a quick gate for transactional deals; MEDDIC suits complex enterprise sales with several decision makers. Whichever you use, the LinkedIn column is the same – it fills in fit, and leaves readiness to the conversation.* Sources: [IBM's BANT template](https://public.dhe.ibm.com/software/pwuploads/BANTTemplate.doc), [HubSpot on qualification frameworks](https://blog.hubspot.com/sales/ultimate-guide-to-sales-qualification) and [GPCTBA/C&I](https://blog.hubspot.com/sales/gpct-sales-qualification).

## Lead qualification questions to ask on the call

These cover the readiness half of the checklist:

- **Need:** What made you look at this now? What happens if nothing changes this quarter?
- **Authority:** Who else will be involved in choosing a solution? Who signs off on a purchase like this?
- **Budget:** Is there budget set aside for this, or would it need to be found? How have you paid for similar tools or services before?
- **Timing:** When do you need this working? Is there a date or event driving that?
- **Process:** What does your evaluation look like from here – a trial, a security review, a procurement step?

Write the answers straight into rows 6–10 – confirming or correcting what LinkedIn hinted for need and authority – and set the readiness disposition before you hang up.

## Outbound vs inbound lead qualification

- **Outbound.** Search narrows the list; then verify the current role, company and remaining fit criteria before the first touch. A standard LinkedIn people search has no company-size filter, so headcount and location still need checking against the company page. The checklist also removes disqualifiers before you write to anyone.
- **Inbound.** The lead arrives with a name, a company and an email address. Find the LinkedIn profile by name and company, then run the same fit checks. Names collide – two people with the same name at different companies are common – so a person confirms the match. There is no lookup from an email address to a LinkedIn profile.

Timing signals that move a lead up the list – a job change, new funding, open roles – are covered in [LinkedIn buying signals](/guides/linkedin-buying-signals).

## Let an AI agent check fit – you review the result

The fit half is research, and an AI agent can do it for a whole list. Connect Claude, Cursor or another MCP client to the Linked API [MCP server](/mcp/overview), or give Claude Code or Codex the agent-friendly [CLI](/cli/getting-started), and give it written rules. Here is a worked example with one illustrative customer profile:

- **Fit requires all of:** a current title in sales leadership or revenue operations (VP, Head or Director of Sales, Sales Operations or RevOps); a current company whose LinkedIn industry is "Software Development" or "IT Services and IT Consulting" (exact labels from our [LinkedIn industry list](/guides/linkedin-industry-list)); a company headcount on LinkedIn of 51–500; a location in the United States or the United Kingdom.
- **Hard exclusions:** works at a named competitor; a student, an intern or a "former" title; an existing customer or open deal in your CRM.
- **Unknown:** a required fit field can't be read – no current position, no company page, no headcount shown or no location. Missing optional evidence, such as no recent posts or no open roles, never fails or blocks fit.
- **Strong fit:** fit, plus at least one extra signal – started the current role 3–24 months ago, the company has open sales roles, or a post in the last 90 days about pipeline, outbound or sales hiring.
- **Readiness:** not confirmed for anyone yet – that needs a conversation.

The customer and open-deal check needs your export, or a CRM tool connected to the same agent – Linked API reads LinkedIn, not your CRM. Here is what a run looks like:

**You:** Here are 30 leads – LinkedIn profile links, plus names and companies for the inbound ones – and our export of current customers and open deals (customers.csv). Check each lead against the rules above. Cite the profile or company field behind every claim. Mark anything you can't see as unknown, write "CRM check not performed" if a lead can't be matched against the export, and leave readiness as "not yet confirmed". Don't contact anyone – show me the table.

**Agent** (used Linked API: fetch_person ×30, fetch_company ×24, search_jobs ×6):
- current title, time in role, location
- industry, headcount on LinkedIn, headquarters
- open sales roles at companies that already fit
- customers.csv matched; 2 leads excluded as existing customers

30 leads checked: 9 strong fit, 12 fit, 4 unknown, 5 excluded. Three examples:

| Lead | Fit evidence | Extra signals | Ask on the call | Fit verdict | Readiness |
| --- | --- | --- | --- | --- | --- |
| VP of Sales, B2B software company | Title "VP of Sales"; industry "Software Development"; 120 employees on LinkedIn; location United States | Started the role 7 months ago; 3 open SDR roles | Budget, timing, who else decides | **Strong fit** | Not yet confirmed |
| Head of RevOps, IT services firm | Title "Head of RevOps"; industry "IT Services and IT Consulting"; location United Kingdom; **no headcount shown** | – | Company size, then the readiness questions | **Unknown** | Not yet confirmed |
| Sales Director at a competitor | Current company is on your competitor list | – | – | **Exclude** | – |

*Nobody is contacted until you review the table.*

*Example run with illustrative leads. Works the same in Claude, ChatGPT, Cursor or Claude Code through the MCP server or the CLI.*

You review the table, correct anything the agent got wrong, and decide who moves on. The agent stops at the table because the prompt tells it to – the pause is an instruction you give, not a built-in approval step – so keep "don't contact anyone" in the prompt. Writing and sending the first message is the next stage of the full workflow in our [AI sales prospecting guide](/guides/ai-sales-prospecting-linkedin).

If you build lists from LinkedIn searches, the ready-made [LinkedIn growth skill](/skills/linkedin-growth) already runs a lighter fit check against your customer profile on search results before it invites anyone. For agent workflows beyond qualification, see the [LinkedIn AI agent guide](/guides/linkedin-ai-agent). Developers can run the same reads in code – see [opening a person page](/docs/action-st-open-person-page) and [opening a company page](/docs/action-st-open-company-page).

## What this check can't tell you

- **Budget, timing and the decision process** – those come from the conversation.
- **Revenue per company** – not shown on company pages; Sales Navigator offers it only as a search filter band.
- **Intent data and technographics** – what a company is researching or which tools it runs are not LinkedIn fields.
- **Email addresses and phone numbers** – finding contact details is a separate enrichment step.
- **Website visitors** – identifying who visited your site is a different category of tool.
- **CRM write-back** – writing verdicts into your CRM is your own integration, or an n8n, Make or Zapier step.
- **Real-time alerts** – today the agent checks when you ask; nothing is pushed to you when a lead changes jobs.

## Frequently Asked Questions (FAQ)

#### What is a qualified lead?

A lead that fits your customer profile and is ready to buy – there is a real need, someone who can decide, and a budget and timeline you have confirmed in a conversation. Fit alone makes a lead worth contacting; readiness makes it worth pursuing now.

#### What is the difference between a lead and a qualified lead?

A lead is anyone who might buy. A qualified lead has passed your checks: they fit the profile you sell to, and a conversation has shown they have the need, authority, budget and timing to move forward.

#### How do you qualify an inbound lead?

Find their LinkedIn profile by name and company, confirm it is the right person, and check fit – role, time in role, company industry, size and location. Then ask the readiness questions on the first call. There is no lookup from an email address to a LinkedIn profile.

#### What is the difference between inbound and outbound leads?

Inbound leads come to you – a form, a demo request, a reply to content. Outbound leads are people you find and contact first. Outbound leads need their fit checked before the first message; inbound leads need it checked before a rep spends time on them.

#### Which lead qualification framework should I use?

BANT is a quick gate for simpler deals; MEDDIC fits complex enterprise sales with several decision makers; CHAMP and GPCTBA/C&I put the buyer's challenges first. All of them depend on a conversation for budget and timing – LinkedIn can only fill in fit.

#### Can AI qualify leads?

An AI agent can do the fit research: read each profile and company, apply your written rules, cite its sources and mark what it cannot see as unknown. Readiness – budget, timing, the decision process – still needs a person to ask. Keep a review step before anyone is contacted.

## Build it in, or hand it to an agent

Build fit checks into your own CRM, routing or scoring with the REST API, the Node and Python [SDKs](/sdks/installation) or the shell [CLI](/cli/getting-started). Or let an AI agent run them out of the box through the [MCP server](/mcp/overview), the agent-friendly CLI or a ready-made [skill](/skills), on your own LinkedIn account. Plans are flat per seat, from $49/mo billed annually – see [pricing](/pricing).

*Facts verified 4 November 2026 – framework definitions checked against IBM's BANT template, HubSpot's published guides and MEDDICC's history of MEDDIC on that date.*