sales-qualify

> **Templates and analytical tools only - not legal, marketing-compliance, or data-protection advice.** Lead qualification draws on public sources only - never scrape LinkedIn (ToS §8.2), Glassdoor, G2, Capterra, or Crunchbase free-tier. Personal data captured during qualification (named individuals, role, employer) is regulated under GDPR Art. 6 (EU/UK), CCPA/CPRA (CA), and equivalent regimes - surface notice obligations downstream. Champion/economic-buyer identification must be evidence-based; never fabricate names or relationships. # Sales Qualify - Lead Qualification Engine (BANT + MEDDIC) > **Host tools.** This procedure names Wayland's tool set. Map each to whatever this host provides: > `web_extract` → the web-fetch tool, `terminal` → the shell, `execute_code` → a scratch script, > `file_tools.*` → read/write, `delegate_task` → subagents (or run the phases yourself, in order). > Where a helper script such as `analyze_page.py` is named and not present, do that parsing inline. Evaluate a prospect against two proven sales qualification frameworks - **BANT** and **MEDDIC** - using only publicly available information. Produces an Opportunity Quality Score (0-100) and a Lead Grade (A/B/C/D) with a recommended sales approach. Runs standalone via `/sales-qualify <url>` or as the Opportunity dimension subagent during `/sales prospect`. ## When to Use Trigger phrases: "qualify this lead", "BANT score <url>", "MEDDIC analysis on <company>", "is this a real opportunity", "should we pursue <company>", `/sales qualify <url>` (verb form), `/sales-qualify <url>` (flat form). Do NOT use for: deep company background research (`sales-research`), decision-maker contact discovery (`sales-contacts`), competitive landscape analysis (`sales-competitors`), or building an ideal customer profile (`sales-icp`). ## Invocation Modes This skill is **dual-mode**. **Standalone mode** (`/sales-qualify <url>`) - User passes a company URL. - Skill fetches the public surface, runs full Phase 1-4 BANT + MEDDIC analysis, and writes a Markdown report. - Default output path: a dated Markdown file in the workspace - typically `.wayland/business-sales/<timestamp>-<slug>.md`. - Caller may override with an explicit `out_path` argument. **Subagent mode** (invoked by `sales-prospect` via `delegate_task(tasks=[...])`) - Parent orchestrator pre-fetches all pages and passes them in `context.pages`. - Child receives a fully self-contained `context` payload (see *Subagent contract* below) - no parent context leaks otherwise. - Child does NOT re-fetch; it analyzes the structured page data passed in. - Child returns a JSON object matching `output_schema` AND writes a per-dimension Markdown file to the assigned `out_path`. - Toolset for the child is `[terminal, file, web]` - `execute_code` is blocked for delegated subagents, so any helper-script work must already be done by the parent. ## Inputs Standalone mode accepts: - `url` (required) - company website URL - `pages` (optional) - pre-fetched page data `{role: text}` to avoid redundant fetches - `icp_context` (optional) - contents of an existing `IDEAL-CUSTOMER-PROFILE.md` for pain-point and budget calibration - `out_path` (optional) - caller-controlled output path; falls back to a dated Markdown file in the workspace Subagent mode receives in `context`: - `company_url`, `company_name` - `pages` - pre-fetched structured data, e.g. `{homepage, pricing, careers, about, blog, case_studies}` - `external_signals` - pre-fetched data from LinkedIn / Crunchbase / news / G2 (parent runs `web_search` once and embeds results) - `icp_context` - ICP pain-point map, if available - `scoring_rubric` - the rubric below, embedded so child has it without reading parent prompt - `output_schema` - exact JSON shape the child must return - `out_path` - deterministic absolute path the child writes its dimension report to --- ## Phase 1: Data Collection ### 1.1 Primary Data Sources Gather qualification signals from these sources. In standalone mode use `web_extract` for site pages (≤5 URLs per call) and `web_search` for external data (≤5 results per call). For raw text on long pages, use `terminal` + `curl --max-filesize 200000` instead of `web_extract`. In subagent mode, read everything from `context.pages` and `context.external_signals` - do NOT re-fetch. | Source | What to Extract | Qualification Relevance | |--------|----------------|------------------------| | **Pricing page** | Price points, tiers, enterprise tier, "Contact Sales" | Budget signals, deal size potential | | **Careers page** | Open roles, department sizes, growth rate | Budget (hiring = spending), Need (roles reveal pain), Timeline (urgency of hiring) | | **Job postings** | Required tools, skills, responsibilities | Tech stack, pain points, current solutions, budget for tools | | **Blog / Resources** | Pain point topics, challenges discussed, industry trends | Need validation, problem awareness | | **Case studies** | Problems solved, vendors used, results achieved | Need patterns, buying behavior, vendor preferences | | **About page** | Company size, stage, mission, leadership | Authority mapping, budget signals | | **Review sites (G2, Capterra)** - *manual human lookup only; do not scrape (ToS forbid)* | Reviews of their product, reviews they leave for other tools | Current tool satisfaction, switching signals | | **Glassdoor** - *manual human lookup only; do not scrape (ToS forbid)* | Employee reviews mentioning tools, processes, problems | Internal pain points, culture around change | | **LinkedIn** - *Marketing Developer Platform / Sales Navigator API only; ToS §8.2 forbids automated scraping* | Employee count growth, recent hires, leadership posts | Timeline signals, authority mapping, growth trajectory | | **News / Press** | Funding, partnerships, expansions, challenges | Budget signals, timeline triggers, need amplifiers | | **Social media** | Company posts, executive posts, engagement | Problem awareness, vendor sentiment, trigger events | | **Competitor mentions** | References to competing solutions on their site or job posts | Current solutions, competitive landscape | ### 1.2 Signal Extraction Methodology For each data source: 1. **Fetch the source** (parent only) or **read from `context`** (subagent). 2. **Scan for keywords** related to each BANT and MEDDIC dimension. 3. **Classify each signal** as Strong, Moderate, Weak, or Absent. 4. **Record the evidence** - exact quote or paraphrase with source URL. 5. **Assign confidence level** (High, Medium, Low, Inferred). **Confidence level definitions:** | Confidence | Definition | Example | |-----------|-----------|---------| | **High** | Directly stated or clearly observable fact | Pricing page shows $499/mo enterprise tier | | **Medium** | Reasonable inference from available data | 5 open engineering roles suggests growing tech team | | **Low** | Indirect signal requiring interpretation | Blog post about "scaling challenges" suggests growing pains | | **Inferred** | Educated guess based on company profile | Series B company likely has $500K+ annual software budget | --- ## Phase 2: BANT Framework Assessment ### Budget (0-25 points) **What we are assessing:** Does this prospect have the financial capacity and willingness to purchase our solution? **Signal detection:** | Signal | Points | Confidence | Where to Find | |--------|--------|-----------|---------------| | Explicit budget mentioned (rare for public data) | 20-25 | High | RFPs, procurement portals | | Recent funding round (Series A: +12, B: +16, C+: +20) | 12-20 | High | Crunchbase, press releases | | Enterprise pricing tier on their own product | 10-15 | Medium | Their pricing page | | Multiple paid SaaS tools visible in tech stack | 8-12 | Medium | Job posts, integration pages | | Hiring for roles that use your product category | 10-15 | Medium | Job postings | | Employee count suggests adequate budget (50+ employees) | 5-10 | Low | LinkedIn, About page | | Cost-conscious signals (all free tools, tiny team) | 0-3 | Medium | Tech stack, team size | | Recent layoffs or cost-cutting news | 0-5 | High | News, LinkedIn | **Budget scoring rubric:** | Score | Interpretation | |-------|---------------| | 20-25 | Strong budget signals. Recent funding or clear enterprise spend. High confidence. | | 15-19 | Good budget indicators. Company size and tech spend suggest capacity. | | 10-14 | Moderate signals. Budget likely exists but unconfirmed. | | 5-9 | Weak signals. Budget is uncertain. May require creative pricing. | | 0-4 | Poor budget signals. Early stage, cost-conscious, or financial distress. | ### Authority (0-25 points) **What we are assessing:** Can we identify who makes the buying decision, and can we access them? **Signal detection:** | Signal | Points | Confidence | Where to Find | |--------|--------|-----------|---------------| | Economic buyer identified by name and title | 20-25 | High | Team page, LinkedIn | | Org structure visible (clear hierarchy) | 10-15 | Medium | Team page, LinkedIn, org chart | | Decision-making titles found (VP+, C-suite, Director) | 8-12 | Medium | Team page, LinkedIn | | Buying committee roles identifiable | 12-18 | Medium | Org structure, LinkedIn | | Procurement process visible (vendor portal, RFP process) | 5-10 | Medium | Website, job postings | | Flat org / owner-operator (easy authority mapping) | 15-20 | High | Small team, founder-led | | Complex enterprise structure (hard to navigate) | 3-8 | Low | Large company, many layers | | No leadership info publicly available | 0-5 | Low | Insufficient data | **Authority scoring rubric:** | Score | Interpretation | |-------|---------------| | 20-25 | Clear buying authority identified. Direct path to decision maker. | | 15-19 | Key stakeholders identified. Likely buying process understood. | | 10-14 | Some authority figures found. Buying process partially mapped. | | 5-9 | Limited authority visibility. Need discovery call to map. | | 0-4 | Cannot identify decision makers from public data. | ### Need (0-25 points) **What we are assessing:** Does this prospect have a problem that our solution solves, and are they aware of it? **Signal detection:** | Signal | Points | Confidence | Where to Find | |--------|--------|-----------|---------------| | Explicit pain point mentioned (blog, interview, social) | 20-25 | High | Blog, news, social media | | Job posting for role that solves the problem your tool solves | 15-20 | High | Job postings | | Negative reviews of their current solution | 12-18 | Medium | G2, Capterra, social media | | Blog content about challenges you solve | 10-15 | Medium | Company blog | | Competitor product mentioned in job posts | 10-15 | Medium | Job postings | | Industry-wide pain point applicable to their segment | 5-10 | Low | Industry reports, news | | Feature requests on their own product suggest internal needs | 8-12 | Low | Community forums, social | | No visible pain signals | 0-5 | Low | Insufficient data | **Need scoring rubric:** | Score | Interpretation | |-------|---------------| | 20-25 | Clear, validated pain point. Prospect is actively seeking solutions. | | 15-19 | Strong need indicators. Problem is real even if not explicitly stated. | | 10-14 | Moderate need signals. Likely experiencing the problem. | | 5-9 | Weak need signals. Problem may exist but is not a priority. | | 0-4 | No visible need. Solution may be premature for this prospect. | ### Timeline (0-25 points) **What we are assessing:** Is there urgency to buy? What is the likely timeframe for a decision? **Signal detection:** | Signal | Points | Confidence | Where to Find | |--------|--------|-----------|---------------| | RFP or vendor evaluation in progress | 22-25 | High | Procurement portals, news | | Active hiring for role that would use your product | 15-20 | High | Job postings | | Recent trigger event (funding, leadership change, expansion) | 12-18 | Medium | News, press releases | | Budget cycle alignment (fiscal year start, Q4 budget) | 8-12 | Low | Industry norms, fiscal calendar | | Contract renewal cycle (annual contracts up for renewal) | 8-12 | Low | Inferred from industry | | Seasonal buying patterns for their industry | 5-10 | Low | Industry knowledge | | Competitor dissatisfaction signals (recent negative reviews) | 8-12 | Medium | G2, social media | | Rapid growth creating urgency | 10-15 | Medium | Hiring pace, funding, news | | No urgency signals detected | 0-5 | Low | Insufficient data | **Timeline scoring rubric:** | Score | Interpretation | |-------|---------------| | 20-25 | Active buying process or immediate trigger event. Decision within weeks. | | 15-19 | Strong urgency signals. Likely to act within 1-3 months. | | 10-14 | Moderate urgency. Timeframe is 3-6 months. | | 5-9 | Low urgency. Timeframe is 6-12 months or undefined. | | 0-4 | No urgency detected. Long-term nurture candidate. | ### BANT Score Calculation ``` BANT Score = Budget + Authority + Need + Timeline Range: 0-100 ``` --- ## Phase 3: MEDDIC Framework Assessment ### Metrics **What we are assessing:** What business metrics does this prospect care about? What would success look like to them? **Research approach:** 1. Check their homepage for metric claims ("We help companies achieve X") 2. Read case studies for the metrics they highlight 3. Check executive LinkedIn posts for KPIs they discuss 4. Review job postings for OKR/KPI mentions 5. Analyze their product to infer which metrics their customers care about **Output format:** - Primary metrics they likely care about (3-5) - How your solution impacts those metrics - Evidence and confidence level for each ### Economic Buyer **What we are assessing:** Who holds the purse strings? Who gives final approval? **Research approach:** 1. Check team/leadership page for C-suite and VP titles 2. Search LinkedIn for the company + titles like "VP of [relevant department]", "Head of [relevant area]" 3. For SMBs: founder/CEO is almost always the economic buyer 4. For mid-market: VP or Director level in the relevant department 5. For enterprise: May need multiple approvals (VP + Procurement + Legal) **Output format:** - Name and title of likely economic buyer - Evidence for why this person is the economic buyer - Alternative economic buyers if uncertain - Confidence level