| name | meta-lead-scoring |
| description | Designs and calibrates a lead-scoring model that distinguishes high-probability buyers from low-probability contacts, enabling clients to prioritise sales follow-up and improve conversion rates. Invoke when a client generates more leads than their team can follow up with equally, or when they need to demonstrate to sales stakeholders that marketing is delivering qualified leads. Sources: Kahan (2022); Zahay et al. (2024).
|
Lead Scoring Playbook
Use when
- Designs and calibrates a lead-scoring model that distinguishes high-probability buyers from low-probability contacts, enabling clients to prioritise sales follow-up and improve conversion rates. Invoke when a client generates more leads than their team can follow up with equally, or when they need to demonstrate to sales stakeholders that marketing is delivering qualified leads. Sources: Kahan (2022); Zahay et al. (2024).
- Use this skill when it is the closest match to the requested deliverable or workflow.
Do not use when
- Do not use this skill for graphic design, video production, software development, or legal advice beyond the repository's stated scope.
- Do not use it when another skill in this repository is clearly more specific to the requested deliverable.
Workflow
- Collect the required inputs or source material before drafting, unless this skill explicitly generates the intake itself.
- Follow the section order and decision rules in this
SKILL.md; do not skip mandatory steps or required fields.
- Review the draft against the quality criteria, then deliver the final output in markdown unless the skill specifies another format.
Anti-Patterns
- Do not invent client facts, performance data, budgets, or approvals that were not provided or clearly inferred from evidence.
- Do not skip required inputs, mandatory sections, or quality checks just to make the output shorter.
- Do not drift into out-of-scope work such as code implementation, design production, or unsupported legal conclusions.
Outputs
- A structured audit, report, model, or analytical framework in markdown, with decisions and recommendations tied to evidence.
References
- Use the inline instructions in this skill now. If a
references/ directory is added later, treat its files as the deeper source material and keep this SKILL.md execution-focused.
- Use
premium-social-selling/SKILL.md when scoring premium, executive, enterprise, luxury/affluent, or high-ticket leads. In those cases, weight fit, authority, buying trigger, referral path, response quality, and meeting readiness above raw engagement volume.
Required Inputs
Ask for the following before generating any output:
- Business name — trading name of the client
- Industry — sector and niche
- Country / city — default is Uganda/East Africa
- Primary goal — what lead scoring must achieve (prioritise sales follow-up, demonstrate marketing ROI, reduce sales team time wasted on unqualified leads, improve conversion rate)
- Ideal customer profile — describe the best customers in terms of company size, role, industry, location, and budget level (for B2B) or demographic and behaviour (for B2C)
- Sales team capacity — how many qualified lead conversations the sales team can manage per week
- Current CRM or contact management tool — what system stores lead data (spreadsheet, CRM, email platform)
- Available data — what information is currently captured for each lead at the point of entry
What Lead Scoring Is
Lead scoring is a numerical model that assigns point values to leads based on two dimensions:
- Explicit attributes — who the lead is (demographic and firmographic characteristics)
- Implicit behaviours — what the lead has done (signals of intent and engagement)
A lead whose total score crosses a defined threshold is forwarded to the sales team for immediate follow-up. A lead below the threshold re-enters the nurture sequence and accumulates score over time through further engagement. This ensures that the sales team's time is spent on the highest-probability buyers — not distributed equally across every contact regardless of intent.
Explicit Scoring Criteria (Demographic / Firmographic)
These criteria describe the lead's fit with the ideal customer profile. A perfect explicit score indicates the lead looks like the best existing customers.
B2B scoring model:
| Criterion | Condition | Points |
|---|
| Job title / authority | Director, owner, or MD | 20 |
| Manager or department head | 10 |
| Executive or coordinator | 5 |
| Unknown | 0 |
| Company size | Within target range | 20 |
| Adjacent (slightly outside range) | 10 |
| Outside target range | 0 |
| Industry | Primary target sector | 20 |
| Secondary sector | 10 |
| Out of scope | −10 |
| Budget indication | Confirmed budget aligned to pricing | 30 |
| Estimated budget in range | 15 |
| No indication | 0 |
| Location | Within service geography | 10 |
| Outside service geography | −5 |
B2C scoring model (adapt as required):
| Criterion | Condition | Points |
|---|
| Age range | Primary target demographic | 15 |
| Adjacent demographic | 5 |
| Outside demographic | 0 |
| Income or spending level | High-value segment | 20 |
| Mid-range segment | 10 |
| Budget segment (if out of scope) | −5 |
| Location | Target city or region | 10 |
| Outside target region | −5 |
Customise both models for the client's specific ideal customer profile. Do not apply the template without adapting the criteria to the client's actual product, pricing, and geography.
Implicit Scoring Criteria (Behavioural)
These criteria measure what the lead has done. Behavioural signals are more powerful predictors of conversion than demographic fit alone — a contact who has visited the pricing page twice in one week is more likely to buy than a perfectly profiled lead who has never engaged with any content.
| Behaviour | Points | Rationale |
|---|
| Downloaded a lead magnet | 10 | Showed enough interest to exchange contact details |
| Attended a webinar or live event | 20 | High-intent action; invested time in the brand |
| Visited the pricing page (if trackable) | 25 | The single strongest behavioural signal available |
| Opened 3 or more emails in the nurture sequence | 15 | Consistent engagement with content |
| Clicked a link in an email | 10 | Moved beyond passive reading to active interest |
| Replied to an email or WhatsApp message | 20 | Initiated a two-way conversation |
| Visited the website 3 or more times in 7 days | 20 | Research behaviour indicates active consideration |
| Requested a consultation, quote, or call | 40 | Explicit buying signal — highest single behavioural score |
| Shared content or referred another contact | 15 | Advocacy behaviour; high engagement |
| Attended an in-person event or demo | 25 | Significant time and interest investment |
Score Decay
Apply score decay to prevent stale leads retaining artificially high scores. A lead who was highly engaged six months ago but has not opened an email or visited the website since is not a hot prospect — they are a disengaged contact who should be in a re-engagement sequence, not on the sales team's priority list.
Decay rule: Deduct 10 points from the lead's score for every 30 days of inactivity.
| Inactivity Period | Score Deduction |
|---|
| 30 days | −10 points |
| 60 days | −20 points |
| 90 days | −30 points (trigger re-engagement sequence) |
| 120 days | −40 points (review for list removal) |
In practice, apply score decay as a monthly batch update. Most CRM tools and email platforms support automated score adjustment based on inactivity rules.
Threshold Calibration with Sales
Do not set the scoring threshold in isolation. The threshold must be agreed with the sales team before the model goes live — and reviewed after 60 days of operation.
Calibration process:
- Propose an initial threshold (recommended starting point: 60 points out of a maximum of approximately 175 points in a standard B2B model)
- Run the model for 60 days without adjusting the threshold
- At the 60-day review, ask the sales team:
- Are the leads arriving above the threshold genuinely qualified?
- Are there leads below the threshold that the sales team considers worth pursuing?
- What is the conversion rate of threshold-crossing leads compared with the previous period?
- Adjust the threshold up or down based on the answers
- Repeat the review at 90 days and then quarterly
The scoring model is not a formula — it is a calibration conversation between marketing and sales, mediated by data. A model that marketing imposes on the sales team without their input will be ignored.
BANT Qualification Layer
Apply BANT as a secondary qualification check for all leads that cross the scoring threshold. A lead with a high score that fails BANT should be returned to nurture, not forwarded to sales.
BANT framework:
| Dimension | Question | Signal |
|---|
| Budget | Has the lead indicated or implied a budget that aligns with the product pricing? | Confirmed = pass; absent = enquire before forwarding |
| Authority | Is this person a decision-maker or a recommender? | Decision-maker = pass; recommender = flag for multi-stakeholder approach |
| Need | Is there a stated or clearly implied business problem this product or service addresses? | Explicit need = pass; vague interest = return to nurture |
| Timeframe | Is there a defined decision date, a project deadline, or an urgency signal? | Defined timeframe = pass; open-ended = lower priority |
A lead that crosses the scoring threshold and passes all four BANT dimensions is a Marketing Qualified Lead (MQL) — ready for sales handover. Document the BANT assessment in the CRM record at the point of handover.
East Africa Application
For most EA clients — particularly SMEs with limited CRM data — a simple scoring model with five to eight criteria is more effective than a complex one. Begin with the four highest-signal behaviours and add criteria as data quality improves.
Recommended starter model for EA clients:
| Criterion | Points |
|---|
| Attended a webinar or live event | 20 |
| Visited the pricing page | 25 |
| Replied to a WhatsApp message or email | 20 |
| Requested a consultation or quote | 40 |
| Suggested threshold | 40 points |
At a threshold of 40 points, a single consultation request triggers immediate sales follow-up — as it should. Two or more moderate signals in combination also cross the threshold. Review and expand the model at the 60-day calibration.
Output: Lead Scoring Design Document
Generate the following for the client:
- Explicit scoring table — customised to the client's ideal customer profile; all criteria and point values documented
- Implicit scoring table — behavioural criteria relevant to the client's channels and content types
- Score decay rule — stated clearly with monthly deduction schedule
- Initial threshold recommendation — with rationale
- 60-day calibration protocol — questions for the sales review meeting and adjustment methodology
- BANT qualification checklist — to be completed by the sales team for every lead crossing the threshold
- CRM implementation notes — how to configure scoring in the client's existing tool (or recommendation for a simple spreadsheet model if no CRM is in use)
- Monthly review report template — lead score distribution, threshold crossings, MQL-to-deal conversion rate
Quality Criteria
Output meets the standard when:
- Scoring model is built collaboratively with the client's sales team — the threshold and criteria are agreed, not imposed by marketing alone
- Both explicit (demographic or firmographic) and implicit (behavioural) criteria are included in the model — a model with only one dimension is incomplete
- Score decay is applied — the model reduces scores for inactive leads on a monthly schedule; no lead retains a high score indefinitely without recent engagement
- Initial threshold is set, formally reviewed at 60 days, and adjusted based on sales feedback — the model is treated as a living calibration, not a fixed formula
- BANT framework is applied as a secondary check on all leads crossing the threshold before they are handed to the sales team
- The scoring model is documented in a shared spreadsheet or CRM field accessible to both marketing and sales — neither team operates from a version the other cannot see
- Lead score distribution is reviewed monthly as a leading indicator of campaign quality — a shift in score distribution signals a change in lead quality before conversion data is available
References
Kahan, R. (2022) High-Velocity Digital Marketing: 7 Proven Strategies to Send Your Revenue Soaring Using Today's Best Digital Practices. Amplify Publishing.
Zahay, D. et al. (2024) Digital Marketing Management: A Handbook for the Current (or Future) CEO. 3rd edn. Business Expert Press.
