lead-scoring

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Lead Scoring

线索评分

Lead scoring is the discipline of quantifying how likely a prospect is to become a paying customer so sales teams spend time on the right people. A good scoring model combines profile fit (does the company match your ICP?) with behavioral intent (are they actively signaling purchase readiness?). This skill equips an agent to define ICPs, build point-based or predictive scoring models, weight intent signals, set MQL/SQL thresholds, implement score decay, and create a shared sales-marketing framework that drives consistent, measurable pipeline qualification.

线索评分是一种量化潜在客户转化为付费客户可能性的方法，帮助销售团队把时间花在高价值客户身上。一个优质的评分模型会结合画像匹配度（该企业是否符合你的ICP？）与行为意向（他们是否表现出明确的购买意愿？）。本技能可协助Agent定义ICP、构建基于分值或预测性的评分模型、加权意向信号、设置MQL/SQL阈值、实现评分衰减机制，并创建一套统一的销售-营销协作框架，推动线索资格认定流程标准化、可衡量。

When to use this skill

适用场景

Trigger this skill when the user:

Needs to define or refine an Ideal Customer Profile (ICP)
Wants to build or overhaul a lead scoring model with point values
Asks how to identify, classify, or weight intent signals (first-party or third-party)
Needs to set MQL, SQL, or PQL thresholds and handoff criteria
Wants to implement score decay for aging or disengaged leads
Asks about BANT, MEDDIC, CHAMP, or any qualification framework
Needs to validate whether a scoring model is actually predicting conversions
Wants to align sales and marketing on lead definitions and SLA terms

Do NOT trigger this skill for:

CRM technical implementation or integration wiring - use a CRM/engineering skill
General demand generation strategy unrelated to lead qualification

当用户有以下需求时，触发本技能：

需要定义或优化理想客户画像（ICP）
希望构建或全面升级带分值的线索评分模型
询问如何识别、分类或加权意向信号（第一方或第三方数据）
需要设置MQL、SQL或PQL的阈值及团队交接规则
想为活跃度下降或沉睡线索实现评分衰减机制
询问BANT、MEDDIC、CHAMP等任何线索资格认定框架
需要验证评分模型是否能有效预测转化
希望对齐销售与营销团队关于线索的定义及SLA条款

请勿在以下场景触发本技能：

CRM技术实施或集成配置 - 请使用CRM/工程类技能
与线索资格认定无关的通用获客策略

Key principles

核心原则

Fit + intent = score - Profile fit answers "should we ever sell to this company?" Intent answers "should we reach out right now?" Neither alone is sufficient. A perfect-fit company with zero intent is a nurture candidate. High intent from a poor-fit company wastes sales cycles. Weight both dimensions and require minimum thresholds on each, not just a combined total.
Decay scores over time - A lead who downloaded a whitepaper six months ago and has not engaged since is not still a hot prospect. Apply time-based decay to behavioral scores so inactivity reduces urgency. Fit scores (firmographic, technographic) typically do not decay; behavioral scores should decay 10-25% per month of inactivity.
Align sales and marketing on definitions - "Marketing Qualified Lead" means nothing if sales uses a different threshold to decide whether to work it. Define MQL, SQL, and PQL in a shared document, tie them to specific score thresholds, and measure SLA compliance. Misalignment here is the single largest source of pipeline leakage.
Start simple, iterate often - Begin with a manual point model covering 8-12 attributes. Get sales and marketing to validate it on historical closed-won data before layering in predictive ML. Complexity that has not been validated destroys trust faster than simplicity.
Validate with closed-won data - Build the model, then score your last 100 closed-won deals and 100 lost/no-decision deals. If the model does not clearly separate the two populations, the attribute weights are wrong. Recalibrate before deploying to live pipeline.

匹配度+意向=评分 - 画像匹配度回答“我们是否应该向这家企业销售？”，意向回答“我们现在是否应该触达？”。单独依靠任何一项都不够：完全匹配ICP但无任何意向的客户属于培育对象；意向很高但匹配度极低的客户会浪费销售周期。需对两个维度分别加权，且要求每个维度都达到最低阈值，而非仅依赖总分。
评分随时间衰减 - 六个月前下载过白皮书且之后无任何互动的线索，不再是高优先级潜在客户。需对行为评分设置时间衰减机制，让活跃度不足的线索优先级自动降低。画像评分（企业基本信息、技术栈信息）通常无需衰减；行为评分建议每月衰减10-25%（针对无互动的线索）。
对齐销售与营销的定义 - 如果销售团队对“营销合格线索（MQL）”的阈值理解与营销团队不同，这个定义将毫无意义。需在共享文档中明确MQL、SQL、PQL的定义，绑定具体的评分阈值，并衡量SLA合规性。双方定义不一致是线索流失的最大原因。
从简入手，持续迭代 - 先从包含8-12个属性的手动分值模型开始。在引入机器学习的预测性模型前，先用历史成单数据让销售和营销团队验证模型。未经验证的复杂模型会比简单模型更快失去信任。
用成单数据验证 - 构建模型后，为最近100个成单线索和100个流失/未决策线索评分。如果模型无法清晰区分这两类群体，说明属性权重设置有误。在上线前重新校准模型。

Core concepts

核心概念

Demographic vs. behavioral scoring are the two axes of every lead score. Demographic (also called profile or fit) scoring assigns points based on static attributes: company size, industry, job title, tech stack, geography, funding stage. These attributes describe who the prospect is. Behavioral scoring assigns points based on actions: page visits, content downloads, email opens, webinar attendance, free trial sign-ups. These attributes describe what the prospect is doing right now. Most models maintain separate fit and behavioral sub-scores and require a minimum threshold on each before routing to sales.

MQL / SQL / PQL definitions are the thresholds that gate handoffs between teams. A Marketing Qualified Lead (MQL) has crossed a score threshold indicating marketing believes it warrants sales attention. A Sales Qualified Lead (SQL) is an MQL that sales has accepted as worthy of active pursuit, typically after a discovery call confirms fit, budget, and timeline. A Product Qualified Lead (PQL) is specific to PLG motions - it is a user (not just a lead) who has reached a product activation milestone that predicts conversion, such as inviting a second user, creating three projects, or integrating with a key tool.

Intent signals taxonomy classifies signals by source and strength. First-party signals come from your own properties (website visits, docs engagement, trial usage, email clicks) and are the highest-confidence because you own the data. Second-party signals come from partner ecosystems (co-marketing events, integration marketplace installs, referral partner activity). Third-party intent signals come from vendors like Bombora, G2, TechTarget, or 6sense - they aggregate content consumption across publisher networks to surface companies researching your category. Rank signals from strongest (pricing page visit, free trial start) to weakest (single blog visit, newsletter open).

Score decay is the mechanism that reduces a lead's behavioral score over time without fresh engagement. Without decay, a lead's score only ever increases, making old engagement permanently inflate priority. Implement decay as a scheduled job (daily or weekly) that multiplies behavioral sub-scores by a decay factor (e.g., 0.9 per week of inactivity). Reset the decay clock when a new qualifying action occurs. Fit scores are not decayed because firmographic attributes do not change frequently.

** demographic评分 vs behavioral评分**是线索评分的两个核心维度。Demographic评分（又称画像匹配度评分）基于静态属性赋值：企业规模、行业、职位头衔、技术栈、地域、融资阶段。这些属性描述潜在客户的“身份”。Behavioral评分基于客户行为赋值：页面访问、内容下载、邮件打开、 webinar参会、免费试用注册。这些属性描述潜在客户“当前的行为”。大多数模型会分别维护匹配度和行为子评分，且要求两个子评分都达到最低阈值后，才会将线索分配给销售团队。

MQL/SQL/PQL定义是团队间线索交接的阈值。营销合格线索（MQL）指达到评分阈值，营销团队认为值得销售团队跟进的线索。销售合格线索（SQL）是销售团队确认值得主动跟进的MQL，通常在发现性通话后确认匹配度、预算和时间线。产品合格线索（PQL）适用于PLG模式——指完成产品激活里程碑（预示高转化可能性）的用户（而非普通线索），比如邀请第二位用户、创建三个项目、集成关键工具等。

意向信号分类按来源和强度对信号进行分类。第一方信号来自自有平台（网站访问、文档互动、试用使用、邮件点击），可信度最高，因为数据由企业自有。第二方信号来自合作伙伴生态（联合营销活动、集成市场安装、推荐伙伴活动）。第三方意向信号来自Bombora、G2、TechTarget、6sense等供应商——他们聚合多个发布商网络的内容消费数据，识别正在研究你的产品类别的企业。信号强度从高到低排序：定价页访问、免费试用启动 > 单次博客访问、 newsletter打开。

评分衰减是在无新互动时降低线索行为评分的机制。如果没有衰减机制，线索评分只会不断上升，导致旧互动记录持续虚高优先级。可将衰减设置为定时任务（每日或每周），将行为子评分乘以衰减系数（例如，每周无互动则乘以0.9）。当有新的合格行为发生时，重置衰减时钟。画像评分无需衰减，因为企业基本属性不会频繁变化。

Common tasks

常见任务

Define an Ideal Customer Profile (ICP)

定义理想客户画像（ICP）

An ICP is a description of the company type (not individual) most likely to buy, retain, and expand. Build it from closed-won analysis, not intuition.

Firmographic criteria:

Industry verticals:      e.g., FinTech, HealthTech, B2B SaaS
Company size (employees): e.g., 50-500
ARR / Revenue range:     e.g., $5M-$50M ARR
Geography:               e.g., North America, EMEA
Funding stage:           e.g., Series A - Series C

Technographic criteria:

Tech stack signals:      e.g., uses Salesforce + Slack (integrates well)
Competitor usage:        e.g., currently on legacy tool X (displacement motion)
Infrastructure:          e.g., AWS/GCP (cloud-native, not on-prem only)

Negative ICP (disqualifiers): Explicitly list company types to reject: e.g., solo-founder, pre-revenue, regulated industries you cannot serve, or geographies you do not support. These should auto-fail leads regardless of behavioral score.

Pull your last 50 closed-won deals and cluster them by firmographic attributes. The cluster with the shortest sales cycle and highest NRR is your ICP. Do not define ICP by who you want to sell to - define it by who actually bought and stayed.

ICP是对最有可能购买、留存并拓展业务的企业类型（而非个人）的描述。需基于成单数据分析构建，而非主观判断。

企业基本信息标准：

行业领域:      例如，金融科技、医疗科技、B2B SaaS
企业规模（员工数）: 例如，50-500人
ARR/营收范围:     例如，500万-5000万美元ARR
地域:               例如，北美、欧洲、中东和非洲
融资阶段:           例如，A轮 - C轮

技术栈信息标准：

技术栈信号:      例如，使用Salesforce + Slack（易于集成）
竞品使用情况:        例如，目前使用 legacy工具X（有替换需求）
基础设施:          例如，AWS/GCP（云原生，而非仅本地部署）

反向ICP（排除标准）： 明确列出需要拒绝的企业类型：例如， solo创始人、无营收、无法服务的受监管行业、不支持的地域。无论行为评分如何，这些线索都应自动被排除。

提取最近50个成单线索，按企业基本属性聚类。销售周期最短、净留存率（NRR）最高的聚类就是你的ICP。不要按“你想卖给谁”定义ICP，而要按“谁实际购买并留存”来定义。

Build a scoring model - point system template

构建评分模型 - 分值系统模板

A point-based model assigns values to attributes. Sum the points to produce a score from 0 to 100. Divide into a fit sub-score (0-50) and a behavioral sub-score (0-50).

Fit scoring template:

Attribute                    | Match             | Points
-----------------------------|-------------------|-------
Industry match               | Exact ICP         | +15
                             | Adjacent          | +8
                             | Outside ICP       | 0
Company size                 | ICP range         | +12
                             | One tier off      | +6
Job title / seniority        | Economic buyer    | +10
                             | Champion / user   | +7
                             | Unrelated         | 0
Technographic signal         | Key tech match    | +8
Funding stage                | ICP stage         | +5
Geography                    | Target region     | +0 (neutral)
                             | Excluded region   | -20 (hard block)

Behavioral scoring template:

Action                       | Points | Decay
-----------------------------|--------|------
Pricing page visit           | +20    | -3/week
Free trial sign-up           | +25    | none (reset point)
Demo request                 | +30    | none (route immediately)
Webinar attendance           | +10    | -2/week
Content download (gated)     | +8     | -2/week
Email click (3 in 7 days)    | +5     | -1/week
Blog visit (single)          | +2     | -1/week
Unsubscribe                  | -15    | permanent
Competitor domain email      | -10    | permanent

MQL threshold: Fit >= 25 AND Behavioral >= 20 (total >= 45) SQL threshold: MQL accepted by sales after discovery (BANT/MEDDIC confirmed)

基于分值的模型为每个属性赋值，总分从0到100。分为匹配度子评分（0-50）和行为子评分（0-50）。

匹配度评分模板：

属性                    | 匹配情况             | 分值
-----------------------------|-------------------|-------
行业匹配               | 完全符合ICP         | +15
                             | 相关行业          | +8
                             | 不符合ICP       | 0
企业规模                 | 符合ICP范围         | +12
                             | 接近ICP范围      | +6
职位头衔/职级        | 决策人    | +10
                             | 支持者/使用者   | +7
                             | 无关职位         | 0
技术栈信号         | 关键技术匹配    | +8
融资阶段                | 符合ICP阶段         | +5
地域                    | 目标区域     | +0（中性）
                             | 排除区域   | -20（直接排除）

行为评分模板：

行为                       | 分值 | 衰减规则
-----------------------------|--------|------
定价页访问           | +20    | 每周-3
免费试用注册           | +25    | 无（重置衰减时钟）
演示请求                 | +30    | 无（立即分配给销售）
Webinar参会           | +10    | 每周-2
 gated内容下载     | +8     | 每周-2
7天内点击3次邮件    | +5     | 每周-1
单次博客访问          | +2     | 每周-1
取消订阅                  | -15    | 永久生效
竞品域名邮箱      | -10    | 永久生效

MQL阈值：匹配度 >=25 且行为评分 >=20（总分>=45） SQL阈值：MQL经销售团队发现性通话确认（符合BANT/MEDDIC标准）后转为SQL

Identify and weight intent signals

识别并加权意向信号

Group signals into tiers before assigning point values:

Tier 1 - Purchase intent (highest weight, route to sales immediately if fit >= 25):

Demo or pricing request (first-party)
Free trial activation (first-party)
ROI calculator completion (first-party)
Third-party intent surge (Bombora/G2) for your exact category

Tier 2 - Solution awareness (medium weight, enroll in fast-track nurture):

Multiple product page visits in 7 days
Case study or comparison guide download
Webinar registration and attendance
Integration marketplace browse or install

Tier 3 - Early research (low weight, standard nurture):

Single blog post visit
Newsletter subscription
Podcast listen or video view
Social media follow or engagement

Third-party intent signals should boost a score but never alone qualify a lead. They confirm category interest, not vendor selection. Combine with first-party engagement before routing to sales.

在赋值前，先将信号分为不同层级：

Tier 1 - 购买意向（最高权重，若匹配度>=25则立即分配给销售）：

演示或定价请求（第一方数据）
免费试用激活（第一方数据）
ROI计算器完成（第一方数据）
第三方意向激增（Bombora/G2）（针对你的产品类别）

Tier 2 - 解决方案认知（中等权重，纳入快速培育序列）：

7天内多次访问产品页
下载案例研究或对比指南
注册并参会Webinar
浏览或安装集成市场工具

Tier 3 - 早期研究（低权重，标准培育序列）：

单次博客访问
订阅新闻通讯
收听播客或观看视频
社交媒体关注或互动

第三方意向信号可提升评分，但不能单独作为线索合格的依据。它们仅表明客户对产品类别有兴趣，而非对你的产品有选择意向。需结合第一方互动数据后，再分配给销售团队。

Set MQL and SQL thresholds

设置MQL和SQL阈值

Thresholds must be agreed by both sales and marketing before launch.

Threshold-setting process:

Score your last 100 closed-won deals with the proposed model
Score your last 100 lost/no-decision deals
Find the score that best separates the two populations (ROC curve / F1 score)
Set the MQL threshold at the point with acceptable false-positive rate for sales
Document the threshold in a shared definition document
Review and recalibrate quarterly

Recommended SLA after MQL:

MQL created     → Sales accepts or rejects within 24 business hours
SQL created     → First meaningful outreach within 4 business hours
Demo completed  → Follow-up sent within 2 business hours

If sales rejects more than 25% of MQLs, the threshold is too low or the fit criteria are wrong. Track MQL rejection reasons - they are the most actionable feedback for recalibrating the model.

阈值必须在上线前经销售和营销团队双方同意。

阈值设置流程：

用待上线模型为最近100个成单线索评分
用同一模型为最近100个流失/未决策线索评分
找到能最清晰区分两类群体的评分阈值（可使用ROC曲线/F1分数）
设置MQL阈值，确保销售团队可接受的误报率
在共享定义文档中记录阈值
每季度回顾并重新校准

推荐MQL后的SLA：

MQL创建     → 销售团队24小时内接受或拒绝
SQL创建     → 4小时内首次有效触达
演示完成  → 2小时内发送跟进邮件

如果销售团队拒绝超过25%的MQL，说明阈值设置过低或匹配度标准有误。跟踪MQL拒绝原因——这是校准模型最有价值的反馈。

Implement score decay

实现评分衰减

Score decay prevents stale behavioral scores from inflating lead priority.

Decay implementation:

-- Pseudocode for weekly decay job
FOR EACH lead WHERE last_behavioral_action > 7 days ago:
  behavioral_score = behavioral_score * 0.85   -- 15% weekly decay
  IF behavioral_score < 5:
    behavioral_score = 0                        -- floor to avoid ghost scores
  total_score = fit_score + behavioral_score
  UPDATE lead record

Decay rate guidance:

Signal type          | Decay rate      | Rationale
---------------------|-----------------|----------------------------------
Single content click | -20%/week       | Low-intent, fades fast
Webinar attendance   | -10%/week       | Higher effort, slower decay
Trial inactivity     | -15%/week       | Active usage is what matters
Demo no-show         | -30% immediate  | Strong disqualification signal
No engagement 90d    | Reset to fit    | Behavioral slate wiped clean

评分衰减可防止陈旧的行为评分虚高线索优先级。

衰减实现方案：

-- 每周衰减任务伪代码
FOR EACH 线索 WHERE 上次互动时间 >7天:
  行为评分 = 行为评分 * 0.85   -- 每周衰减15%
  IF 行为评分 <5:
    行为评分 =0                        -- 最低设为0，避免无效评分
  总分 = 匹配度评分 + 行为评分
  更新线索记录

衰减率参考：

信号类型          | 衰减率      | 原因
---------------------|-----------------|----------------------------------
单次内容点击 | 每周-20%       | 低意向，快速衰减
Webinar参会   | 每周-10%       | 高投入行为，衰减较慢
试用无活动     | 每周-15%       | 活跃使用才是关键
演示缺席         | 立即-30%  | 强烈的不合格信号
90天无互动    | 重置为匹配度评分    | 清除所有行为评分

Validate scoring model against outcomes

用实际结果验证评分模型

Before going live, back-test the model against historical data.

Validation checklist:

Score last 6 months of closed-won deals - average score should be >60
Score same period of closed-lost deals - average score should be <40
Calculate separation ratio: (avg won score - avg lost score) / std dev
Run precision and recall: what % of deals above MQL threshold actually closed?
Identify attributes that are over- or under-weighted by inspecting outliers
Validate with sales: show them the top 20 scored leads, ask if they agree

Healthy model signals:

Metric                         | Target
-------------------------------|------------------
Avg closed-won score           | > 65
Avg closed-lost score          | < 35
MQL-to-SQL conversion          | > 60%
SQL-to-opportunity conversion  | > 40%
MQL rejection rate by sales    | < 20%

上线前，需用历史数据回测模型。

验证清单：

为过去6个月的成单线索评分——平均分应>60
为同期的流失线索评分——平均分应<40
计算区分度：(成单平均分 - 流失平均分)/标准差
计算精准度和召回率：MQL阈值以上的线索实际成单比例是多少？
通过分析异常值，识别权重过高或过低的属性
与销售团队验证：展示评分前20的线索，询问他们是否认可

健康模型指标：

指标                         | 目标值
-------------------------------|------------------
成单线索平均分           | > 65
流失线索平均分          | < 35
MQL到SQL转化率          | > 60%
SQL到商机转化率  | > 40%
销售团队MQL拒绝率    | < 20%

Align sales and marketing on lead handoff SLA

对齐销售与营销的线索交接SLA

Misalignment on definitions and handoff procedures is the most common reason lead scoring fails to improve pipeline. Build a shared definition document covering:

Shared definition document structure:

1. ICP definition (firmographic + technographic criteria)
2. Negative ICP / auto-disqualify criteria
3. Lead lifecycle stages: Raw → Engaged → MQL → SAL → SQL → Opportunity
4. Score thresholds for each stage transition
5. Handoff SLA: who does what and within how long
6. Rejection protocol: how sales rejects an MQL and what reason codes to use
7. Recycling protocol: how rejected/lost leads re-enter nurture
8. Review cadence: monthly score review, quarterly model recalibration

定义和交接流程不一致是线索评分无法提升线索质量的最常见原因。需构建共享定义文档，包含：

共享定义文档结构：

1. ICP定义（企业基本信息+技术栈标准）
2. 反向ICP/自动排除标准
3. 线索生命周期阶段：原始线索 → 互动线索 → MQL → SAL → SQL → 商机
4. 各阶段转换的评分阈值
5. 交接SLA：责任人及时间要求
6. 拒绝流程：销售团队如何拒绝MQL及使用的原因代码
7. 回流流程：被拒绝/流失的线索如何重新进入培育序列
8. 回顾节奏：每月评分回顾，每季度模型校准

Anti-patterns

反模式

Anti-pattern	Why it's wrong	What to do instead
Scoring only behavioral signals	A highly engaged person at a wrong-fit company wastes sales time	Require minimum fit sub-score before behavioral score can trigger MQL
Never decaying scores	Old engagement permanently inflates score; leads from 6 months ago stay "hot"	Apply weekly behavioral decay; reset to fit-only score after 90 days of inactivity
Setting thresholds without data	Arbitrary thresholds (e.g., "score > 50") produce MQL lists sales ignores	Back-test on closed-won vs. closed-lost before launching; set threshold at the empirical separation point
Treating all page visits equally	A pricing page visit is 10x stronger than a blog visit	Tier signals by purchase intent; assign points proportionally
Defining MQL without sales buy-in	Marketing routes leads sales won't work; both teams disengage	Co-define MQL criteria with sales leadership; make sales sign off on thresholds
Ignoring negative signals	Leads who unsubscribe or use competitor emails stay "qualified"	Apply score penalties or hard blocks for disqualifying actions
Building a complex ML model first	Black-box models are hard to debug and lose sales trust	Start with a transparent point model; add ML only after validating the manual model

反模式	问题所在	正确做法
仅对行为信号评分	匹配度极低但互动频繁的客户会浪费销售时间	要求匹配度达到最低阈值后，行为评分才能触发MQL
不设置评分衰减	旧互动记录持续虚高优先级，6个月前的线索仍被标记为高优先级	每周对行为评分设置衰减；90天无互动则重置为匹配度评分
无数据支撑设置阈值	任意设置阈值（如“总分>50”）会导致销售团队忽略MQL列表	上线前用成单vs流失数据回测；基于数据设置阈值
所有页面访问权重相同	定价页访问的意向强度是单次博客访问的10倍	按购买意向对信号分层，按比例赋值
定义MQL时未征求销售同意	营销团队分配的线索被销售团队拒绝，双方积极性下降	与销售领导层共同定义MQL标准，让销售团队签字确认阈值
忽略负面信号	取消订阅或使用竞品邮箱的线索仍被标记为合格	对不合格行为设置评分惩罚或直接排除
先构建复杂的机器学习模型	黑盒模型难以调试，且会失去销售团队的信任	先从透明的手动分值模型开始；验证手动模型后再引入ML

References

参考资料

For detailed content on specific sub-domains, read the relevant file from

references/

```
references/scoring-models.md
```
- Example scoring models for SaaS B2B, PLG, and enterprise motions with full attribute tables and threshold recommendations. Load when building or comparing scoring model templates for a specific GTM motion.

Only load a references file if the current task requires deep detail on that topic.

如需特定子领域的详细内容，请查看

references/

目录下的相关文件：

```
references/scoring-models.md
```
- 适用于SaaS B2B、PLG和企业级业务模式的线索评分模型示例，包含完整的属性表和阈值建议。当为特定GTM模式构建或对比评分模型模板时，可加载该文件。

仅当当前任务需要该领域的深度细节时，才加载参考文件。