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lead-scoring

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Lead Scoring

销售线索评分

Design, validate, and maintain a model that ranks leads by likelihood to become revenue. The practitioner standard splits scoring into two separate axes:
  • Fit (can they buy): firmographic, demographic, technographic.
  • Engagement (are they about to buy): behavioral, in-product.
This two-axis model appears under several names: Marketo's A-D x 1-4 grade-and-score grid, MadKudu's Customer Fit x Likelihood to Buy, and OpenView's product-qualified lead (PQL) in PLG.
Scoring fails far more often from organizational neglect - no sales sign-off, no recalibration, MQL count treated as the goal - than from bad math, so validation and governance are part of the design here, not an afterthought. The finished score is an input to assignment: routing, territories, and rep matching belong to 
mbfinotti/revops-skills@lead-routing
. Defining the account-fit criteria this skill's fit axis weights - firmographic and technographic ICP tiers - belongs to 
mbfinotti/sales-skills@sales-icp-definition
; this skill owns turning that definition, plus behavioral engagement, into a working score.
The capture-score-route-nurture mechanics are structurally identical for B2B and B2C/PLG. What genuinely differs between B2C/PLG and B2B:
  • Dominant signal source is in-product behavior, not forms and content.
  • No buying committee - one user's behavior can qualify.
  • Cycles run in days, not quarters.
Because of these differences, decay, thresholds, rescoring frequency, and recalibration all run faster in B2C/PLG.
设计、验证并维护一个按成为营收可能性对线索进行排序的模型。从业者标准将评分拆分为两个独立的轴：
  • 拟合度（Fit）（他们能否购买）：公司画像、人口统计、技术画像。
  • 参与度（Engagement）（他们是否即将购买）：行为、产品内行为。
这个双轴模型有多个名称：Marketo 的 A-D x 1-4 等级与评分网格、MadKudu 的客户拟合度 x 购买可能性、以及 PLG 中 OpenView 的产品合格线索（PQL）。
评分失败更多源于组织层面的忽视——没有销售签字确认、没有重新校准、把 MQL 数量当成目标——而不是糟糕的算法，因此验证和治理是这里设计的一部分，而非事后补充。最终评分是分配环节的输入：路由、区域、销售代表匹配属于 
mbfinotti/revops-skills@lead-routing
。定义本 skill 拟合轴所加权的账户拟合标准——公司画像和技术画像 ICP 分层——属于 
mbfinotti/sales-skills@sales-icp-definition
；本 skill 负责将该定义以及行为参与度转化为一个可用的评分。
线索捕获-评分-路由-培育的机制在 B2B 和 B2C/PLG 中结构相同。B2C/PLG 与 B2B 真正不同的地方：
  • 主导信号来源是产品内行为，而不是表单和内容。
  • 没有采购委员会——单个用户的行为就可能使其合格。
  • 周期以天计，而非季度。
由于这些差异，衰减、阈值、重新评分频率、重新校准在 B2C/PLG 中运行得更快。

Interview

访谈

Ask before designing anything. One question per message; offer multiple-choice answers when possible. Skip a question only when the user already gave the answer.
  • Which motion? (a) sales-led B2B, (b) PLG / self-serve with sales assist, (c) B2C transactional.
  • Roughly how many new leads per month, and how many closed-won and closed-lost outcomes exist from the last 12-24 months? (Decides rules-based vs predictive, and how much backtesting is possible.)
  • What is actually on a lead record at creation time - enrichment coverage per fit field, form fields, product events? (A score is downstream of data quality.)
  • Does a model already exist? If yes: redesign, or diagnose first? (Diagnosis path: see Diagnosing a Broken Model.)
  • How many leads can sales actually work per week? (The honest threshold constraint.)
  • Who signs off - which sales leader, and what acceptance-rate floor will they hold the model to?
  • Any EU or consent constraints? Behavioral scoring is profiling under GDPR; the lawful basis must be documented.
  • By what date must the model be scoring live, and what is waiting on it? (A date inside two weeks rules out both enrichment procurement and the conversion-band threshold method; a quarter of runway makes both affordable.)
  • One-off win or compounding asset? (A single campaign list to work stops at v1 weights and a capacity threshold; a compounding mandate is what justifies enrichment, the full backtest, and the recalibration cadence that keep the model honest after launch.)
  • What is the effort ceiling - analyst hours, CRM/marketing-automation admin access, a procurement and legal path for a data contract, and how much of sales' attention you can spend? (No analyst rules out re-deriving weights from won/lost data and the conversion-band method; no procurement path deletes the enrichment rung outright rather than ranking it last.)
Carry these last three answers into every ranking in this skill and in its references; they are the only inputs that reorder the defaults for this user. Every ranking here is a default, not a law: it shifts with context and with who executes it. Re-rank before recommending a rung, for example:
  • An enrichment contract already in place collapses that rung's effort to a field mapping and promotes it to the top.
  • A CRM whose fit fields arrive self-reported on the form at high coverage removes the coverage menu entirely.
  • No analyst promotes every rung a CRM admin can ship alone and demotes every rung that needs a cohort analysis.
在设计任何内容之前先提问。每条消息提一个问题；尽可能提供多选题选项。仅当用户已经给出答案时才跳过问题。
  • 哪种流程？(a) 销售主导 B2B，(b) PLG / 自助服务加销售辅助，(c) B2C 交易型。
  • 每月大约多少新线索，过去 12-24 个月有多少已赢单和已输单结果？（决定基于规则还是预测性，以及可进行多少回测。）
  • 创建时线索记录上实际有什么——各拟合字段的补全覆盖率、表单字段、产品事件？（评分是数据质量的下游。）
  • 是否已有模型？如果有：重新设计，还是先诊断？（诊断路径：参见“诊断失灵模型”。）
  • 销售每周实际能处理多少线索？（诚实的阈值约束。）
  • 谁签字确认——哪位销售负责人，他们要求模型达到什么样的验收率下限？
  • 是否有欧盟或同意（consent）约束？行为评分属于 GDPR 下的画像（profiling），必须记录合法依据。
  • 模型最迟必须于何时上线开始评分，什么在等待它？（两周内上线排除补全采购和转化区间阈值法；一个季度的余量则两者都可负担。）
  • 一次性成果还是可复利资产？（单一营销活动列表只需 v1 权重和容量阈值；复利任务才值得做补全、完整回测和持续重新校准节奏，让模型上线后保持靠谱。）
  • 投入上限是什么——分析师工时、CRM/营销自动化管理员权限、数据合约的采购和法律路径，以及你能占用销售多少注意力？（没有分析师就无法从赢单/输单数据重新推导权重、无法使用转化区间法；没有采购路径就直接删除补全这一级，而不是将其排在最后。）
将这最后三个回答带入本 skill 及其引用中的每一个排序；它们是唯一会为该用户重排默认值的输入。这里的每个排序都是默认值而非法则：随上下文和执行者变化。在推荐某一级之前重新排序，例如：
  • 已有补全合同会将该级的投入压缩为字段映射，并把它提升到最前。
  • 一个 CRM 的拟合字段在表单上以高覆盖率自报，则完全移除覆盖率菜单。
  • 没有分析师会把每个 CRM 管理员可独立上线的级别向上提，并把每个需要队列分析的级别向下压。

Workflow

工作流

  1. Run the Interview; collect every answer before designing.
  2. Audit the data first. Measure enrichment coverage per fit field over the last 90 days of leads. A fit field below roughly 70% coverage means most of the database can never reach the threshold - the enrichment-ceiling failure. Three ways out, ranked by value per unit of effort, where effort is analyst hours, admin work, procurement, and reversibility:
    • efficiency: score unknowns 0 > enrich the field > drop the field
    • value (fit signal the model can actually act on): enrich the field > score unknowns 0 > drop the field
    • effort: enrich the field > drop the field > score unknowns 0
    • compliance cost: enrich the field > score unknowns 0 == drop the field
    Effort in magnitudes:
    • Scoring unknowns 0: near-zero, a scoring-config change reversible in a click.
    • Dropping the field: an hour, and the least reversible option in this menu - the signal stops being collected and the next recalibration cannot re-derive its weight without first rebuilding the history.
    • Enrichment: a procurement and contracting step before a single lead scores, then a standing per-record dependency and a coverage number to monitor forever.
    Default to scoring unknowns 0: it keeps every thin record scoreable on engagement and ships this week. Justify the compliance tie: neither the 0 rule nor the deletion introduces a new data source, so neither triggers a consent or provenance review. Enrichment does - third-party fit data is profiling input under GDPR, and its lawful basis and provenance have to be documented before it scores anyone.
    Name the bias that default buys: scoring unknowns 0 demotes records that are thin for reasons other than fit - SMB, non-US, privacy-conscious buyers - so track their conversion as a separate cohort and re-derive the weight if they convert anyway. What this order starves is enrichment: first on value, first on effort, so a ratio never buys it, and it is the only rung that restores the signal instead of routing around it.
    Promote enrichment when:
    • The field is the ICP gate itself (employee count at enterprise ACV).
    • Coverage is low across the whole database rather than one segment.
    • A contract already exists.
    Delete the enrichment rung outright, and say it is deleted, where the Interview gave no procurement path - a rung parked at the bottom of the list returns as a quarter of vendor calls nobody approved.
  3. Derive signals from outcomes, not intuition.
    • Pull 12-24 months of closed-won and closed-lost records.
    • Compare signal frequency between the two groups.
    • Keep signals with a real conversion delta over baseline.
    The gut list and the evidenced list are usually different lists. Trap: fields that look complete on won deals because sales filled them right before close - verify each signal existed at scoring time.
  4. Build the signal catalogue from references/signal-taxonomy.md: fit and engagement as two separate scores, hard disqualifiers as exclusions (never negative points), soft demotions as negative points, decay on engagement only.
  5. Weight per references/weighting-and-thresholds.md: start near 60/40 fit/engagement for sales-led, 50/50 hybrid, 30-40 fit / 60-70 engagement for PLG and B2C where behavior dominates. Cap each signal category so no single channel can cross the threshold alone.
  6. Set the threshold by sales capacity first, conversion band second, percentile as sanity check - that is the efficiency ranking of the three methods, not just a sequence, and the same reference states each method's value and effort before choosing. Define tiers where each tier maps to a distinct play.
  7. Backtest per references/validation-and-kpis.md: retro-score history, compute conversion by score band, compute the top band's lift over the unscored baseline, run the spot-check holdout set. Iterate signals, weights, and threshold until the pass threshold below clears.
  8. Get written sales sign-off on the signal list, the threshold, and the acceptance floor. Models built without it are the most-cited failure in the field: marketing builds alone; sales rejects half the leads; both blame the other.
  9. Emit the spec (next section), roll out staged - score the live flow before batch-scoring the backlog - and book the v2 review for 60 days post-launch before launching.
  10. If your harness has persistent memory, memorize the approved spec - signals, weights, threshold, version, review date - so later recalibration and diagnosis runs start from it instead of re-interviewing.
  1. 先执行“访谈”；在设计前收集所有回答。
  2. 先审计数据。对过去 90 天线索的每个拟合字段测量补全覆盖率。拟合字段覆盖率低于约 70% 意味着大部分数据库永远无法达到阈值——补全天花板失败。三种解决方式，按单位投入的价值排序，其中投入指分析师工时、管理工作、采购和可逆性：
    • 效率：未知计 0 分 > 补全字段 > 弃用字段
    • 价值（模型实际可利用的拟合信号）：补全字段 > 未知计 0 分 > 弃用字段
    • 投入：补全字段 > 弃用字段 > 未知计 0 分
    • 合规成本：补全字段 > 未知计 0 分 == 弃用字段
    投入量级：
    • 未知计 0 分：接近零，一次点击即可撤销的评分配置变更。
    • 弃用字段：一小时，且是该菜单中可逆性最差的选择——信号停止收集，下次重新校准若不先重建历史数据就无法重新推导其权重。
    • 补全：在任何线索评分之前先有采购和签约步骤，然后是持续的单记录依赖和一个需要永久监控的覆盖率数字。
    默认采用未知计 0 分：它让每条薄记录仍可按参与度评分，并能本周上线。说明合规平局的理由：0 规则和删除都不会引入新数据源，因此都不会触发同意或来源审查。补全会——第三方拟合数据是 GDPR 下的画像输入，在它对任何人评分前必须记录其合法依据和来源。
    指出该默认选择买来的偏差：未知计 0 分会降低那些因拟合以外原因而记录单薄（SMB、非美国、注重隐私的买家）的记录，所以要将其转化作为单独队列跟踪，如果他们仍然转化，则重新推导权重。这个顺序饿死的是补全：价值第一、投入第一，因此一个比值永远不会为它买单，而它是唯一一个恢复信号而非绕开信号的级别。
    在以下情况提升补全：
    • 该字段本身就是 ICP 门（企业 ACV 下的员工数）。
    • 覆盖率低是全网数据库问题，而非某个细分。
    • 合同已存在。
    直接删除补全级别，并明说它被删除了，如果访谈中没有采购路径——停在列表底部的级别会以一堆没人批准的供应商电话的形式回来。
  3. 从结果推导信号，而不是凭直觉。
    • 拉取 12-24 个月的已赢单和已输单记录。
    • 比较两组之间的信号频率。
    • 保留相对基线有实际转化差值的信号。
    直觉列表和有证据列表通常是不同列表。陷阱：赢单交易上的字段看起来完整，因为销售在临关单前才填写——验证每个信号在评分时点是否存在。
  4. 根据 references/signal-taxonomy.md 构建信号目录：拟合度和参与度作为两个独立评分，硬性取消资格作为排除项（永远不是负分），软性降级作为负分，衰减只作用于参与度。
  5. 根据 references/weighting-and-thresholds.md 设置权重：销售主导从接近 60/40 拟合/参与度开始，混合型 50/50，PLG 和 B2C 使用 30-40 拟合 / 60-70 参与度，因为行为主导。对每个信号类别设置上限，使单一渠道不能单独越过阈值。
  6. 先按销售容量设置阈值，其次按转化区间，百分位作为合理性检查——这是三种方法的效率排序，不只是顺序，且同一引用在推荐前给出各方法的价值与投入。定义分层，每层映射到一个明确玩法。
  7. 根据 references/validation-and-kpis.md 进行回测：对历史数据回溯评分，按评分区间计算转化，计算最高区间相对未评分基线的提升，运行抽查留出集。迭代信号、权重、阈值，直到下面通过阈值的要求被满足。
  8. 就信号清单、阈值和验收下限获得销售书面签字确认。没有它的模型是该领域被引用最多的失败：营销独自构建；销售拒绝一半线索；双方互相指责。
  9. 输出规格（下一节），分阶段上线——先对实时流转评分，再批量评历史数据——并在上线前预定上线后 60 天的 v2 评审。
  10. 如果你的 harness 有持久化记忆，记住已批准的规格——信号、权重、阈值、版本、评审日期——以便后续重新校准和诊断运行从它开始，而不是重新访谈。

The Scoring Model Spec

评分模型规格

Deliver every engagement as this artifact - a document the user can hand to marketing ops and sales. Fuller worked versions live in references/examples.md.
SCORING MODEL SPEC - <company/product>, <date>, v<n>
Motion       : sales-led | PLG | B2C - fit/engagement weight split, scale (e.g. 100-pt)
Fit signals  : attribute -> points | data source | coverage % | evidence (won-vs-lost delta)
Engagement   : signal -> points | decay rule | per-category cap | evidence
Exclusions   : hard disqualifiers, excluded outright (competitors, students, customers...)
Negative pts : soft demotions -> points
Threshold    : MQL/PQL score + the capacity math behind it
Tiers        : band -> play (T1 fast human follow-up, T2 queue, T3 nurture...)
Backtest     : period, conversion by band, top-band lift vs baseline, false-negative %
Guardrails   : sales acceptance floor, score-distribution inflation alarm
Governance   : owner, sales sign-off (name, date), v2 review date, change-log location
Three rules about the spec itself:
  • Every point value carries its evidence. "Pricing page 2+ visits +20: converted 2.8x baseline in last-FY won analysis" is reviewable; a bare number is folklore waiting to be gamed.
  • The spec has a pass threshold. In backtest, the top score band must convert to opportunity at at least 2x the unscored all-leads baseline (published guidance only demands "beats no-scoring"; treat 2x as this skill's practical floor, since a thin edge will not survive live noise). The backtest's false-positive rate must also make the agreed sales acceptance floor - 60% is the common practitioner target, a convention rather than a researched constant - plausible. Iterate until both clear; refuse to ship a model that does not beat the unscored baseline.
  • The model is versioned. v1 is a starting position; the v2 review is booked before v1 ships; every change is logged and announced to sales. An unexplainable score kills rep adoption faster than a wrong one.
每次交付都以这个工件交付——一份用户可交给营销运营和销售的文件。更完整的可用版本见 references/examples.md。
SCORING MODEL SPEC - <company/product>, <date>, v<n>
Motion       : sales-led | PLG | B2C - fit/engagement weight split, scale (e.g. 100-pt)
Fit signals  : attribute -> points | data source | coverage % | evidence (won-vs-lost delta)
Engagement   : signal -> points | decay rule | per-category cap | evidence
Exclusions   : hard disqualifiers, excluded outright (competitors, students, customers...)
Negative pts : soft demotions -> points
Threshold    : MQL/PQL score + the capacity math behind it
Tiers        : band -> play (T1 fast human follow-up, T2 queue, T3 nurture...)
Backtest     : period, conversion by band, top-band lift vs baseline, false-negative %
Guardrails   : sales acceptance floor, score-distribution inflation alarm
Governance   : owner, sales sign-off (name, date), v2 review date, change-log location
关于规格本身的三条规则：
  • 每个分值都携带其证据。 “定价页 2+ 次访问 +20：上一财年赢单分析中转化率为基线 2.8 倍”可被审查；裸数字只是等待被操纵的传说。
  • 规格有通过阈值。 在回测中，最高评分区间转化为商机的速率必须至少达到未评分全线索基线的 2 倍（公开指南只要求“优于不评分”；本 skill 将其 2 倍视为实操下限，因为微弱的优势无法在真实噪声中存活）。回测的假阳性率也必须使约定的销售验收下限——60% 是常见从业者目标，是惯例而非经过研究的常数——显得合理。迭代直到两者都满足；拒绝发布一个无法超过未评分基线的模型。
  • 模型有版本。 v1 是起点；v2 评审在 v1 上线前就预定好；每次变更都记录并向销售公告。一个无法解释的评分比错误的评分更快杀死销售代表的采纳。

Diagnosing a Broken Model

诊断失灵模型

When the user arrives with a running model ("our scores are wrong", "sales ignores the score"), check every symptom below before proposing a rebuild - checking is cheap and most broken setups fail two or three, not one. Fixing is where the choice is, so the rows are ordered by value per unit of effort; work them top-down:
  • efficiency: zero the vanity signals > report the right number > add decay > fix the coverage gap > add the missing buying paths > re-derive inflated weights > full recalibration
  • value (opportunities the corrected model wins, or stops losing): full recalibration > add the missing buying paths > re-derive inflated weights > add decay > zero the vanity signals > fix the coverage gap > report the right number
  • effort: full recalibration > re-derive inflated weights > add the missing buying paths > fix the coverage gap > add decay > report the right number > zero the vanity signals
SymptomLikely faultFixEffort
Newsletter devotees outrank real buyersVanity signals carry points (email opens are structurally unreliable since inbox privacy features)Zero or floor low-intent signals; cap the email categoryNear-zero - point values already in the matrix, edited by the model owner alone
MQL count celebrated while pipeline is flatGoodhart dynamic - MQL volume became the target, so the bar drifts downReport MQL-to-opportunity conversion and pipeline contribution upward, never raw MQL volumeNear-zero in hours, but spends political capital with whoever set the MQL target
Stale contacts look as hot as active onesNo decay - points accumulate foreverAdd decay to engagement only (mechanisms ranked in signal-taxonomy)An hour of config, plus one rescore of the database
Most of the database can never reach thresholdEnrichment ceiling - fit fields empty for most recordsMeasure coverage, then apply the ranked menu in Workflow step 2 - score unknowns 0 firstNear-zero for the default rung; a quarter and a standing job if enrichment is promoted
Closed-won deals that never hit MQLFalse negatives - model ignores real buying paths (referrals, events, product usage)Add the missing paths; track false-negative rate as a standing KPIA week - find the path in won records, add signals, re-backtest
MQL volume up, MQL-to-opportunity downScore inflation - generous weights or vanity signalsRe-derive weights from won/lost data; add category caps; raise thresholdA week of analyst work on won/lost cohorts, plus sales re-sign-off on the new bar
High scores stopped predicting winsStale model - ICP, product, or content shifted since calibrationRecalibrate on latest multi-quarter cohort; install drift triggersA quarter to rebuild, then a standing job to keep
What this order starves: the full recalibration - first on value, first on effort, so a ratio never schedules it. Promote it above everything when ICP, pricing, or product changed since the last calibration, or when two rows above have already been fixed and conversion still does not track the score.
Where the Interview gave no analyst, delete the two cohort-analysis rungs - re-deriving weights and the full recalibration - and say they are deleted rather than leaving them at the bottom of a plan nobody can execute. Against inflation, that leaves category caps and a raised threshold, which a CRM admin can ship alone and which buy back most of the precision.
当用户带着一个运行中的模型来（“评分错了”、“销售无视评分”），在建议重建前检查以下每个症状——检查很便宜，而且大多数失灵配置会同时失败两三项，而不是一项。修复才是做选择的地方，因此各行按单位投入价值排序；自上而下处理：
  • 效率：虚荣信号清零 > 报告正确数字 > 增加衰减 > 修补覆盖率缺口 > 增加缺失购买路径 > 重新推导膨胀权重 > 完整重新校准
  • 价值（纠正后的模型赢回或不再失去的机会）：完整重新校准 > 增加缺失购买路径 > 重新推导膨胀权重 > 增加衰减 > 虚荣信号清零 > 修补覆盖率缺口 > 报告正确数字
  • 投入：完整重新校准 > 重新推导膨胀权重 > 增加缺失购买路径 > 修补覆盖率缺口 > 增加衰减 > 报告正确数字 > 虚荣信号清零
症状可能故障修复投入
订阅邮件忠实读者排在实际买家前面虚荣信号带分（邮箱打开在收件箱隐私功能出现后结构上不可靠）将低意图信号清零或设下限；对邮件类别设上限接近零——分值已在矩阵中，仅需模型所有者编辑
MQL 数量被庆祝而管道持平古德哈特动态——MQL 量成为目标，门槛逐渐下移向上汇报 MQL 到商机转化率和管道贡献，绝不汇报原始 MQL 量几小时即可，但会消耗设定 MQL 目标者的政治资本
沉睡联系人和活跃联系人看起来一样热无衰减——分数无限累积仅对参与度增加衰减（机制排序见 signal-taxonomy）一小时配置，加上一次全库重新评分
大部分数据库永远无法达到阈值补全天花板——大部分记录的拟合字段为空测量覆盖率，然后应用工作流第 2 步的排序菜单——先尝试未知计 0 分默认级别接近零；若提升补全则需要一个季度和一项持续工作
已赢单的交易从未达到 MQL假阴性——模型忽略真实购买路径（推荐、活动、产品使用）增加缺失路径；将假阴性率作为常设 KPI 跟踪一周——在赢单记录中找路径，增加信号，重新回测
MQL 量上升，MQL 到商机转化下降分数膨胀——权重过宽或虚荣信号从赢单/输单数据重新推导权重；增加类别上限；提高阈值一周分析师处理赢单/输单队列，加上销售对新门槛的重新签字
高评分不再预测赢单模型陈旧——自校准以来 ICP、产品或内容已变化基于最新多季度队列重新校准；安装漂移触发器重建需要一个季度，然后持续维护
这个排序饿死的是什么：完整重新校准——价值第一、投入第一，所以一个比值永远不会安排它。当上次校准以来 ICP、定价或产品发生变化，或上面两行已修复而转化仍不与评分同步时，将其提升到一切之上。
当访谈显示没有分析师时，删除两个队列分析级别——重新推导权重和完整重新校准——并明说它们被删除，而不是把它们留在一个没人能执行的计划底部。对抗膨胀，剩下类别上限和提高阈值，CRM 管理员可独立上线，并且能买回大部分精确度。