تقييم العملاء المحتملين وكشف النية
يراقب النشاط عبر الأنظمة اللي يظهرون فيها العملاء المحتملين — زيارات الموقع، تنزيل المحتوى، فتح الإيميلات، استفسارات الدعم، تسجيلات التجربة — وينتج درجة محدّثة باستمرار لكل عميل محتمل: كم احتمال يكون مناسب، كم متفاعل، وش يبدو مهتم فيه. يبرز العملاء ذوي الدرجات العالية للمبيعات بترتيب الأولوية، ويعلّم الحسابات اللي أنماط تفاعلها توحي بنية شراء نشطة. يعوّض الفرز الحدسي للعملاء المؤهّلين تسويقياً بشي قابل للقياس.
المتطلبات تصف قدرات يحتاجها النمط في بيئتك، مو الموردين اللي لازم تشتريهم. أي نظام يملأ متطلباً يحقّقه — وهذا اللي يخلّي الكتالوج قابلاً للنقل عبر الذيل الطويل من أدوات الشركات الصغيرة والمتوسطة.
lead_record_storeWhere lead records live. The pattern writes scores back to these records so they're visible to sales reps in their normal working surface.
- CRM with lead records
- marketing automation system with lead lifecycle
- internal lead database
engagement_event_streamStream of behavioral events: page visits, content downloads, email opens, product activity. The raw material the score is computed from.
- analytics platform with event tracking
- marketing automation activity stream
- product usage event log
- combined event bus aggregating multiple sources
firmographic_data_sourceCompany-level attributes that determine fit: industry, size, geography, tech stack. Distinct from engagement; engagement is what they do, firmographics is what they are.
- data enrichment service appended to CRM records
- imported firmographic data from a list provider
- internal company profiles
score_destinationWhere the score becomes visible to sales reps. Inside their existing working surface, not a separate dashboard they have to remember to open.
- field on the lead record in the CRM
- lead queue sorted by score in the sales tool
- Slack alerts for high-score changes
score_definition_inputsWhat 'good' means for this client. The pattern doesn't invent scoring criteria; it operationalizes ones the client provides.
- ICP document maintained by marketing leadership
- scoring configuration in a small admin UI
- historical conversion data the pattern learns from
intent_signal_externalExternal buying-intent signals beyond what the client's own systems see. Optional but powerful.
- third-party intent data feed
- manual addition of competitor or industry trigger lists
- news monitoring system surfacing relevant company events
- 01Continuously ingest engagement events as they happen
engagement_event_stream - 02For each affected lead, fetch firmographic data and any external intent signals
firmographic_data_sourceintent_signal_external - 03Compute fit subscore from firmographics matched against ICP definition
score_definition_inputs - 04Compute engagement subscore from recent activity weighted by recency and signal strength
score_definition_inputs - 05Compute intent subscore from buying-pattern signals: research-heavy visits, multiple stakeholders engaging, trial usage
- 06Combine into overall score; write back to lead record
lead_record_store - 07If a lead crosses a threshold or jumps significantly, surface to the score destination with a recent-change indicator
score_destinationقرار Only surface on threshold crossings or large changes; constant updates create alert fatigue.
مخرجات منظّمة ينتجها هذا النمط. أنماط ثانية وأنظمة العملاء تقدر تشترك فيها، وهكذا يتركّب الكتالوج مع الوقت.
qualified_lead_pipelineStream of leads crossing into qualified status, with reason codes.
- sales rep alerting
- marketing attribution dashboards
- pipeline forecasting
scoring_calibration_signalPer-lead score vs. eventual conversion outcome, used to tune the model over time.
- scoring model refinement
- monthly RevOps review
icp_drift_signalPatterns where the ICP description doesn't match converted-customer reality, surfaced as candidates for ICP refinement.
- marketing leadership
- annual strategy reviews