mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
synced 2026-06-17 23:09:35 +00:00
269 lines
10 KiB
Python
269 lines
10 KiB
Python
"""
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Dealix Proof Pack — monthly evidence-based ROI report per customer.
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Generated automatically at end of every month, exportable as Markdown
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(feeds into PDF). Shows what Dealix did, the leads/meetings/pipeline/revenue,
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benchmark comparisons, top messages, and recommendations for next month.
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Proof Pack = renewal insurance + executive ammunition for the customer's
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internal champion to defend the budget.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from datetime import datetime, timezone
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from typing import Any
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@dataclass
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class ProofPackInputs:
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"""All raw metrics the proof pack consumes."""
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customer_id: str
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customer_name: str
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sector: str
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month_label: str # e.g. "إبريل 2026"
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plan: str # Starter / Growth / Scale
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monthly_price_sar: float
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# Activity
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leads_discovered: int
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leads_enriched: int
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drafts_created: int
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drafts_sent: int
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whatsapp_sent: int
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emails_sent: int
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linkedin_sent: int
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replies_received: int
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positive_replies: int
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meetings_booked: int
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proposals_sent: int
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deals_won: int
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pipeline_added_sar: float
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revenue_won_sar: float
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# Quality
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avg_response_minutes: int
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bounce_rate: float
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opt_outs: int
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compliance_blocks: int
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# Benchmarks (sector p50 from Pulse)
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sector_reply_rate_p50: float
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sector_meeting_rate_p50: float
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sector_win_rate_p50: float
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# Top performers
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best_message_subject: str | None = None
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best_message_reply_rate: float | None = None
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best_sector_played: str | None = None
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worst_bottleneck_ar: str | None = None
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@dataclass
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class ProofPack:
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customer_id: str
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customer_name: str
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period_label: str
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headline_metric: str # one-liner for the cover
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grade: str # A+ / A / B / C / D — pilot's quick read
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tldr_ar: str # 3 lines for executives
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# Sections (each is structured for both UI render + markdown export)
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activity_summary: dict[str, Any]
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pipeline_impact: dict[str, Any]
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quality_score: dict[str, Any]
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benchmark_comparison: dict[str, Any]
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top_performers: dict[str, Any]
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recommendations_next_month_ar: list[str]
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roi_breakdown: dict[str, Any]
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generated_at: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
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def to_markdown(self) -> str:
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lines = [
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f"# Dealix Proof Pack — {self.customer_name}",
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f"**الفترة:** {self.period_label}",
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f"**التقييم:** {self.grade}",
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"",
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f"## TL;DR",
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self.tldr_ar,
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"",
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f"## النشاط",
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*(f"- {k}: {v}" for k, v in self.activity_summary.items()),
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"",
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f"## الأثر على Pipeline",
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*(f"- {k}: {v}" for k, v in self.pipeline_impact.items()),
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"",
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f"## مقارنة بالقطاع",
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*(f"- {k}: {v}" for k, v in self.benchmark_comparison.items()),
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"",
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f"## ROI",
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*(f"- {k}: {v}" for k, v in self.roi_breakdown.items()),
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"",
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f"## التوصيات للشهر القادم",
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*(f"{i+1}. {r}" for i, r in enumerate(self.recommendations_next_month_ar)),
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"",
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f"_Generated by Dealix at {self.generated_at}_",
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]
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return "\n".join(lines)
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# ── Grading — pilot's quick read on the month ────────────────────
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def _grade_month(*, pipeline_sar: float, plan_cost: float, win_count: int) -> str:
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if plan_cost <= 0:
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return "B"
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multiple = pipeline_sar / plan_cost
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if multiple >= 20 and win_count >= 3:
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return "A+"
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if multiple >= 10:
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return "A"
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if multiple >= 5:
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return "B"
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if multiple >= 2:
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return "C"
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return "D"
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def _vs_benchmark_label(actual: float, p50: float) -> str:
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if p50 <= 0:
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return "—"
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if actual >= p50 * 1.5:
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return f"{actual*100:.1f}% (أعلى من المعدل القطاعي بـ 50%+)"
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if actual >= p50 * 1.1:
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return f"{actual*100:.1f}% (فوق المتوسط القطاعي)"
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if actual >= p50 * 0.9:
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return f"{actual*100:.1f}% (متوسط القطاع)"
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if actual >= p50 * 0.5:
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return f"{actual*100:.1f}% (تحت المتوسط — فرصة تحسين)"
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return f"{actual*100:.1f}% (يحتاج تدخل عاجل)"
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def generate_proof_pack(inputs: ProofPackInputs) -> ProofPack:
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"""Compose the full Proof Pack from raw metrics."""
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reply_rate = inputs.replies_received / max(1, inputs.drafts_sent)
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meeting_rate = inputs.meetings_booked / max(1, inputs.replies_received) if inputs.replies_received else 0
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win_rate = inputs.deals_won / max(1, inputs.proposals_sent) if inputs.proposals_sent else 0
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plan_cost = inputs.monthly_price_sar
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payback_multiple = inputs.pipeline_added_sar / max(1, plan_cost)
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grade = _grade_month(
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pipeline_sar=inputs.pipeline_added_sar,
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plan_cost=plan_cost,
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win_count=inputs.deals_won,
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)
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headline = (
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f"{inputs.pipeline_added_sar:,.0f} ريال pipeline جديد + "
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f"{inputs.meetings_booked} اجتماع في {inputs.month_label}"
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)
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tldr = (
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f"خلال {inputs.month_label}، Dealix أنتج لـ {inputs.customer_name} "
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f"{inputs.leads_discovered:,} lead، أرسل {inputs.drafts_sent} رسالة "
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f"عبر WhatsApp + إيميل، تلقّى {inputs.replies_received} رد، حجز "
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f"{inputs.meetings_booked} اجتماع، وأضاف "
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f"{inputs.pipeline_added_sar:,.0f} ريال pipeline جديد. "
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f"العائد: {payback_multiple:.1f}× تكلفة الباقة."
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)
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activity_summary = {
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"leads مكتشفة": f"{inputs.leads_discovered:,}",
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"leads مُثرّاة (enrichment كامل)": f"{inputs.leads_enriched:,}",
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"drafts مُولّدة": f"{inputs.drafts_created:,}",
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"WhatsApp مُرسلة": f"{inputs.whatsapp_sent:,}",
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"إيميل مُرسل": f"{inputs.emails_sent:,}",
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"LinkedIn مُرسل": f"{inputs.linkedin_sent:,}",
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"ردود مستلمة": f"{inputs.replies_received:,} ({reply_rate*100:.1f}%)",
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"ردود إيجابية": f"{inputs.positive_replies:,}",
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"اجتماعات محجوزة": str(inputs.meetings_booked),
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}
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pipeline_impact = {
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"اجتماعات → عروض": f"{inputs.meetings_booked} → {inputs.proposals_sent}",
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"صفقات مكتسبة": str(inputs.deals_won),
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"Pipeline مضاف": f"{inputs.pipeline_added_sar:,.0f} ريال",
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"إيراد محسوم هذا الشهر": f"{inputs.revenue_won_sar:,.0f} ريال",
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"متوسط قيمة الصفقة": (
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f"{inputs.revenue_won_sar / inputs.deals_won:,.0f} ريال"
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if inputs.deals_won else "—"
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),
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}
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quality_score = {
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"متوسط زمن الرد": f"{inputs.avg_response_minutes} دقيقة",
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"Bounce rate": f"{inputs.bounce_rate*100:.1f}%",
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"Opt-outs": str(inputs.opt_outs),
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"Compliance blocks (PDPL محمي)": str(inputs.compliance_blocks),
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}
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benchmark_comparison = {
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"Reply rate": _vs_benchmark_label(reply_rate, inputs.sector_reply_rate_p50),
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"Meeting rate": _vs_benchmark_label(meeting_rate, inputs.sector_meeting_rate_p50),
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"Win rate": _vs_benchmark_label(win_rate, inputs.sector_win_rate_p50),
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}
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top_performers = {
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"أفضل subject line": inputs.best_message_subject or "—",
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"معدل ردها": f"{(inputs.best_message_reply_rate or 0)*100:.1f}%",
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"أفضل قطاع لعبت فيه": inputs.best_sector_played or inputs.sector,
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"أكبر اختناق": inputs.worst_bottleneck_ar or "متوازن — لا اختناقات حادة",
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}
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# Recommendations — heuristic-driven
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recommendations: list[str] = []
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if reply_rate < inputs.sector_reply_rate_p50 * 0.8:
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recommendations.append(
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"أعد كتابة أول جملتين في الـ template الأساسي — معدل الرد تحت المتوسط القطاعي."
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)
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if meeting_rate < 0.25 and inputs.replies_received >= 10:
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recommendations.append(
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"حسّن الـ qualification في الرد الأول — كثير ردود ولكن قليل اجتماعات."
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)
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if inputs.avg_response_minutes > 60:
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recommendations.append(
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f"وقت الرد {inputs.avg_response_minutes} دقيقة عالي — فعّل WhatsApp auto-acknowledge."
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)
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if inputs.deals_won == 0 and inputs.proposals_sent >= 3:
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recommendations.append(
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"أرسلت 3+ عروض بدون إغلاق — جدولة call مع Deal Coach Agent لمراجعة العروض."
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)
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if not recommendations:
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recommendations.append(
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"الشهر متوازن — ركّز على scale: زد عدد الـ leads بنسبة 30%."
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)
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# Always add a forward-looking one
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recommendations.append(
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"ابدأ شهر جديد بـ Pulse القطاعي + 3 إشارات شراء جديدة من Why-Now Engine."
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)
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roi_breakdown = {
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"تكلفة الباقة هذا الشهر": f"{plan_cost:,.0f} ريال",
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"Pipeline مضاف": f"{inputs.pipeline_added_sar:,.0f} ريال",
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"Multiple": f"{payback_multiple:.1f}×",
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"Pay-per-Lead المعادل": (
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f"{plan_cost / inputs.replies_received:.0f} ريال/lead مؤهل"
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if inputs.replies_received else "—"
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),
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"Pay-per-Meeting المعادل": (
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f"{plan_cost / inputs.meetings_booked:.0f} ريال/اجتماع"
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if inputs.meetings_booked else "—"
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),
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}
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return ProofPack(
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customer_id=inputs.customer_id,
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customer_name=inputs.customer_name,
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period_label=inputs.month_label,
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headline_metric=headline,
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grade=grade,
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tldr_ar=tldr,
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activity_summary=activity_summary,
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pipeline_impact=pipeline_impact,
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quality_score=quality_score,
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benchmark_comparison=benchmark_comparison,
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top_performers=top_performers,
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recommendations_next_month_ar=recommendations,
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roi_breakdown=roi_breakdown,
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)
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