mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
synced 2026-06-19 15:59:37 +00:00
120 lines
4.6 KiB
Python
120 lines
4.6 KiB
Python
"""
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Causal Impact Simulator — "what if I changed X, what would change?"
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The user adjusts knobs (response time, follow-up cadence, sector focus,
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channel mix), Dealix projects the delta in pipeline / meetings / revenue
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based on Pulse benchmarks + historical lift coefficients.
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This is the engine behind the Revenue Impact Simulator dashboard widget.
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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 typing import Any
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# Historical lift coefficients (calibrated from pilot data + Pulse)
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RESPONSE_TIME_LIFT_PER_HOUR_REDUCTION = 0.014 # +1.4% reply per hour faster
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FOLLOWUP_LIFT_PER_EXTRA_TOUCH = 0.025 # +2.5% reply per extra follow-up (cap 3)
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WHATSAPP_VS_EMAIL_LIFT = 1.6 # WhatsApp gets 1.6x reply rate vs email in B2B Saudi
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SECTOR_FOCUS_LIFT_PER_SECTOR_DROPPED = 0.04 # +4% conversion per sector you stop chasing
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@dataclass
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class ImpactScenario:
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"""Result of one simulation: baseline vs scenario, delta."""
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scenario_name: str
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baseline_revenue_sar: float
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scenario_revenue_sar: float
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delta_sar: float
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delta_pct: float
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explanation_ar: str
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confidence: float
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risk_warnings_ar: list[str] = field(default_factory=list)
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def simulate_impact(
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*,
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current_baseline_revenue_sar: float,
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response_time_reduction_hours: float = 0,
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extra_followup_touches: int = 0,
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shift_to_whatsapp_pct: float = 0, # 0..1 fraction shifted from email
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drop_n_sectors: int = 0,
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scenario_name: str = "scenario_1",
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) -> ImpactScenario:
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"""
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Project incremental revenue from a set of operational changes.
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Each lever is applied multiplicatively to the baseline revenue.
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Returns the new total + delta + explanation.
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"""
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multiplier = 1.0
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explanation_parts: list[str] = []
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if response_time_reduction_hours > 0:
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lift = response_time_reduction_hours * RESPONSE_TIME_LIFT_PER_HOUR_REDUCTION
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multiplier *= 1 + lift
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explanation_parts.append(
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f"تقليل وقت الرد {response_time_reduction_hours} ساعة → +{lift*100:.1f}% إيراد"
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)
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if extra_followup_touches > 0:
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capped = min(extra_followup_touches, 3)
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lift = capped * FOLLOWUP_LIFT_PER_EXTRA_TOUCH
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multiplier *= 1 + lift
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explanation_parts.append(
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f"+{capped} متابعة إضافية → +{lift*100:.1f}% إيراد"
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)
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if shift_to_whatsapp_pct > 0:
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# Shifting from email to WhatsApp: each shifted % gets 1.6x reply rate
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# Approx incremental lift = shift_pct × (WHATSAPP_VS_EMAIL_LIFT - 1) × 0.4 (conversion factor)
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lift = shift_to_whatsapp_pct * (WHATSAPP_VS_EMAIL_LIFT - 1) * 0.4
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multiplier *= 1 + lift
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explanation_parts.append(
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f"تحويل {shift_to_whatsapp_pct*100:.0f}% للـ WhatsApp → +{lift*100:.1f}% إيراد"
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)
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if drop_n_sectors > 0:
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lift = drop_n_sectors * SECTOR_FOCUS_LIFT_PER_SECTOR_DROPPED
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multiplier *= 1 + lift
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explanation_parts.append(
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f"التخلي عن {drop_n_sectors} قطاع أقل → +{lift*100:.1f}% إيراد"
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)
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new_revenue = current_baseline_revenue_sar * multiplier
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delta = new_revenue - current_baseline_revenue_sar
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delta_pct = (multiplier - 1) * 100
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explanation = (
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"السيناريو لن يحدث فرقاً قابلاً للقياس."
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if not explanation_parts
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else " · ".join(explanation_parts)
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)
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# Confidence drops as cumulative lift exceeds 50% (less reliable extrapolation)
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confidence = max(0.4, 1.0 - max(0, multiplier - 1) * 1.2)
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risks: list[str] = []
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if shift_to_whatsapp_pct > 0.7:
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risks.append("تحويل 70%+ للـ WhatsApp قد يخلق opt-out مرتفع — اختبر تدريجياً.")
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if drop_n_sectors > 3:
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risks.append("التخلي عن 3+ قطاعات يحد TAM — تأكد من concentration risk.")
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if extra_followup_touches > 5:
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risks.append("5+ متابعات قد تُعتبر spam — احرص على تنويع القنوات.")
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if multiplier > 2.0:
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risks.append("الـ uplift المقترح كبير جداً (2x+). اختبر السيناريو على عينة محدودة أولاً.")
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return ImpactScenario(
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scenario_name=scenario_name,
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baseline_revenue_sar=current_baseline_revenue_sar,
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scenario_revenue_sar=round(new_revenue, 2),
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delta_sar=round(delta, 2),
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delta_pct=round(delta_pct, 2),
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explanation_ar=explanation,
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confidence=round(confidence, 3),
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risk_warnings_ar=risks,
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)
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