Intelligence (inside PBCP)

API Base: https://pbcp-api.pcampus.co/v1/intelligence
Recommendations: https://pbcp-api.pcampus.co/v1/recommendations

Overview

PBCP Intelligence is the analytics and recommendation layer that sits on top of Business Context and transforms context into actionable insights — answering the questions business owners actually need:

• Who is about to churn? → Send a win-back coupon
• Which segment responds best to this campaign? → Target that group specifically
• What should this customer receive next? → A personalized offer tailored to them

Position in PBCP stack

Intelligence reads from the Context API only — it never queries the Event Store directly.

Four intelligence pillars

1. Customer Intelligence

Analyzes individual customers:

2. Segment Intelligence

Automatically segments customers based on behavior:

Segments use the RFM model (Recency · Frequency · Monetary) as a baseline — weights are adjustable per domain

3. Campaign Intelligence

Measures and compares campaigns:

4. Journey Intelligence

Analyzes funnels and paths:

• Where most customers drop off
• What path leads to the fastest purchase
• What behavior distinguishes sessions that convert

5. Evaluation Intelligence

Measures outcomes and learns from actual actions — making Intelligence smarter with every cycle.

AI Quality Metrics:

Human Quality Metrics:

Outcome Attribution:

Every signal combines into (context → action → outcome) tuples — stored in the Evaluation Store and fed back into Intelligence to refine scoring and recommendations.

Recommendation Engine

The core of Intelligence — takes all insights and converts them into structured recommendations that the customer app can use immediately.

Individual Recommendations

Output Example:

{
  "customerId": "cust_xxx",
  "segment": "at_risk",
  "churnRisk": 0.78,
  "recommendations": [
    {
      "type": "coupon",
      "reason": "high_churn_risk",
      "offer": "10% off next purchase",
      "targetCategory": "vitamins",
      "confidence": 0.82,
      "validDays": 7
    },
    {
      "type": "reward",
      "reason": "loyalty_recognition",
      "offer": "bonus points x2",
      "confidence": 0.74
    }
  ]
}

Segment Recommendations

Output Example:

{
  "segment": "at_risk",
  "audienceSize": 142,
  "recommendations": [
    {
      "type": "campaign",
      "name": "comeback_offer",
      "description": "ส่งคูปอง 15% สำหรับลูกค้าที่ไม่ซื้อ 45+ วัน",
      "expectedResponseRate": 0.24,
      "expectedRevenueLift": 18500,
      "suggestedChannels": ["line_oa", "sms"]
    }
  ]
}

Full data flow

Pilot — Pharmacy (Pharmacy)

API overview