users/services/rfm-engine.ts

export type RFMScore = { R: number; F: number; M: number; total: number; grade: "S" | "A" | "B" | "C" | "D" }
export type AnalyzeInput = {
  user_id: string
  last_active: string
  interactions: number
  amount: number
  chat_logs?: string[]
  source?: string
}
export type AnalyzeResult = {
  user_id: string
  rfm_score: RFMScore
  tags: {
    emotion?: "积极" | "中性" | "消极"
    behavior?: string[]
    intent?: "弱意图" | "中等意图" | "强意图"
    lifecycle?: "新用户" | "活跃用户" | "沉睡用户" | "流失风险"
    value?: "高" | "中" | "低"
  }
  weights: { R: number; F: number; M: number }
  created_at: string
  updated_at: string
}
type GroupSummary = {
  gradeCount: Record<RFMScore["grade"], number>
  valueCount: Record<"高" | "中" | "低", number>
  lifecycleCount: Record<string, number>
}

// 内存存储（后续可替换为 Mongo/ES）
const store = new Map<string, AnalyzeResult>()
let weights = { R: 0.5, F: 0.3, M: 0.2 }

export function getWeights() {
  return weights
}
export function setWeights(w: Partial<typeof weights>) {
  weights = { ...weights, ...w }
}

// utils
function clamp(n: number, min: number, max: number) {
  return Math.max(min, Math.min(max, n))
}
function daysFromNow(iso: string) {
  const d = new Date(iso).getTime()
  const now = Date.now()
  return Math.max(0, Math.floor((now - d) / (1000 * 60 * 60 * 24)))
}

// 评分
function scoreR(lastActiveISO: string): number {
  const days = daysFromNow(lastActiveISO)
  if (days <= 1) return 5
  if (days <= 3) return 4
  if (days <= 7) return 3
  if (days <= 30) return 2
  return 1
}
function scoreF(interactions: number): number {
  if (interactions >= 30) return 5
  if (interactions >= 15) return 4
  if (interactions >= 7) return 3
  if (interactions >= 3) return 2
  return 1
}
function scoreM(amount: number): number {
  if (amount >= 5000) return 5
  if (amount >= 2000) return 4
  if (amount >= 800) return 3
  if (amount >= 200) return 2
  return 1
}
function gradeFromTotal(t: number): RFMScore["grade"] {
  if (t >= 4.5) return "S"
  if (t >= 3.8) return "A"
  if (t >= 3.0) return "B"
  if (t >= 2.2) return "C"
  return "D"
}
function analyzeEmotion(chat?: string[]): "积极" | "中性" | "消极" | undefined {
  if (!chat || chat.length === 0) return undefined
  const joined = chat.join(" ")
  if (/[好棒|满意|喜欢|👍|推荐]/.test(joined)) return "积极"
  if (/[差|不行|失望|退款|投诉]/.test(joined)) return "消极"
  return "中性"
}
function analyzeIntent(chat?: string[], interactions?: number): "弱意图" | "中等意图" | "强意图" {
  const hasBuyWords = chat?.some((t) => /(购买|下单|价格|优惠|库存)/.test(t)) ?? false
  if (hasBuyWords && (interactions ?? 0) >= 20) return "强意图"
  if (hasBuyWords || (interactions ?? 0) >= 10) return "中等意图"
  return "弱意图"
}
function lifecycleByR(R: number): AnalyzeResult["tags"]["lifecycle"] {
  if (R >= 5) return "活跃用户"
  if (R >= 3) return "新用户"
  if (R === 2) return "沉睡用户"
  return "流失风险"
}
function valueByScore(total: number): "高" | "中" | "低" {
  if (total >= 4.0) return "高"
  if (total >= 2.8) return "中"
  return "低"
}

export function computeRFM(input: AnalyzeInput): RFMScore {
  const R = scoreR(input.last_active)
  const F = scoreF(input.interactions)
  const M = scoreM(input.amount)
  const total = clamp(R * weights.R + F * weights.F + M * weights.M, 1, 5)
  const grade = gradeFromTotal(total)
  return { R, F, M, total: Number(total.toFixed(2)), grade }
}

export function analyzeUser(input: AnalyzeInput): AnalyzeResult {
  const rfm = computeRFM(input)
  const tags: AnalyzeResult["tags"] = {
    emotion: analyzeEmotion(input.chat_logs),
    behavior: [],
    intent: analyzeIntent(input.chat_logs, input.interactions),
    lifecycle: lifecycleByR(rfm.R),
    value: valueByScore(rfm.total),
  }
  if (input.interactions >= 20) tags.behavior?.push("高频互动")
  if (input.amount >= 1000) tags.behavior?.push("高消费偏好")
  if (input.source === "wechat") tags.behavior?.push("微信渠道")
  if (input.source === "douyin") tags.behavior?.push("短视频渠道")

  const now = new Date().toISOString()
  const result: AnalyzeResult = {
    user_id: input.user_id,
    rfm_score: rfm,
    tags,
    weights,
    created_at: store.has(input.user_id) ? store.get(input.user_id)!.created_at : now,
    updated_at: now,
  }
  store.set(input.user_id, result)
  return result
}

export function getUserTags(user_id: string): AnalyzeResult | null {
  return store.get(user_id) ?? null
}

export function getGroupSummary(): GroupSummary {
  const grades: GroupSummary["gradeCount"] = { S: 0, A: 0, B: 0, C: 0, D: 0 }
  const values: GroupSummary["valueCount"] = { 高: 0, 中: 0, 低: 0 }
  const lifecycle: Record<string, number> = {}
  store.forEach((r) => {
    grades[r.rfm_score.grade]++
    if (r.tags.value) values[r.tags.value]++
    const lc = r.tags.lifecycle ?? "未知"
    lifecycle[lc] = (lifecycle[lc] || 0) + 1
  })
  return { gradeCount: grades, valueCount: values, lifecycleCount: lifecycle }
}

export function dumpCsv(): string {
  const headers = ["user_id", "R", "F", "M", "total", "grade", "emotion", "intent", "lifecycle", "value"].join(",")
  const rows: string[] = [headers]
  store.forEach((r) => {
    rows.push(
      [
        r.user_id,
        r.rfm_score.R,
        r.rfm_score.F,
        r.rfm_score.M,
        r.rfm_score.total,
        r.rfm_score.grade,
        r.tags.emotion ?? "",
        r.tags.intent ?? "",
        r.tags.lifecycle ?? "",
        r.tags.value ?? "",
      ].join(","),
    )
  })
  return rows.join("\n")
}