refactor: overhaul UI for streamlined user experience
Redesign navigation, home overview, user portrait, and valuation pages with improved functionality and responsive design. Co-authored-by: null <4804959+fnvtk@users.noreply.github.com>
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v0
292
services/data-analysis-service.ts
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292
services/data-analysis-service.ts
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import type {
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UserRFMData,
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RFMAnalysisResult,
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UserFilterOptions,
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DataComparisonResult,
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UserSegment,
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} from "@/types/data-analysis"
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// 模拟用户RFM数据
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export const getUserRFMData = (): UserRFMData[] => {
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return [
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{
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userId: "1",
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userName: "张三",
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phone: "13812345678",
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lastPurchaseDate: "2023-07-15",
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purchaseFrequency: 12,
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totalSpent: 15800,
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rfmScore: {
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recency: 5,
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frequency: 5,
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monetary: 5,
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totalScore: 15,
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},
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segment: "高价值活跃用户",
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valueEstimation: 3200,
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},
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{
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userId: "2",
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userName: "李四",
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phone: "13987654321",
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lastPurchaseDate: "2023-06-20",
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purchaseFrequency: 8,
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totalSpent: 9500,
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rfmScore: {
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recency: 4,
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frequency: 4,
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monetary: 4,
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totalScore: 12,
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},
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segment: "中价值活跃用户",
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valueEstimation: 1800,
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},
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{
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userId: "3",
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userName: "王五",
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phone: "13765432198",
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lastPurchaseDate: "2023-04-10",
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purchaseFrequency: 3,
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totalSpent: 3200,
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rfmScore: {
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recency: 2,
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frequency: 2,
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monetary: 3,
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totalScore: 7,
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},
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segment: "低价值流失风险用户",
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valueEstimation: 650,
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},
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{
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userId: "4",
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userName: "赵六",
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phone: "13654321987",
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lastPurchaseDate: "2023-07-18",
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purchaseFrequency: 15,
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totalSpent: 25000,
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rfmScore: {
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recency: 5,
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frequency: 5,
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monetary: 5,
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totalScore: 15,
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},
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segment: "高价值活跃用户",
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valueEstimation: 4500,
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},
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{
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userId: "5",
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userName: "钱七",
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phone: "13543219876",
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lastPurchaseDate: "2023-03-15",
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purchaseFrequency: 6,
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totalSpent: 12000,
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rfmScore: {
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recency: 1,
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frequency: 3,
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monetary: 4,
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totalScore: 8,
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},
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segment: "中价值沉睡用户",
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valueEstimation: 1200,
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},
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{
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userId: "6",
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userName: "孙八",
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phone: "13432198765",
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lastPurchaseDate: "2023-07-10",
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purchaseFrequency: 4,
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totalSpent: 5800,
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rfmScore: {
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recency: 4,
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frequency: 3,
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monetary: 3,
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totalScore: 10,
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},
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segment: "中价值活跃用户",
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valueEstimation: 950,
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},
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{
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userId: "7",
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userName: "周九",
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phone: "13321987654",
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lastPurchaseDate: "2023-05-25",
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purchaseFrequency: 2,
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totalSpent: 2500,
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rfmScore: {
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recency: 3,
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frequency: 2,
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monetary: 2,
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totalScore: 7,
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},
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segment: "低价值流失风险用户",
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valueEstimation: 480,
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},
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{
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userId: "8",
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userName: "吴十",
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phone: "13219876543",
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lastPurchaseDate: "2023-07-20",
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purchaseFrequency: 10,
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totalSpent: 18000,
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rfmScore: {
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recency: 5,
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frequency: 4,
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monetary: 5,
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totalScore: 14,
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},
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segment: "高价值活跃用户",
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valueEstimation: 3600,
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},
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{
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userId: "9",
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userName: "郑十一",
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phone: "13198765432",
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lastPurchaseDate: "2023-02-10",
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purchaseFrequency: 5,
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totalSpent: 8000,
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rfmScore: {
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recency: 1,
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frequency: 3,
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monetary: 3,
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totalScore: 7,
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},
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segment: "中价值沉睡用户",
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valueEstimation: 850,
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},
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{
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userId: "10",
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userName: "王十二",
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phone: "13098765432",
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lastPurchaseDate: "2023-07-05",
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purchaseFrequency: 7,
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totalSpent: 13500,
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rfmScore: {
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recency: 4,
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frequency: 4,
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monetary: 4,
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totalScore: 12,
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},
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segment: "中价值活跃用户",
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valueEstimation: 2200,
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},
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]
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}
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// 定义分群分布的类型
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interface SegmentDistribution {
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segment: UserSegment
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count: number
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percentage: number
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}
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// 分析RFM数据
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export const analyzeRFMData = (data: UserRFMData[]): RFMAnalysisResult => {
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const userCount = data.length
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// 计算平均值
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const averageRecency = data.reduce((sum, user) => sum + user.rfmScore.recency, 0) / userCount
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const averageFrequency = data.reduce((sum, user) => sum + user.rfmScore.frequency, 0) / userCount
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const averageMonetary = data.reduce((sum, user) => sum + user.rfmScore.monetary, 0) / userCount
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// 计算总估值和平均估值
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const totalValueEstimation = data.reduce((sum, user) => sum + user.valueEstimation, 0)
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const averageValueEstimation = totalValueEstimation / userCount
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// 计算分群分布
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const segmentCounts: Record<UserSegment, number> = {} as Record<UserSegment, number>
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data.forEach((user) => {
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segmentCounts[user.segment] = (segmentCounts[user.segment] || 0) + 1
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})
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const segmentDistribution: SegmentDistribution[] = Object.entries(segmentCounts)
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.map(([segment, count]) => ({
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segment: segment as UserSegment,
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count,
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percentage: (count / userCount) * 100,
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}))
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.sort((a, b) => b.count - a.count)
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return {
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userCount,
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averageRecency,
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averageFrequency,
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averageMonetary,
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segmentDistribution,
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totalValueEstimation,
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averageValueEstimation,
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}
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}
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// 过滤用户数据
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export const filterUserData = (data: UserRFMData[], options: UserFilterOptions): UserRFMData[] => {
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return data.filter((user) => {
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// 分群过滤
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if (options.segments.length > 0 && !options.segments.includes(user.segment)) {
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return false
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}
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// 日期范围过滤
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if (options.dateRange.start && options.dateRange.end) {
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const userDate = new Date(user.lastPurchaseDate)
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const startDate = new Date(options.dateRange.start)
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const endDate = new Date(options.dateRange.end)
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if (userDate < startDate || userDate > endDate) {
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return false
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}
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}
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// 价值范围过滤
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if (options.valueRange.min !== undefined && options.valueRange.max !== undefined) {
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if (user.valueEstimation < options.valueRange.min || user.valueEstimation > options.valueRange.max) {
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return false
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}
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}
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return true
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})
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}
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// 比较两个时间段的数据
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export const compareDataPeriods = (beforeData: UserRFMData[], afterData: UserRFMData[]): DataComparisonResult[] => {
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const segments: UserSegment[] = [
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"高价值活跃用户",
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"高价值流失风险用户",
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"高价值沉睡用户",
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"中价值活跃用户",
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"中价值流失风险用户",
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"中价值沉睡用户",
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"低价值活跃用户",
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"低价值流失风险用户",
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"低价值沉睡用户",
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"新用户",
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]
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return segments
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.map((segment) => {
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const beforeSegment = beforeData.filter((user) => user.segment === segment)
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const afterSegment = afterData.filter((user) => user.segment === segment)
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const beforeCount = beforeSegment.length
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const afterCount = afterSegment.length
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const beforeValue = beforeSegment.reduce((sum, user) => sum + user.valueEstimation, 0)
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const afterValue = afterSegment.reduce((sum, user) => sum + user.valueEstimation, 0)
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const changePercentage =
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beforeCount === 0 ? (afterCount === 0 ? 0 : 100) : ((afterCount - beforeCount) / beforeCount) * 100
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const valueChangePercentage =
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beforeValue === 0 ? (afterValue === 0 ? 0 : 100) : ((afterValue - beforeValue) / beforeValue) * 100
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return {
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segment,
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beforeCount,
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afterCount,
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changePercentage,
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beforeValue,
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afterValue,
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valueChangePercentage,
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}
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})
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.filter((result) => result.beforeCount > 0 || result.afterCount > 0)
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}
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