mirror of
https://github.com/christianvidalwolf-prog/CrazeAnalytix.git
synced 2026-08-03 13:35:24 +02:00
feat: Add Amazon Ads Weekly integration with Dashboard KPIs, Grid summary, and chart lines
- Update AdsRecord interface to support weekly data (year, week, cpc, ctr, acos, conversions) - Rewrite processAdsExcel to parse multiple sheets (2025, 2026) with 12-column format - Update mergeSalesAndAdsData to match by ASIN+Country+Year+Week - Add Advertising Performance section to Dashboard with Ad Spend, Attributed Sales, ACOS, ROAS - Add Ads toggle button and summary bar to DataGrid - Add Ad Spend lines (fuchsia dashed) to weekly comparison chart - Fix import paths in Dashboard.tsx and AdvertisingDashboard.tsx
This commit is contained in:
Christian Vidal Wolf
+120
-91
@@ -285,58 +285,61 @@ export const processAdsCSV = (file: File): Promise<AdsRecord[]> => {
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for (let i = 0; i < len; i++) {
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const row = rows[i];
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if (!Array.isArray(row) || row.length < 13) continue;
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if (!Array.isArray(row) || row.length < 12) continue;
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// Check header row (Column A: Customer or Country)
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const c0 = String(row[0]).trim().toLowerCase();
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if (c0.includes('customer') || c0.includes('country') || c0.includes('marketplace')) continue;
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// A (0): Customer/Marketplace
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// B (1): Month (Can be "may", "01", or Excel serial "45544")
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// C (2): Year
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// D (3): ASIN
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// E (4): Ad Spend
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// F (5): Clicks
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// G (6): Impressions
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// ...
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// L (11): Units (Attributed)
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// M (12): Sales (Sell Out)
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// Column mapping for CSV (same as Excel):
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// A (0): Country
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// B (1): Week
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// C (2): ASIN
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// D (3): Cost
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// E (4): Clicks
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// F (5): Impressions
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// G (6): CPC
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// H (7): CTR %
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// I (8): ACOS %
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// J (9): Conversions (30d)
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// K (10): Units (30d)
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// L (11): Sales (30d)
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const countryRaw = row[0];
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const monthRaw = row[1];
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const yearRaw = row[2];
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const asin = row[3];
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const costRaw = row[4];
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const clicksRaw = row[5];
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const impressionsRaw = row[6];
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const weekRaw = row[1];
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const asin = row[2];
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const costRaw = row[3];
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const clicksRaw = row[4];
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const impressionsRaw = row[5];
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const cpcRaw = row[6];
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const ctrRaw = row[7];
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const acosRaw = row[8];
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const conversionsRaw = row[9];
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const unitsRaw = row[10];
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const salesRaw = row[11];
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const unitsRaw = row[11]; // L
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const salesRaw = row[12]; // M
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if (!asin || !countryRaw || weekRaw === undefined) continue;
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if (!asin || !countryRaw) continue;
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const weekNum = parseInt(String(weekRaw));
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if (isNaN(weekNum) || weekNum < 1 || weekNum > 53) continue;
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// Construct Normalized Month-Year String (e.g., "May-24")
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const pureMonth = normalizeMonth(String(monthRaw)); // Returns "May"
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let yearShort = '';
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if (yearRaw) {
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yearShort = String(yearRaw).trim().replace(/[,.]/g, '').slice(-2); // "2024" -> "24", handle "2,024"
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}
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// Avoid double year if normalizeMonth already extracted it (rare for serial numbers but possible for strings)
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let finalMonthStr = pureMonth;
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if (!finalMonthStr.includes('-') && yearShort) {
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finalMonthStr = `${pureMonth}-${yearShort}`;
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}
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// For CSV without sheet names, assume current year
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const currentYear = new Date().getFullYear();
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data.push({
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country: mapCountryToMarketplace(String(countryRaw)),
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month: finalMonthStr,
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year: currentYear,
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week: weekNum,
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asin: String(asin).trim(),
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cost: parseCurrency(String(costRaw)),
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clicks: parseUnits(String(clicksRaw)),
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impressions: parseUnits(String(impressionsRaw)),
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attributedSales30d: parseCurrency(String(salesRaw)),
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cpc: parseCurrency(String(cpcRaw)),
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ctr: parseCurrency(String(ctrRaw)),
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acos: parseCurrency(String(acosRaw)),
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conversions: parseUnits(String(conversionsRaw)),
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attributedUnits30d: parseUnits(String(unitsRaw)),
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attributedSales30d: parseCurrency(String(salesRaw)),
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});
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}
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resolve(data);
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@@ -353,61 +356,80 @@ export const processAdsExcel = async (file: File): Promise<AdsRecord[]> => {
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try {
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const arrayBuffer = await file.arrayBuffer();
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const workbook = XLSX.read(arrayBuffer);
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const firstSheetName = workbook.SheetNames[0];
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const worksheet = workbook.Sheets[firstSheetName];
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const allData: AdsRecord[] = [];
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// Use header: 'A' to strictly map columns by index letter
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const jsonData = XLSX.utils.sheet_to_json(worksheet, { header: "A", defval: "" });
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const data: AdsRecord[] = jsonData.map((row: any) => {
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// Check if it's a header row
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if (row['A'] === 'Customer' || row['A'] === 'Country') return null;
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// Updated Mapping for Excel Column Letters
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// A: Country
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// B: Month
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// C: Year
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// D: ASIN
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// E: Cost
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// F: Clicks
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// G: Impressions
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// ...
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// L: Units
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// M: Sales
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const countryRaw = row['A'];
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const monthRaw = row['B'];
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const yearRaw = row['C'];
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const asin = row['D'];
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const costRaw = row['E'];
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const clicksRaw = row['F'];
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const impressionsRaw = row['G'];
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const unitsRaw = row['L'];
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const salesRaw = row['M'];
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if (!asin || !countryRaw) return null;
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// Construct Normalized Month-Year String
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const pureMonth = normalizeMonth(String(monthRaw));
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let yearShort = '';
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if (yearRaw) {
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yearShort = String(yearRaw).trim().slice(-2);
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// Process ALL sheets (e.g., "2025", "2026")
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for (const sheetName of workbook.SheetNames) {
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const year = parseInt(sheetName);
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if (isNaN(year) || year < 2020 || year > 2100) {
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console.warn(`Skipping sheet "${sheetName}" - not a valid year`);
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continue;
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}
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const finalMonthStr = yearShort ? `${pureMonth}-${yearShort}` : pureMonth;
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return {
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country: mapCountryToMarketplace(String(countryRaw)),
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month: finalMonthStr,
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asin: String(asin).trim(),
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cost: parseCurrency(String(costRaw)),
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clicks: parseUnits(String(clicksRaw)),
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impressions: parseUnits(String(impressionsRaw)),
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attributedSales30d: parseCurrency(String(salesRaw)),
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attributedUnits30d: parseUnits(String(unitsRaw)),
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};
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}).filter((r): r is AdsRecord => r !== null);
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const worksheet = workbook.Sheets[sheetName];
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// Use header: 1 to get array of arrays (row-based)
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const jsonData: any[][] = XLSX.utils.sheet_to_json(worksheet, { header: 1, defval: "" });
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return data;
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console.log(`Processing sheet ${sheetName}: ${jsonData.length} rows`);
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// Skip header row (index 0), process data rows
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for (let i = 1; i < jsonData.length; i++) {
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const row = jsonData[i];
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if (!row || row.length < 12) continue;
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// Column mapping for Ads Weekly.xlsx:
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// A (0): Country
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// B (1): Week
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// C (2): ASIN
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// D (3): Cost
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// E (4): Clicks
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// F (5): Impressions
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// G (6): CPC
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// H (7): CTR %
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// I (8): ACOS %
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// J (9): Conversions (30d)
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// K (10): Units (30d)
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// L (11): Sales (30d)
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const countryRaw = row[0];
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const weekRaw = row[1];
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const asin = row[2];
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const costRaw = row[3];
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const clicksRaw = row[4];
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const impressionsRaw = row[5];
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const cpcRaw = row[6];
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const ctrRaw = row[7];
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const acosRaw = row[8];
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const conversionsRaw = row[9];
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const unitsRaw = row[10];
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const salesRaw = row[11];
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// Skip if missing essential data
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if (!asin || !countryRaw || weekRaw === undefined || weekRaw === '') continue;
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const weekNum = parseInt(String(weekRaw));
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if (isNaN(weekNum) || weekNum < 1 || weekNum > 53) continue;
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allData.push({
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country: mapCountryToMarketplace(String(countryRaw)),
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year,
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week: weekNum,
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asin: String(asin).trim(),
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cost: parseCurrency(String(costRaw)),
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clicks: parseUnits(String(clicksRaw)),
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impressions: parseUnits(String(impressionsRaw)),
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cpc: parseCurrency(String(cpcRaw)),
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ctr: parseCurrency(String(ctrRaw)),
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acos: parseCurrency(String(acosRaw)),
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conversions: parseUnits(String(conversionsRaw)),
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attributedUnits30d: parseUnits(String(unitsRaw)),
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attributedSales30d: parseCurrency(String(salesRaw)),
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});
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}
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}
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console.log(`Total Ads records loaded: ${allData.length}`);
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return allData;
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} catch (error) {
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console.error("Error processing Ads Excel:", error);
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throw error;
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@@ -417,13 +439,12 @@ export const processAdsExcel = async (file: File): Promise<AdsRecord[]> => {
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// --- DATA MERGING ---
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export const mergeSalesAndAdsData = (salesData: SalesRecord[], adsData: AdsRecord[]): CombinedKPIs[] => {
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// 1. Index Ads Data for fast lookup: Key = ASIN + Marketplace + Month
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// 1. Index Ads Data for fast lookup: Key = ASIN + Marketplace + Year + Week
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const adsMap = new Map<string, AdsRecord>();
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adsData.forEach(ad => {
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// Ensure month format matches sales data (e.g. "May-24" vs "May-24")
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// Case-insensitive key
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const key = `${ad.asin.trim().toUpperCase()}|${ad.country.trim().toUpperCase()}|${ad.month.trim()}`;
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// Case-insensitive key using ASIN + Country + Year + Week
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const key = `${ad.asin.trim().toUpperCase()}|${ad.country.trim().toUpperCase()}|${ad.year}|${ad.week}`;
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// If duplicates exist (e.g. multiple campaigns for same ASIN), sum them up
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if (adsMap.has(key)) {
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@@ -433,6 +454,7 @@ export const mergeSalesAndAdsData = (salesData: SalesRecord[], adsData: AdsRecor
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existing.impressions += ad.impressions;
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existing.attributedSales30d += ad.attributedSales30d;
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existing.attributedUnits30d += ad.attributedUnits30d;
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existing.conversions += ad.conversions;
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} else {
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adsMap.set(key, { ...ad });
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}
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@@ -440,14 +462,21 @@ export const mergeSalesAndAdsData = (salesData: SalesRecord[], adsData: AdsRecor
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// 2. Iterate Sales Data and merge
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const mergedData: CombinedKPIs[] = salesData.map(sale => {
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const key = `${sale.asin.trim().toUpperCase()}|${sale.customer.trim().toUpperCase()}|${sale.month.trim()}`;
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// Use week from sales record if available
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const weekNum = sale.week || 0;
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const key = `${sale.asin.trim().toUpperCase()}|${sale.customer.trim().toUpperCase()}|${sale.year}|${weekNum}`;
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const adData = adsMap.get(key) || {
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country: sale.customer,
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month: sale.month,
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year: sale.year,
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week: weekNum,
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asin: sale.asin,
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cost: 0,
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clicks: 0,
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impressions: 0,
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cpc: 0,
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ctr: 0,
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acos: 0,
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conversions: 0,
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attributedSales30d: 0,
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attributedUnits30d: 0
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};
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