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Flipify: Marketplace Alerts

Flipify: Marketplace Alerts

Sam Salfi

Shopping免费v3.1.18
App Store
评分

3.9

42 条评分

星级

★★★★☆

最近更新

2026年5月12日

发布日期

2024年7月2日

更新内容

v3.1.18

Bug fixes + inventory

应用信息

开发者
Sam Salfi
分类
Shopping
价格
免费
版本
3.1.18
App ID
6504143452

简介

Flipify delivers instant Facebook Marketplace alerts and Craigslist alerts so you never miss new listings. Get instant notifications for underpriced items, local deals, and hidden gems before anyone else. Stop wasting hours manually refreshing your browser. In the world of reselling, speed is profit. Whether you are looking for used cars, free stuff, vintage clothing, or undervalued electronics, Flipify monitors the listings 24/7 so you don't have to. Why Choose Flipify? - Zero-Delay Alerts: Get push notifications within seconds of a new item being posted. - Multi-Platform Support: Simultaneously scan Facebook Marketplace, Craigslist, and other major local listing sites (coming soon). - Advanced Filtering: Set specific parameters for price range, location radius, keywords, and negative keywords (exclude items you don't want). - 24/7 Monitoring: Our bots run in the cloud, monitoring your searches even when your phone is asleep. Perfect For: - Retail Arbitrage & Resellers: Find underpriced items to flip for a profit on eBay or Amazon. - Car Flippers: Be the first to see reliable used cars and trucks before dealers grab them. - Real Estate Investors: Spot "For Sale By Owner" listings instantly. - Collectors: Snag rare vinyl, Pokémon cards, coins, and vintage streetwear. - Home Improvers: Find free furniture, appliances, and tools in your local area. Don't let another deal slip through your fingers. Join thousands of smart buyers who are saving money and building side hustles with Flipify. Try Flipify today and start winning the marketplace! EULA: https://www.apple.com/legal/internet-services/itunes/dev/stdeula/ https://flipifyapp.com

下载量预测

专业 · 预览

预估总下载量

3K2K6K
保守估计乐观估计

91

低 / 月

135

预估 / 月

261

高 / 月

基于42 条评分
假设评分率1.4%
应用年龄23 个月

基于评分数量 ÷ 类别评分率估算,实际下载量误差可达 ±50%,与 Sensor Tower 方法一致。