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淘券返利街-购物优惠券返利app

淘券返利街-购物优惠券返利app

杭州卓辉服饰有限公司

Shopping免费v5.2.7
App Store
评分

5.0

1 条评分

星级

★★★★★

最近更新

2026年1月14日

发布日期

2017年10月24日

更新内容

v5.2.7

System upgrade Level optimization bug

应用信息

开发者
杭州卓辉服饰有限公司
分类
Shopping
价格
免费
版本
5.2.7
App ID
1299661425

简介

Rebate Taobao alliance is a professional rebate app, so that the majority of users get benefits and concessions. The rebate app that allows users to save money helps users find more coupons and get more rebate. The rebate Taobao alliance gathers well-known shopping channels of the whole network to build a one-stop rebate platform. When shopping, the rebate is as high as 90%! Deep cooperation with thousands of e-commerce companies to provide users with convenience, safety and trust, leading brand of online shopping rebate, focus on online shopping rebate, online shopping assistant must be convenient online shopping, save money rebate, super taospecials! On the rebate Taobao! It is a rebate platform leading in market scale and user activity of domestic shopping discount industry. On the one hand, it provides customers with a new experience of safe and cost-effective online shopping, on the other hand, it provides cooperative e-commerce with CPS effect marketing services, so as to ensure a win-win situation in which customer flow value * maximization and online shopping resource allocation * optimization are realized. It is a kind of money saving helper that focuses on discounts and coupons, Taoke saving / alliance app, and Taoke shopping masters are using! Because focus, so professional; from all aspects to meet your shopping experience! Taoke shopping tools to save money, Taoke shopping talent, Taoke, office workers, Baoma people can not miss! [function highlights] Jingdong Coupons: easy to take hidden coupons and rebate! Coupons: limited hidden coupons, large coupons get coupons and reduce them instantly, so you can't think of the actual price!

下载量预测

专业 · 预览

预估总下载量

7450143
保守估计乐观估计

0

低 / 月

1

预估 / 月

1

高 / 月

基于1 条评分
假设评分率1.4%
应用年龄104 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。