返回榜单
Unroll.Me - Email Cleanup

Unroll.Me - Email Cleanup

Unroll.Me

Productivity免费v3.3.27
App Store
评分

4.7

128,264 条评分

星级

★★★★★

最近更新

2026年1月10日

发布日期

2015年11月5日

更新内容

v3.3.27

Bug fixes and other improvements to make your experience more enjoyable.

应用信息

开发者
Unroll.Me
分类
Productivity
价格
免费
版本
3.3.27
App ID
1028103039

简介

Are spam and subscription emails flooding your inbox? Do you have hundreds, if not thousands, of useless emails making your cluttered mailbox impossible to navigate? Worry no more! Unroll.Me to the rescue! Cleaning your inbox has never been so easy, or looked so good! With Unroll.Me, we’ll show you all the subscription emails in your inbox, and give you full control over what you want to do with them. Easily block unwanted emails, keep the ones you want, and rollup those that you don’t want to block, but also don’t necessarily want to see in your inbox. Here’s what you can expect from Unroll.Me: • View all subscription emails flooding your inbox and we’ll update this as we detect new subscriptions. • Block, keep, and rollup your subscription emails, either in bulk or individually. • Search your subscriptions easily so you can find that one company that won’t stop spamming you. • Wanted to block an email subscription that you kept or rolled up? No worries, you can edit any and all changes that you’ve made to your subscriptions in the Subscriptions tab. • View your rolled up emails - this updates once a day and we’ll send you a daily email of all new mail you received from your rolled up subscriptions. It’s like a daily digest email! • Add multiple email accounts and tackle your subscriptions across all accounts with Unroll.Me. • Support for the following email providers: Gmail, iCloud, Yahoo!, AOL, Outlook and Google Apps. More to come… Stop stressing over your inbox and get back to spending time on what matters to you. Download Unroll.Me and get back the “you time” you’ve been missing. Love Unroll.Me? Leave a review and let us know what you think!

下载量预测

专业 · 预览

预估总下载量

12.8M8.6M25.7M
保守估计乐观估计

67K

低 / 月

100K

预估 / 月

200K

高 / 月

基于128,264 条评分
假设评分率1.0%
应用年龄128 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。