Discuz! Board

 找回密码
 立即注册
搜索
热搜: 活动 交友 discuz
查看: 61|回复: 0

Ephemeral Marketing Harnessing the Power of Temporary

[复制链接]

1

主题

1

帖子

7

积分

新手上路

Rank: 1

积分
7
发表于 2024-3-6 16:28:18 | 显示全部楼层 |阅读模式
Sory impact performance for worse (or better) during either period? Did any other aspects of the campaign change during the eight weeks the test was running? It’s not perfect, but it can be useful to test sequentially to see results. Geolocation testing In a geolocation A/B testing example, you keep the existing campaign set up as it is, then create an experiment variant in a second location. This could be either to an expanded market or a portion of where you’re currently targeting (i.e., your campaign targets the entire United States, but for this test, you make the changes only effective in a handful of states). ab testing examples - geolocation targeting ab test example screenshot .


To accomplish this, you need to make sure your control and experiment are mutually exclusive so there’s no overlap. This can be done by setting up new campaigns and excluding locations in your control campaign. Unlike the sequential Iceland Phone Number A/B testing example, Geolocation testing can allow you to run your variants at the same time and compare results. Any head or tailwinds you feel during the run of the test should be equal for both locations. The downfalls come when you realize that no two regions are exactly alike. Who’s to say why a cost-focused message might work better in Oklahoma than in Nebraska? Or why the East Coast performs better with automated bidding than the .




Mountain time zone? A/B split testing Split tests are likely the best example of A/B testing as it removes some of the cons we see in sequential and geolocation testing. The problem is that true A/B testing is also the hardest to come by. ab testing examples - screenshot of google ads ad rotation optimization options Platforms like Google Ads and Meta Ads have long done away with rotating variables evenly. For example, both platforms have AI-powered machine learning that will pretty much always favor one ad variant over another based on the desired outcome of the campaign or ad set. The same is true for bid strategies. If you’re testing manual versus auto-bidding, or

回复

使用道具 举报

您需要登录后才可以回帖 登录 | 立即注册

本版积分规则

Archiver|手机版|小黑屋|DiscuzX

GMT+8, 2024-9-22 23:37 , Processed in 0.022150 second(s), 18 queries .

Powered by Discuz! X3.4

Copyright © 2001-2020, Tencent Cloud.

快速回复 返回顶部 返回列表