A/B Testing and Experimentation
A/B testing is a technique that allows marketers to compare different versions of an ad to see which one performs best. For example, two different headlines might be tested: one that focuses on the discount incentive and another on the quality of the product.
Servers hosting the ads can do this automatically when delivering the ad, and the test is measured for results by segment.
After an A/B test, ad servers will take a deeper look at performance metrics such as CTR, conversion rate, and CPA. For example, if an ad variant shows a 2% higher CTR, it can be flagged as effective and thus widely implemented in the campaign.
Reorientation strategies
Retargeting involves showing ads to users who may have visited a website previously but did not convert. For example, adding a product to a user's shopping cart can trigger a "cart abandonment" stage, and ads can be presented at this stage to remind the user about the product.
Lookalike audiences brazil business fax list are the type of audiences that work similarly to the behavior of your existing customers. Ad servers identify such people in the crowd and then show an ad that is likely to lead to more conversions on them. For example, a business would target lookalike audiences on Facebook just for the purpose of expanding its reach and maintaining a good successful conversion rate.
Real-time optimization
An advertiser can bid and set budgets during an ad for campaigns at will, taking into account real-time changes within the campaign. For example, you have suitable ad placements that convert several leads at incredibly low costs. Through real-time optimization, the ad server will increase the bid for those placements.
Ad servers analyze data and determine the location where maximum customer engagement and traffic conversion occurs. Therefore, a banner on a news website can be even more successful than a simple sidebar ad.
Challenges and Considerations
3 key challenges ad servers must overcome in the near future.
Privacy and data security
Now, with increasing sophistication, ad servers have to deal with privacy intrusion and strong privacy regulations like GDPR and CCPA. For example, in 2019, Google was fined $57 million for GDPR violations in the way it handled user data. Therefore, to avoid facing the same penalty, companies must ensure that their ad servers comply with all regulations to obtain explicit user consent for data collection and use.
