Artificial Intelligence for campaign analysis to predict Risks/ Opportunities
Even before Artificial Intelligence, running Lead or Mobile CPI campaigns always require regular ROI checks to ensure that users actually convert into paying customers. For this marketing, experts typically use Goals or also called Post Conversion Events to measure user activity or purchases. The approach works very well as its accurate but it has a significant downside.
When a new campaign starts, marketers have to wait typically 2 weeks until they can see first results on KPI / metrics to determine the quality of the source. For statistical significance its also needed to collect enough data to not take a premature decision. Unfortunately, this is a time consuming and costly undertaking following this simple calculation:
Payout Per Lead
More often than not, the quality does not meet the expectation, and the campaign has to be stopped, either for Fraud reason or simply low ROI. Now we have been analyzing how to shorten the time frame. If it would be possible to get a better indication on the first day of the campaign rather than having to wait for 14 days.
Introducing Artificial Intelligence quality predictions
From our internal analytics of sources for fraud reasons, we have discovered that quality of users also depends on a variety of factors – such as Day of the week, if a VPN is used, how long it took the user to complete a survey or install an APP, the Device, OS Version and many other factors. Overlooking these factors is difficult and the rules for this are often too complex to put it into a fixed set of rules.
Provide easy to understand Artificial Intelligence Quality Prediction Scoring
Custom model trainings are possible(upon requests)
A.I Quality score will be shown inside the Fraud Report to give the best overview of your traffic
iOS 14.5 Skad Network ready
How can machine learning algorithms help you stop fraud?
Artificial intelligence Trained model
we started training an A.I model based on 7.5 mio conversions and comparing the outcome of each single conversion to turn into a paying customers. The results were extremely interesting as we have identified differences in languages and even ISP. As an example users from the Google Fiber Network in the U.S (a Gigabit Internet ISP for tech savvy users) showed a 70% higher probability to turn into paying users than AT&T users.
An Overview of the Campaign analysis with the Artificial Intelligence FEATURE
Different Models for different use cases can be selected. The model considers input values, including User Agent, Country, City, ISP, time zone, connection type, Click to Conversion time and more.
Every conversion receives its own A.I predictions score in our report. The results are predictions and dot not represent a perfect accuracy. Prediction scoring is meant as another tool for marketers to decide early in the campaign whether they want to continue running the campaign at high volume or if they better reduce volume or pause the campaign to see definite results after 2 weeks. In our tests we could see that Adwords / Apple and Youtube Video campaigns scored significantly higher than other sources.