As the popularity of Google and Facebook continues to soar, so do data restrictions. While they have the infrastructure and are experts in processing massive volumes of data, some aspects weaken under this weight. Examples include customization, third party integrations, access to data, reporting, etc.
Google, rather stealthily, deployed a new restriction in January 2020 where iOS app installs driven by search traffic on Apple devices can’t be attributed to third-party networks. Advertisers will feel the blow of this punch since they won’t be able to promote an app through attribution providers and verify which traffic source generated profits.
So, can be done? Creative optimization can work when it comes to addressing the end user but it means working without actionable data.
With all of this in mind, the optimum route is, if resources allow it, to create a healthy traffic mix that’s composed of the Facebook-Google twosome coupled with smaller networks that expand reach.
What about fraud?
In a nutshell, fraud is increasingly becoming more sophisticated. Scalarr’s Data Science and Analytics teams work around the clock to look for anomalies in data. The challenge lies in the fact that there are a lot of fraud schemes working behind the scenes on many different levels.
Data is the key ingredient to uncover non-legitimate or fake installs. Ad networks, media buyers, and the majority of inventory out there are represented by legitimate companies in the market, but they are blind without data. And even with the right data, it’s very hard to eliminate fraud single-handedly or through traditional methods. The best solution is to employ advanced algorithms.
Here are some valuable recommendations:
- Choose an anti-fraud solution. When partnering with an anti-fraud solution, marketers and UA Managers can rest assured and know there are dedicated resources to analyze their traffic and ultimately, detect and reject fraud in order to protect the mobile app against it, especially when using non-trusted sources. The true differentiator is to use an anti-fraud solution that employs Machine Learning to detect fraud as it is the only technology capable of detecting fraud at any level imaginable.
- Dedicate an in-house team to analyze and validate traffic that comes from non-trusted sources. That way, the team can keep a close eye on specific KPIs and other performance metrics that can alert to any wrongdoing or the presence of anomalies. At the same time, understanding whether this “fraudulent source” is really harmful or if the portion of clean traffic it brings could get you a true uplift in revenue.
Overall, it’s unwise to deprioritize fraud. Instead, it should be treated with an assertive data-driven approach. Sometimes, we see up to 5% of all traffic as being fraudulent one month, but the next, it spikes up to 35%. With everything being measurable, a deep look into numbers will help marketers see that not everything is black and white when it comes to data.
What about attribution?
Most of the developers we interviewed asked our team about effective cross-channel, human-based attribution, and whether it was attainable. This is something that we definitely need along with standardized protocols of attributing installs for all sources. With these measures in place, attribution could reach new levels.
UALand, the place where anything goes. So, where is the treasure hidden?
Staying truly data-driven and keeping an eye on metrics will help marketers and UA Managers choose the right strategy to promote their apps, including which channels they should buy from. Right now, DSPs are looking very attractive and are gaining traction in the market that is always looking for novelty ways to attract new users. Maybe they’ll end up becoming a force of transformation in the UA industry. Let’s see what the future holds.
If you want more details about how Scalarr’s anti-fraud tool can employ personalized machine learning models specifically for your app and that guarantee accuracy of up to 97% in fraud detection, request a trial and contact us so we can work together.