New data study from Adjust reveals more fake in-app purchases than genuine
Freemium and free-to-play (F2P) models continue to dominate the app economy, making in-app purchases a primary source of revenue for app publishers. However, as it turns out, a lot of in-app purchases aren’t real at all.
We took a long look at in-app purchases tracked through our systems. The analysis, released today, reveals the scope of in-app fraud and the effectiveness of different methods of preventing them.
- Number of fraudulent in-app purchases varies greatly by OS
- Synchronous server-to-server purchase verification is best all round option
- Cleaner datasets can improve ROI forecasting and retargeting
Analyzing over 6 million in-app purchases
Using adjust’s Purchase Verification software, we conducted a data analysis of over 6 million in-app purchases across both iOS and Android. Across our dataset, fake purchases actually outnumber the genuine.
Based on these findings, we reviewed and rated some of the most common methods of verifying in-app purchases: from local receipt verification and binary asynchronous validation services, down to full synchronous server-to-server verification.
The biggest danger is to your dataset
Based on our findings, we believe that the main issue isn’t how some users access content that’s normally behind a paywall. Instead, the main victim of faked purchases is your dataset. Fake purchases can get into your dataset, skewing your purchase analysis, your LTV calculation or ROI forecasting - potentially causing you to draw the wrong conclusions about your marketing strategy.
If you can’t separate the big spending users from the fraudster gamers, then your retargeting efforts and ad spend could be wasted. The true problem is identifying in-app fraud within your dataset to ensure your data and your decisions are as accurate as possible.
This report presents data insights into the level of in-app fraud across iOS and Android. In the paper, we offer actionable industry best practices on how app marketers can best protect themselves and their datasets against in-app fraud.