In this guide, we look in detail at how to optimize your testing strategies, frequency and size of test groups, how to reduce churn with ‘tutorial flow tests’, and how difficulty, pace and balance impacts player perceptions.
Most players don’t return after their first session. So, we look at what makes a game sticky. Covering how to compete for their attention, how to optimize their on-boarding experience.
Personalization isn’t just about marketing campaigns. In this guide, we explore how data insight and testing can lead to games that are designed to react to different playing styles, with a view to improving the in-game experience and engagement.
We explore 10 vital considerations for choosing a game analytics & marketing solution. The guide includes how objectives lead to different buying strategies, whether data should be big, raw or deep, and is real-time necessary?
In this article and video, Chris Wright, CTO of deltaDNA talks about his experience of building and maintaining the deltaDNA platform, and the criteria you should consider when deciding whether to build or buy game analytics.
This popular guide looks at why players leave, the role of personalization and how to increase monetization without detrimentally affecting player retention. We also look at the metrics best used for segmentation.
There are five factors to consider when optimizing ad revenue, the least of which is eCPM. Find out why nearly every developer is leaving money on the table when it comes to their ad strategy, and why many may have been better off not serving ads.
Understanding how players interact with your game is an important step towards optimizing both the player experience and game performance. We look at some of the advanced techniques employed by our data science team.
He’s back! DeltaDNA’s data mining dazzler runs through more crucial insights that are needed to make great games. In Vol. 2 he looks at pricing strategies, user acquisition, soft launch and Whales.
If data science is the new rock ’n roll, then deltaDNA’s own Elvis is back in the building. In Vol. 3 he croons about sample size when testing, multiple payments, gender splits and churn prediction. Uh-hu-huh.