AdSplit: Separating smartphone advertising from applications

A wide variety of smartphone applications today rely on third-party advertising services, which provide libraries that are linked into the hosting application. This situation is undesirable for both the application author and the advertiser. Advertising libraries require additional permissions, resulting in additional permission requests to users. Likewise, a malicious application could simulate the behavior of the advertising library, forging the user's interaction and effectively stealing money from the advertiser. This paper describes AdSplit, where we extended Android to allow an application and its advertising to run as separate processes, under separate user-ids, eliminating the need for applications to request permissions on behalf of their advertising libraries. We also leverage mechanisms from Quire to allow the remote server to validate the authenticity of client-side behavior. In this paper, we quantify the degree of permission bloat caused by advertising, with a study of thousands of downloaded apps. AdSplit automatically recompiles apps to extract their ad services, and we measure minimal runtime overhead. We also observe that most ad libraries just embed an HTML widget within and describe how AdSplit can be designed with this in mind to avoid any need for ads to have native code

Multi-touch attribution in the mobile gaming industry

User acquisition spend is a big investment for mobile gaming companies. Because of the large scale, even small improvements in how this spend is allocated can provide big returns. To allocate advertising spend well; it is important that the credit of a conversion be attributed as accurately as possible. The current attribution model - standard to the industry - is a last-touch attribution model, which attributes 100% of the credit to the last touch-point. However, before a user installs a game they might see ads from multiple channels that might all affect the user’s propensity to install. With the last-touch attribution model, the uplift of these ads is not observed which skews the returns on advertising spent for different channels. This study looks at how install probability develops as impressions per user increase, how long the effect of an ad lasts and attempts to find better attribution models that attribute credit better than the last-touch model. Three multi-touch attribution models are proposed; two based on the Shapley value and one based on the ad effect time decay of different channels. The data for this study comes from a mobile gaming company and consists of impressions seen by both installed and non-installed users as well as impression channels, impression time and install time. The data was collected during a 38-day period and has data from 44,719,217 users who were divided into a training set and a test set with a 70%/30% split. The test set is used to validate the proposed models against the last-touch attribution model by using the models trained on the training set to generate predictions on install probability for user paths in the test data set. The study finds that the ad effect of all channels declines very quickly after the first day and is almost zero at seven days after the impression. The study also attempts to find the correlation between install probability and the amount of impressions a user has seen. Regarding this objective, the study is inconclusive. This correlation behaves very differently between different channels and because the amount of impressions per users could not be controlled for, it is difficult to deduce causation. Out of the three proposed attribution models, only one is able to outperform the last-touch model when it comes to predicting install probabilities from the training set’s paths. The model that outperformed is a Shapley value based model that considers the times of impressions for each path when calculating credit attribution. Finally, the study finds that only 9.5% of observed installs had impressions from more than one channel during a seven-day attribution window. This combined with the difficulty of validating attribution models based on return on advertising spend means that developing a multi-touch attribution model probably is not a very low hanging fruit for performance marketers. What would be worth looking into would be to test optimizing the frequency of ads shown to users

In search of the audience

We all are members of media audiences. On many occasions, we are self-consciously so – such as when we sit in darkness in a cinema, transfixed by a larger-than-life screen, sharing the experience with a group of relative strangers. More frequently, we are part of an audience through habit or circumstance. Much of our media use is habitual. We are often barely aware of it. We scan the morning newspaper, half-listen to the car radio or iPod on the journey to work or university, glance at billboards, check online daily news updates, glance at the evening news bulletin – all this happens amidst the clutter of domestic life and regular patterns of work and leisure

Discharge Patterns of Single Fibers in the Cat's Auditory Nerve

Discharge patterns of single fibers in cat auditory nerve in response to controlled acoustic stimul

The History of the iPad

The purpose of this paper is to review the history of the iPad and its influence over contemporary computing. Although the iPad is relatively new, the tablet computer is having a long and lasting affect on how we communicate. With this essay, I attempt to review the technologies that emerged and converged to create the tablet computer. Of course, Apple and its iPad are at the center of this new computing movement