As always Chitika strives to constantly be improving and changing. Chitika is proud to announce it's new slider ad units. These units show multiple targeted products that cycle as the user remains on the page. The new UI increases user engagement which results in a higher CTR.
When this ad unit is used we see an increased in CTR by 3-4%. Currently only the 550x250 ad size uses this new format. Additional sizes will be launching in the upcoming weeks.
Every now and then, we update our policies to improve our publisher experience and to prepare for future changes to the products and services we offer.
By now, Chitika Insights is no stranger to Apple earnings calls: Among nods from former Apple CFO Peter Oppenheimer to Apple CEO Tim Cook, the company has referenced Chitika Insights data numerous times in public-facing announcements. This time, current Apple CFO Luca Maestri cited Chitika Insights data regarding not just one, but both of the company’s key products, the iPhone and the iPad
As an organization, Chitika has wholeheartedly invested in building out the Chitika Insights research program through staffing, rigorous process management, and a quality data infrastructure. Additionally, our reports rely on our vast trove of ad impression data and our knowledgeable and skilled team of data scientists. It’s this impression-level data and attention to statistical detail that has ultimately led our research to be cited by The New York Times, Wall Street Journal, CNN, Bloomberg, and many others.
In our last edition of Logging Data, we introduced Cluster Map Reduce, or CMR. The new tool acts as an alternative to Hadoop and HDFS when paired with a POSIX compliant clustered file system, simplifying the movement of data through the analytical back end, and helping to minimize the dependencies and potential points where the data pull process may slow or stop altogether. Today, we’re proud to provide this tool to the world as a free, Open Source release!
Thus far, our Logging Data series has focused on the nuts and bolts of our network operations and data infrastructure. While we employ some terrific software and hardware, our proverbial secret sauce consists of the various customizations we employ using these tools. No place was this more evident than during the transition from HDFS to Gluster, and the subsequent porting of Hadoop resources. The team here is well versed in working around issues, so after some brainstorming, the solution pretty much morphed into “Let’s just build something internally that fulfills our needs better than Hadoop.” Not an easy task, but one that our Operations and DI teams took on readily
We’ve briefly mentioned our implementation of Infiniband in both of the previous Logging Data posts without giving a thorough explanation of its function and capabilities within our architecture. In this latest installment, we’ll be doing just that, along with discussing our corresponding Hadoop framework.
The previous installment of our Logging Data series outlined how individual impressions move through our network. In this edition, we’ll discuss the necessary storage considerations cataloguing all of these impressions effectively 24 hours a day, specifically focusing on the challenges that result from the requirements of ad network operations.
In this “Logging Data” series, we’ll provide some in-depth detail on the intricacies of data collection, infrastructure, and access here at Chitika, hopefully providing some useful lessons for both newcomers and veterans in the field. Our first post will focus on our logs – the baseline of our data collection – and the subsequent processes that coalesce the information they contain into more readily accessible formats for our data scientists.