When onboarding new customers, one of the most common questions we get is “how reliable are predicted LTVs? Put another way, this becomes the single biggest question that every marketer faces: how confident should I be in the projected success of my campaigns?
We share some of our expertise in how you can shortcut gaining confidence in your predicted LTVs by relying on historical predictions.
We’ve learned to be beneficial, data needs to be easily available, understandable, and actionable. Applied correctly data is a tool to make team’s lives easier and help them achieve their goals and drive forward KPIs. Applied incorrectly it becomes a tool no one knows how to use and will be ignored.
We cover how teams use data effectively, and those who don’t.
Data is a driving force between the ever-increasing number of mobile company acquisitions. But big data is hard. Really hard. Mobile apps generate a ton of data, so combining datasets is an ambitious and expensive undertaking.
We work with and talk to many large-scale app publishers, vendors, and tech providers and have seen a variety of strategies for how to combine and leverage the combined large datasets. Today, we’ll talk about the various methods of how companies are going about combining and leveraging these acquired datasets.
The presence of cheap capital and increasing UA costs are responsible for a flurry of M&A deals. And while there’s daily press on how these companies will leverage the resulting data for strategic advantage, there isn’t much information on what those companies are actually doing with the combined datasets.
We talk to many large-scale app publishers, technology vendors, and advertising platforms and we’ve shared some interesting strategies and examples of how these visionaries are utilizing acquired datasets for their business advantage.
How big is “big data”? Well, a petabyte (PB) is a thousand terabytes– or a million gigabytes. You would generate 1PB of data if you took 4,000 photos. A day. Over your entire life.
So big data is, well, big. But big data is hard. And expensive. Designing and maintaining a cost-effective data platform that scales to process 10PB of data a day is no small feat. AdLibertas CTO, Allen Eubank, walks through our approach at how we achieve massive scale in helping our customers get useful, actionable insights from their user data.