Incrementality is the measure of the lift that advertising spend provides to the conversion rate. Incrementality allows the advertiser to measure the effect of paid advertising for users from being idle/churned to coming back to the app on top of the organic return.
Whether a data scientist is just beginning her professional career, or she is already a seasoned professional, working at a startup offers a number of advantages. Most startups are more hands-on, and usually most employees are involved in many aspects of the company.
Companies know what buzzwords prospective clients want to hear, and they’ll use them regardless of whether they truly relate to their offering. This not only damages the client’s trust; it also harms the entire ecosystem’s push towards a safer and more efficient industry.
At Bigabid, ensemble learning methods are the weapons of choice when it comes to our machine learning (ML) architecture. As ensemble learning methods combine multiple base models, together they have a greater ability to produce a much more accurate ML model.
When you implement the Data QA process we’ve outlined here, you’ll be amazed by the number of bugs that exist in the data writing process that you’ve never even noticed.
One in five users forgets about an app after using it for the first time. They’re originally drawn to the app from an offer they’ve received or for a specific use, but after their first time using it, there’s a good chance the app will fall completely off the user’s radar.
The most obvious issues are related to computational efficiency, and the inability to visualize high-dimensional data. In this article, we’ll dive into the technicalities of PCA to help you better understand the model and its uses, benefits, and limitations. We’ll also explore some extensions to PCA.
AI is now mature and widespread enough to encounter criticism. Since most AI systems today aren’t quite software, the difference in their business model renders them less attractive for venture capitalists.
Although feature stores play a vital role in data strategy, it’s still difficult to find information about them online. But understanding what feature stores are and why they’re important is crucial, especially in today’s world of increasing data governance, and business problems being increasingly solved by machine learning models.
A Game-Plan to Scaling in a Responsible Way. Scaling up your platform is a necessary part of the evolution of your company. Moreover, scaling should be something that’s always on your mind.
Are you interested in getting into the field of data science? We don’t blame you. Data science is an exciting field that’s constantly changing and developing, which gives data scientists’ work endless potential.
Imbalanced classes are a common problem in machine learning classification, where there’s a disproportionate ratio of observations in each class. In this article, we offer guidelines for working with imbalanced datasets.
A/B testing is usually the first choice for people aiming to optimize an ad campaign setup. Instead of relying on guesswork, with A/B testing you can make choices based on more scientific data.
While heatmaps are hardly a new concept in digital advertising, their advantages are often forgotten and they’re rarely used for in-app user acquisition. In this article, we survey some of the ways in which heatmaps can be beneficial to your performance strategy.
In order to ensure that the work of data scientists is well-managed and delivers impactful results, our approach focuses on defining a set of possible outcomes for each data science task.
