A good ML model needs to not only be able to identify the obscure qualities of high LTV users but also output a “user score” that is precisely proportional to that quality. When predicting whether or not a user is going to have a high LTV, the model should calculate the following at the time of an auction…

This case study will demonstrate how the company built a high-performance real-time architecture with minimal data engineering using Upsolver, S3 and Amazon Athena.

After months of discussion, pushback, and debate Apple has finally pulled the plug on its Identifier for Advertisers with its iOS 14.5 update, which started rolling out to users on April 26.

The transition of advertising into the digital world took us from a time of Tinseltown and Madison Avenue to the world of Silicon Valley and technology disruptions. 

A good ML model needs to not only be able to identify the obscure qualities of high LTV users but also output a “user score” that is precisely proportional to that quality. When predicting whether or not a user is going to have a high LTV, the model should calculate the following at the time of an auction…

It’s been a week since iOS users started receiving notifications that they could turn off cross-app tracking and, unsurprisingly, it’s difficult to draw any meaningful conclusions so far.

While programmatic ad spending has been growing rapidly over the past few years, it still has a lot of room for growth. In some areas of the world, such as South East Asia, the market is just beginning to realize the potential of programmatic ads.

“Because there are a lot of unknowns around the impact of Apple’s move, some marketers are allocating budgets to places where they have more control over measurement, i.e Android, Ido Raz of advertising company Bigabid told Digiday.

While programmatic ad spending has been growing rapidly over the past few years, it still has a lot of room for growth. In some areas of the world, such as South East Asia, the market is just beginning to realize the potential of programmatic ads.

The idea for Bigabid came from a simple understanding that there’s a lack of efficiency of how things are being made in our industry. The media industry is fascinating as it is evolving and changing all the time and I was lucky to get exposed to it early in my career.

I’ve seen much value gained from building and maintaining a centralized feature store. Feature store is a centralized software library that contains many functions, where each function creates a single feature from a standardized input (data).

One in five users forget 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.

Apple’s anti-tracking feature is looming. But whether it will help or hinder ad dollars flowing into Apple depends on who you ask. There’s a general consensus that costs will go up once fewer companies are able to use Apple’s mobile Identifiers for Advertisers mobile identifier to track people.

Customer lifetime value is the amount of revenue that you can expect from a customer over the period that your service will be provided to them. So, this metric is clearly dependent on how long a customer will use your service and how much they’d be paying you.

We bring the most advanced technology to the digital media world. Using military methods to analyze data and make decisions in real-time helps our app developer customers reach their users. For example. Bigabid has mapped the app stores in a way that aids developers through a technology we created called Deep Categories.

The moat in those fields is primarily the data sets your models are trained on and, to a lesser degree, the DL architecture you are using. In other areas, hand-crafted features and domain knowledge, as well as the unique data sets you have access to, will be the game-changers.

“Deep learning costs a lot in compute resources, for marginal payoffs.”It’s important to note that the AI business model differs from a typical SaaS business model in the sense that there are higher cloud/compute resources costs associated with building models and execution.

Granulate, a startup developing a platform that optimizes computing infrastructure, today announced it has raised $30 million, bringing its total raised to $45.6 million.

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.

Bigabid, a second-generation optimized DSP that helps app developers with user acquisition & re-engagement, is launching a new, proprietary mechanism called Precondition Targeting.

Bigabid, a second-generation optimized DSP that helps app developers with user acquisition & re-engagement, is launching a new, proprietary mechanism called Precondition Targeting.

For many companies/data scientists that specialize or work with machine learning (ML), ensemble learning methods have become the weapons of choice. As ensemble learning methods combine multiple base models, together they have a greater ability to produce a much more accurate ML model.

Here’s something I often hear from advertisers:“What makes you different? And please don’t say ‘machine learning and AI’… we get dozens of people telling us that’s what they do, and it’s rarely true.