Blog & Latest Articles

Blog & Latest Articles

In this post, I will present why it is difficult to build trust in machine learning projects. To gain the most business value from the model, we want stakeholders to trust it. We want to provide defensive mechanisms to avoid problems impacting stakeholders and to build developers’ trust in the product.
This brief article will define the difference between all three and help illuminate best practices when utilizing these metrics.
App developers depend on a steady stream of ad revenue to be in business. Besides working with ad networks and partners to deliver the highest eCPM, publishers need to achieve the highest possible ad fill rates to maximize their ad revenue.
This brief article will define hyper-casual games, where they came from, and most importantly, how they can be monetized.
2022 has seen a challenging market hampered by the economic downturn, rising inflation, a war in Ukraine, a post-covid slump, and the remaining aftershocks of Apple’s privacy changes. But as they say, “when the going gets tough, the tough get going”.
Most Mobile marketers work tirelessly to increase their app’s conversation rate. The conversation rates they’re chasing could apply to app store installs, in-app purchases, subscriptions, etc., but the idea is the same… get your prospect to complete an action that converts them to a paying customer.
This article will outline what ads.txt is, how it works, its use in the mobile space, and uncover the app-ads.txt solution.
Mobile ad mediation is a mobile app monetization solution that allows app developers (publishers) to set, manage, and optimize multiple mobile ad networks with only one SDK (software development kit) integration.
In recent years, programmatic advertising has gone through rapid changes. With the onset of RTB and Header-Bidding technologies, auctions have grown more complex. If Publishers and buyers haven’t opted for fixed or pre-auction deals, the auction will be dynamic, where buyers bid for advertising inventory at different rates. This article will expose the benefits and pitfalls of both.
A programmatic waterfall (aka daisy-chaining) starts with a chain of ad networks/exchanges arranged in order of their performance for any given publisher from top to bottom. The network/exchange performance is typically rated based on CPM, fill rate, latency, etc.. Ad impressions are passed from network to network, from top-rated to bottom-rated ones until they’re sold.
Israel Mobile Summit is the ultimate event for everyone in the apps/games/adtech ecosystem. Whether you’re a publisher, developer, app marketer, startup, or VC, the summit features sessions on how to grow and monetize your apps using advertising and in-app purchases, how to analyze your data, as well as how to acquire and maintain more users.
If you’re looking to enhance your mobile app marketing campaign ROAS and deliver the best possible experience to your users, then understanding user segmentation is key.
As we depend more and more on ML (machine learning) in big business, we are beginning to see the effect of some big business ML failures. 9-figure ML-induced losses are becoming a trend like the recent Unity 110 M loss, Zillow’s recent ML failures, and many more. Considering this disturbing trend, we thought it pertinent to share how Bigabid approaches ML data quality, real-time and near-real-time monitoring, and performance by segment.
Many DSPs (Demand-Side Platforms) specialize in either UA (User Acquisition) or RT (Retargeting) technologies but not both. Running UA and RT campaigns in isolation can lead to misattribution, redundancies, and therefore a wasted spend. Bigabid advocates running UA+RT in harmony, and here are five reasons why!
Mobile gaming has taken over the world. Today, 9/10 gamers are mobile gamers, about 70% of all app store revenue is gaming, and by next year there will be roughly 3.07 billion mobile gamers worldwide.
From exploration to execution, high-scale campaigns demand the symbiotic coordination of an experienced team and revolutionary AI. Bigabid’s RTB team has refined its methodologies and technologies to scale campaigns with confidence by baking in all the essential ingredients that our high-scale campaigns depend on.
Recent mergers and acquisitions involving gaming companies like Microsoft and Activision Blizzard and advertising companies like AppLoving, IronSource, and Zynga have furthered the shift towards the use of first-party data.
Benchmarks can be CPI, ROI, or any other KPI that is important to the client. Sharing what ROI other vendors are reaching does not disclose any sensitive information such as budget or even strategy and is completely anonymous.
With proprietary tools like Deep Categories, which uniquely categorizes apps into thousands of categories instead of the 50 or so that the app stores use; Real-Time Data Analysis, which analyzes terabytes of data in real-time; and Creative Personalization, which creates thousands of ad variations to find the perfect fit…
App Tracking Transparency (ATT) is here to stay. Whether or not Apple is an altruistic champion of user privacy, playing war games with Facebook, or creating an unfair home-field advertising advantage is moot.
Bigabid has built an A/B testing system that is able to run hundreds of experiments simultaneously to provide answers about the effect size and statistical significance of creatives and treatments to any sub-group.
With recent advancements in creative optimization and personalization, along with modern AI-powered retargeting technologies, Bigabid can execute retargeting ads at high resolution and accuracy. 
In the past 3 years, we’ve collaborated with game app developers, designers, analysts, and monetization experts to give our proprietary ML (machine learning) technologies the edge in the social casino app industry
In the past 3 years, we’ve collaborated with game app developers, designers, analysts, and monetization experts to give our proprietary ML (machine learning) technologies the edge in the social casino app industry
We often hear from advertisers around the globe the term “cannibalism” relating to working with multiple advertising channels on either user acquisition or retargeting campaigns at the same time.
Today, Bigabid is hyper-focused on spearheading the use of ML to create a new generation of DSPs to target high LTV users in a more effective way than anyone else in the market.
App tracking transparency (ATT) is a new privacy protection framework for Apple devices. In practice, it means that when one downloads or opens an app, a notification pops up asking if the user wants to be tracked…
Retargeting activities are crucial for app growth. From owned media to working with DSPs, retargeting should be constantly evaluated and tailored per app. Now go find your retargeting partner or partner(s).
Real-time data analysis adds an exciting performance-enhancing element to user acquisition. Targeting in real-time based on session features and Deep Categories greatly improves our pricing, and therefore, your ROAS.
Bigabid upholds transparency as a core value in all of our relationships from clients to users, employees, and investors. It is the basis of our workflow, and therefore, it flows throughout our technology.
An app retargeting campaign is targeted toward users of an app who previously installed the app, were active for some time, and have churned. Because the audience is accurate and relevant, these campaigns can be highly effective and profitable.
The fact that Bigabid has been effectively using Deep Categories for a few years now, gives us a major advantage. We can spearhead this new era with our compliant, contextual-based technologies and help our clients have a smooth transition and ongoing success.
This is why Bigabid has developed such a unique approach. We create, produce, and run a massive collection of ad creatives. We integrate different concepts, wording, web-psychology insights, design patterns, and many more elements.
Bigabid is among the pioneers bringing order to this segment that was once called the “wild west of advertising,” helping advertisers reach users who are outside the “safe zones” of the Google and Facebook duopoly.
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.
How to deal with scaling on a day-to-day basis. You’re now managing a scaled system. Congratulations! But wait—don’t throw your feet up just yet.
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.
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