The world of mobile gaming ads is experiencing a quiet revolution. While the overall growth in ad spend has been modest, innovative strategies are gaining momentum among advertisers. Key among these is the rise of playable ads and the extension of campaign durations—a trend reshaping the landscape of mobile gaming advertising.

By harnessing the potential of in-app messaging, businesses can optimize user acquisition efforts, bolster their retargeting campaigns, and ultimately cultivate a more substantial and loyal user base.

Let’s delve deeper into the top 10 trends shaping the mobile gaming industry, influencing user engagement, technological advancements, and marketing strategies. By understanding and leveraging these trends, mobile app marketers can strategically position themselves for success and innovation in the evolving realm of mobile gaming.

In the fast-paced world of mobile app marketing, understanding cutting-edge technology can make all the difference in achieving successful advertising campaigns. One such area that is gaining traction is the use of neural networks and deep neural networks in mobile advertising. But what exactly are these technologies, and how do they differ? More importantly, how can they impact your advertising strategies?

Numerous industries are harnessing the immense potential of deep learning to fuel innovation. As computing power and datasets have significantly expanded, deep learning has transitioned from theory to practical applications. Whether enhancing manufacturing processes or refining mobile advertising strategies, deep learning offers opportunities to boost efficiencies and foster growth in various business sectors. Below, we unveil how neural networks with deep learning algorithms are poised to drive groundbreaking machine learning innovations for mobile advertising in 2024.

Tracking essential metrics for mobile app performance is crucial to guarantee a favorable user experience, boost engagement and retention, optimize revenue generation, and remain competitive in the constantly changing mobile app environment.

The rapid expansion of artificial intelligence (AI) has revolutionized diverse industries, notably mobile marketing. Through AI advancements, machine learning is now integral to data analysis and implementation in mobile marketing. This evolution is reshaping online advertising, prompting mobile marketers to adopt innovative strategies and technologies. Understanding AI’s significance in the marketing evolution is crucial for competitiveness in the mobile app industry.

Artificial Intelligence (AI) is at the forefront of transforming the landscape of mobile advertising, playing a pivotal role in driving significant change within the industry. Its influence extends across various critical areas such as campaign optimization, audience targeting, fraud prevention, and enhancing creative components, showcasing the multifaceted impact AI has on mobile advertising strategies.

Crafting a compelling ad for your mobile game can forge a strong bond with users, a pivotal factor for triumph in the mobile game industry. A captivating ad not only attracts the right audience but also enhances organic user acquisition, leading to optimal ROI. Furthermore, monitoring the performance of mobile game ad creatives can unlock the full potential of your game.

For years, focusing on user acquisition (UA) has been a key strategy for mobile game publishers to attract valuable players. This approach thrived on the abundant user data that powered their successful marketing efforts. However, recent privacy changes by Apple, and now Google have limited access to this data, prompting marketers to adapt their tactics to meet performance goals and rethink the role of AI in achieving success.

Want to grow your app’s user community? You’ll want to employ a user acquisition funnel—a strategic setup designed to captivate and convert newcomers to your app. It all starts with raising awareness about your app, nurturing potential users’ interest, stirring their consideration, and finally prompting them to act and install your app.

There are numerous strategies employed in the acquisition of users. Some of the most popular methods include influencer marketing, paid advertisements, social media campaigns, and search engine optimization (SEO). These strategies require a detailed understanding of your target audience to effectively reach and engage with them.

The technique known as cross-device targeting involves the identification and subsequent delivery of advertisements to individuals across this array of devices and platforms. Statista forecasts that by the start of the next decade, an average person worldwide will have close to 7 devices connected to the network. This projection presents advertisers with the formidable task of engaging with consumers in a consistent and impactful way across every device they use.

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.

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.

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. 

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.

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.

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.