New Study Shows 67% of App Revenue Comes From Advertising

Advertising on apps has come under fire because of the fact that this is the sort of thing that could potentially end up invading user privacy. Companies like Apple have attempted to curtail the non-consensual use of user data by toggling third party tracking off by default, thereby making users have to opt in for this form of tracking if they feel like it is worth it. In spite of the fact that this is the case, advertising continues to be a massive driver of revenue for the app economy.

It is currently estimated that the annual revenues brought in by these apps sits at around $500 billion. With all of that having been said and now out of the way, it is important to note that 67% of this revenue, or $336 billion to be precise, comes from advertising. The remaining 33%, which comes up to approximately $167 billion, comes from purchases that users make within the app.

50% of the ad revenue received by apps went towards so called owned and operated ads. The term refers to ads that are owned by the platforms themselves rather than being provided by a third party. These include the likes of Instagram, TikTok, Facebook, YouTube, Twitter, LinkedIn, Snapchat and Pinterest.

35% was earned by games, and 15% was distributed between other apps with all things having been considered and taken into account. However, games tend to receive far more revenue from in app purchases. 66% of in app purchases revenue was earned by games, so the shortfall in terms of ad revenue is more by design rather than suggesting an inherent flaw.

Also, the massive setbacks caused by App Tracking Transparency and crackdowns by the EU did not hinder the growth of advertising revenue. It grew by about 14% in 2022, which is far higher than the 2% increase seen in in-app purchasing.

When comparing various regions, North America unsurprisingly topped the list in term of how much revenue was generated. Approximately half of all advertising revenue came from this region, with China coming in second with a share of 23%. China is followed by Europe which had a 19% share of total ad revenue for 2022.

A unique trend that has emerged is that of one time purchases. Previously being seen as the sole purview of gaming apps, other apps like TikTok have been utilizing this method to great effect since it can make revenues higher than might have been the case otherwise. It will be interesting to see if apps other than TikTok try to adopt this model, since it has been doing wonders for the companies profit margins.

Most apps on both the Apple App Store and the Google Play Store use what is called a mixed monetization strategy. For example, while 90% of the revenue earned by YouTube comes from ads, it still relies on in-app purchases such as subscriptions for 10%. Subscription models are dominating the in-app purchasing segment as well, even though games tend to take up the bulk of the earnings on that front.

Advertising is still the bread and butter of most major apps. This does not look like it will change in the near future.

H/T: DataAI

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by Zia Muhammad via Digital Information World

How game marketers can get the best value on their ad spend

Author: Joel Julkunen - Head of Analytics at GameRefinery, A Liftoff Company

The mobile games market experienced its first year-over-year, sequential dip for the first time in over a decade last year, with revenue falling by 6.4% to $92.2 billion from 2021 to 2022. This is very different from how the market looked just a few years ago during the pandemic’s mobile games boom when spending rose by over 20%. Still, there’s plenty to be positive about, as sequential growth is expected to resume from 2022 to 2023. Mobile marketers and game developers have risen to the challenge with innovative game design and advertising to attract new players.

So what’s changed? The end of the pandemic meant that many mobile gamers turned their attention back to the real world, and the impact of inflation is making them less likely to spend money when they do pick up and play their favorite mobile games.

There’s also the matter of Apple’s ATT (App Tracking Transparency), which since April 2021 has mandated iOS apps to ask users’ permission to track their activity across other apps and websites. This has impacted the quality of user data available to mobile advertisers, with as many as 68% stating that marketing has become more difficult due to being unable to tailor ads to the personal interests of individual users.

With all the new changes, it’s never been more important for mobile game marketers to get the best value from their ad spend. The problem is that mobile gamers each have their own individual player motivations and genre preferences. Knowing where to focus your ad spend isn’t easy without detailed tracking information.

Thankfully, the insights available in Liftoff’s 2023 Casual Gaming Apps Report—based on our programmatic data spanning over 390 billion ad impressions and 16.7 billion clicks across 100 million installs—can provide some answers for casual game marketers.

Breaking down the casual market

Let’s start by discussing genre. Most mobile gamers play casual games, and our report also found ads placed in casual gaming apps to be the biggest driver of all gaming installs—regardless of genre—at 86.9%. We can see where we need to focus if we break the casual market down into the three most popular subgenres.

First up is puzzle, which includes many match-3 titles alongside trivia, solitaire, coloring, wordplay, and other games. Then we have lifestyle, which includes interactive story games such as Lovelink, those concentrating on home customization or dress-up, and several music and rhythm games. Last is simulation—these games put the player in control of almost anything, such as a theme park in RollerCoaster Tycoon, and include virtual pet sims.

Simulation, lifestyle, and puzzle games are popular and tend to perform well financially. They are competitive in terms of their CPI (cost per install) and ROAS (return on ad spend). However, the findings in our report indicate that the biggest opportunities are in simulation games, which offer the best user acquisition deal at $0.59 per install while providing an 8.5% return after seven days.

Mobile game developers should take note. By emphasizing simulation gameplay mechanics in their ad playables, they can improve their chances of conversion.

The subgenres driving the most installs

Equally important when it comes to subgenres, especially given today’s difficulties in acquiring users against deterministic data, is the different impact each has on user acquisition. That begs the question, which gaming subgenres drive the most users for other subgenres?

Our report traced installs of gaming apps to the apps where their ads are displayed and found that puzzle games are one of the biggest install drivers at 31.3%, which is to be expected given their popularity. Ads in hyper casual games also remain a significant driver of installs at 32.3%, although it remains to be seen how long this will last given the category’s steady decline.

Comparatively, ads in simulation and lifestyle games make up a smaller proportion of the mobile market, meaning they drive fewer installs at around 9% each. As you move away from casual and look at mid-core—which includes shooters, strategy games, and RPGs—the impact drops significantly to 3.3%.

Despite this, there are some gains to be had, as these smaller genres generally offer much better CPI and ROAS due to less competition from other titles on the market. Mobile marketers should also pay close attention to player motivations. If a lot of users come from ads in a specific type of game, playables should be tailored to appeal more to that audience. Mid-core games, with their smaller audiences, stand to benefit significantly.

Many mobile marketers simply focus on similar games to theirs to find users, but one of the best ways to take advantage of these install rates would be to diversify their strategies. In our report, we took a closer look at match-3 games and found a lot of crossover with other genres, such as word games (11.2%) and lifestyle (13.1%).

Similar games would benefit from targeting multiple genres and subgenres with their ads to maximize their reach.

Key trends for casual game marketers on mobile

As the mobile gaming market grows more challenging and competitive, developers have responded with notable innovations. Here are a few new trends driving revenue for the casual gaming market.

One trend is the rising popularity of merge games, where players drag and combine different items. Developers have found they can easily combine the straightforward gaming mechanics of merge games with other meta layers to bring new experiences to their players. For example, the story-driven merge game Gossip Harbor combines its merge-2 mechanics with a strong narrative focus to keep players coming back. Mid-core titles like Top War have also introduced merge gameplay to attract casual gaming audiences.

Another genre that is making waves is hybrid casual. We already briefly mentioned how hyper casual is dwindling in popularity, so much so that it's fallen from around 50% market share in Q1 2021 to just over 30% in Q1 2023. This has been mainly caused by market oversaturation, as well as the pandemic and IDFA. Many hyper casual developers are integrating more complex mechanics from mid-core genres into their titles to offer players something new.

Genre crossover has also become popular across the casual games market, with around 23% of the top 200 grossing casual games now featuring minigames. These engage users by offering them new experiences that differ from core gameplay loops, both as part of events and as permanent additions. For example, the tycoon-exploration game Family Farm Adventure primarily revolves around building a farm and fulfilling orders but has steadily introduced minigames incorporating features such as archery and platforming.

Marketers often feature minigames in ad campaigns to acquire new users. For example, a 4X strategy title could introduce a minigame with merge mechanics and create a version for playable ads. This will likely widen their audience.

Summary

If mobile game marketers and developers hope to succeed in this challenging climate, they need to use everything at their disposal to get the best value out of their ad spend. That means knowing where the best opportunities lie in terms of player motivations, demographics, and genre, as well as being aware of the latest trends proving to be a big hit with casual gamers.

Our 2023 Casual Gaming Apps Report is a great place to start, but here are the main takeaways:

• Simulation games have the lowest CPI (cost per install) at $0.59. By comparison, lifestyle players cost over twice as much to acquire at $1.32 but offer a similar return after seven days at 8.3% (compared to 8.5% for simulation).
• Despite their dip in popularity, hyper casual games are still the most significant driver of installs across all genres at 32.3%, closely followed by puzzle games at 31.3%.
• When comparing CPI, Android costs an average of $0.63 compared to $2.23 for iOS. Despite the cost margin, D7 (day seven) ROAS (return on ad spend) rates are similar between the two platforms, with iOS offering a slightly better return on D7 at 7.8% compared to 7% on Android.
• North America has the highest CPI worldwide by far at $3.59, which is over three times as much as the CPI for Europe, the Middle East, and Africa, but it also has the highest D7 ROAS at 8.1%. Comparatively, Latin America has the lowest CPI at $0.55 per install, although it also has the lowest D7 ROAS at 4.8%.
• Non-merge and mid-core games are integrating merge mechanics, while hyper casual games are becoming hybrid casual. Marketers should keep a close eye on the hybrid casual genre and which gameplay mechanics are trending to consider how these can be incorporated into ad playables.

Read next: Generative AI is Disrupting the Digital Marketing Sector, Here’s What You Need to Know
by Web Desk via Digital Information World

Fighting Fraud with Artificial Intelligence: Empowering Identity Verification

In today's digital landscape, the rise of fraudulent activities poses significant challenges for businesses and individuals alike. However, thanks to advancements in technology, particularly in the field of artificial intelligence (AI), combating fraud has become more effective and efficient. One crucial aspect of fraud prevention is identity verification, which serves as a strong defense against malicious actors. In this article, we will explore how AI can bolster efforts to fight fraud, with a particular focus on the role of identity verification.

The Role of Artificial Intelligence in Fraud Detection

Artificial intelligence has revolutionized the way fraud detection is conducted. Traditional methods often rely on rule-based systems that can be rigid and limited in their ability to adapt to emerging threats. AI, on the other hand, leverages machine learning algorithms to analyze vast amounts of data and identify patterns and anomalies that may indicate fraudulent activity.

Enhanced Pattern Recognition: AI-powered systems excel at recognizing complex patterns, enabling them to detect subtle signs of fraudulent behavior that might otherwise go unnoticed. By continuously learning from new data, AI algorithms improve over time, adapting to evolving fraud techniques and staying one step ahead.

Real-time Monitoring: AI enables real-time monitoring of transactions and activities, allowing for immediate detection of suspicious behavior. This proactive approach helps prevent fraud before it occurs, minimizing potential losses and damages.

Illustration: Gstudioimagen/freepik
Behavioral Analysis: AI algorithms can analyze user behavior and establish a baseline for each individual. Deviations from the established patterns can raise red flags and trigger further investigation. This approach is particularly effective in identifying account takeover attempts and unauthorized access.

The Power of Identity Verification

Identity verification plays a pivotal role in preventing fraud and protecting both businesses and individuals from malicious actors. AI-powered identity verification solutions offer a range of sophisticated techniques to verify the authenticity of an individual's identity. These techniques include:

Document Authentication: AI algorithms can examine identity documents, such as passports or driver's licenses, to determine their validity. They analyze various security features, including holograms, watermarks, and embedded microprinting, and compare them against a database of known fraudulent documents.

Biometric Authentication: Biometric data, such as facial recognition or fingerprint scanning, is a highly secure method for verifying identity. AI-powered systems can compare an individual's biometric data against trusted sources to ensure their legitimacy. This approach reduces the reliance on traditional authentication methods that can be vulnerable to theft or replication.

Fraudulent Pattern Detection: AI algorithms excel at identifying patterns indicative of fraudulent activity. By analyzing vast datasets and detecting anomalies in user behavior or transaction patterns, these systems can identify potential fraudsters attempting to exploit stolen or fabricated identities.

Combining AI and Identity Verification

By integrating AI with identity verification processes, businesses can significantly enhance their fraud prevention capabilities. The synergy between these two technologies allows for:

Streamlined User Experience: AI-powered identity verification systems can simplify the onboarding process for legitimate users. By automating identity checks and reducing the need for manual document verification, businesses can offer a seamless and user-friendly experience while maintaining robust security measures.

Efficient Risk Assessment: AI algorithms can analyze numerous data points, including historical patterns, transactional data, and third-party sources, to assess the risk associated with an individual or a transaction. This information enables businesses to make informed decisions promptly, minimizing the risk of fraudulent activities slipping through the cracks.

Adaptive Fraud Prevention: AI algorithms continuously learn from new data, evolving alongside fraudsters and adapting to new tactics. By leveraging machine learning, identity verification systems become increasingly adept at recognizing and preventing fraud, even as fraudsters employ more sophisticated methods.

Conclusion

The fight against fraud requires a multi-faceted approach, and artificial intelligence, combined with robust identity verification, is proving to be a powerful weapon in this battle. With AI's ability to analyze vast amounts of data, detect patterns, and identify anomalies in real-time, businesses can stay ahead of fraudsters and prevent fraudulent activities before they cause significant harm.

As technology continues to advance, fraudsters will undoubtedly become more sophisticated. However, the combination of AI and identity verification provides a robust defense that empowers businesses to fight fraud effectively. By leveraging the power of AI, we can create a safer digital environment where trust and security prevail.

by Web Desk via Digital Information World

Your Devices May Have Hacker Protection, but Loved Ones Are Snooping Too

When most of us think about digital privacy, we imagine attacks from scammers, hackers, and data thieves. However, what about the person sitting next to you in the living room? It turns out that the overwhelming majority of Americans admit to digital snooping, or accessing someone’s information on a digital device without their permission.

A recent Secure Data Recovery survey asked Americans about their digital snooping habits and opinions to gain a better understanding of when and why people snoop—and who is most likely to do it. Spoiler alert: it’s usually the people closest to us.

Digital Snooping Is Common

The survey found that 82% of respondents have snooped through someone else’s device. While most Americans admit to digital snooping, some do it more than others. 85% of millennials, who came of age during the rise of smartphones and social media, are more likely to snoop than Gen Z (77%). Gender also appears to play a role, as women are more likely to snoop than men.

With this many people admitting to snooping, it’s also interesting to note who regrets it after. While most people feel bad after peeking at their loved one’s devices, over 1 in 3 snoopers report not feeling guilty about it at all. Perhaps these snoopers feel their behavior is justified—or maybe they just haven’t been busted yet. In fact, a surprising 81% of Americans have never been caught snooping.

Snooper’s remorse seems to vary by age and gender. Men are more likely to regret it than women, and (perhaps surprisingly) baby boomers are the generation least likely to regret snooping.

Why do some people choose not to snoop? Only 10% said it was because they believe snooping is not appropriate. Another 10% said it was because they have had no reason to, suggesting they might do so if they felt the need.

What’s Snooped Through and Why?

When people decide to snoop, there are clear patterns in what kind of things they look at.

• Messages: Almost all snoopers (87%) look at messages when they secretly access someone’s device. This category represents a large quantity of information including text messages, emails, and social media DMs.
• Photos/videos: While snoopers may not find pictures as interesting as texts, they apparently still find them worth peeking at. Nearly half (44%) of survey respondents admit snooping around in other people’s photos or videos.
• Browser history: Snoopers also want to know what their loved ones are looking at online, as over 38% check out browser history.
• Private notes: A smaller group of snoopers (12%) look at a device’s private notes.
• Location history: 9% want to know where their loved ones have been by checking out their location history.

Perhaps so many people snoop because they are often rewarded for doing so. Over half of survey respondents have found something incriminating when looking through someone else’s device, suggesting that people may snoop to validate suspicions they already have.

When snoopers make concerning discoveries, 70% said they found evidence of cheating or inappropriate flirting, whether digital or in person. Some 17% caught someone in a lie that was unrelated to relationships, and a small percentage even discovered illegal activity.

Biggest Snooping Targets

Perhaps unsurprisingly, most digital snooping involves romantic partners, indicating that trust issues and insecurities often accompany romantic relationships. However, people also admit to snooping on the devices of other family members, friends, and even work colleagues.

Two-thirds of survey respondents admitted to snooping on their current or former significant other, using digital devices as an easy way to investigate their activities. Perhaps slightly more surprising is that almost as many people snoop on their ex-partners (28%) as their current partners (38%).

Next on the list, 9% admitted to snooping on their children’s devices, showing parents are monitoring their kids’ online activity. Finally, 8% of Americans snoop on friends’ devices. As to why, your guess is as good as ours: Curiosity? Jealousy? Concern?

While not in the majority, a few people also report snooping on their siblings (7%), parents (7%), or coworkers (3%).

What Can You Do About Snooping?

With our lives increasingly conducted online, the temptation to snoop is ever present. Now that you know how prevalent digital snooping really is, it’s up to you to decide how to handle it in your own life. It’s smart to set healthy boundaries and work on building trust with people you know. If you feel the need to snoop, you may want to ask yourself how that reflects on you or your relationship.

Psychological questions aside, you can take technological steps to prioritize your digital privacy. Many basic digital security measures can help prevent snooping, like making smart password choices, using two-factor account authentication, and enabling the Find My iPhone/Device feature. Another way to keep sensitive information from roaming eyes is to level up your lock-screen game. Enable automatic lockout with a screen saver and limit the notifications that show on your lock screen.

If important information of yours has already been compromised or deleted, you may need the help of a professional data recovery service to restore your peace of mind. If not, make sure to take steps today to reduce the chances of snooping or other digital dangers compromising your personal information.

Methodology

Secure Data Recovery asked over 1,000 people across the United States about their digital snooping habits and opinions. Respondents’ ages ranged from 18 to 76 years old.

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by Irfan Ahmad via Digital Information World

Express Freely with Bing Chat’s New Update! Bing Chat Doubles its Character Limit!

Microsoft's Bing Chat, a platform powered by advanced chatbot AI, has recently undergone a significant update that is set to enhance the messaging experience for its users. By increasing the character limits from 2,000 to 4,000, Bing Chat aims to provide users with the ability to ask more complex questions and engage in more in-depth conversations with the chatbot. This improvement comes as part of Microsoft's ongoing efforts to refine its AI technologies and cater to the evolving needs of its users. Alongside other recent features and advancements, the expanded character limits in Bing Chat signify Microsoft's commitment to delivering a comprehensive and user-friendly chatbot experience.

It has made a significant update to its messaging capabilities, increasing the character limits from 2,000 to 4,000. The Bing Chat team at Microsoft has been actively working on enhancing the chatbot AI by introducing new features and improvements. While these updates are usually announced through press releases or blog posts, the latest improvement was revealed in a simple Twitter message.

The Advertising and Web Services leader at Microsoft, Mikhail Parakhin took to Twitter to reveal an exciting development in Bing Chat. Through a shared image, Parakhin showcased a blank Bing Chat question that presented a notable change. Rather than the standard 0/2000 character count, the updated image showcased 0/4000, indicating that Microsoft has expanded each message's character limit in Bing Chat to 4,000 characters.

Prior to this update, Bing Chat users were restricted to a character limit of 2,000 for their questions. However, critics have voiced their concerns online, stating that this limit was insufficient for asking more complex inquiries to the chatbot. Microsoft previously expressed its apprehension about exceeding the 2K character limit, as it had the potential to confuse the chatbot AI.

Microsoft's confidence in expanding the character limit of Bing Chat to 4,000 stems from the valuable development and experience gained since the chatbot's initial launch earlier this year. The company believes that the enhancements made to the chatbot's capabilities have resolved any potential confusion that could arise from longer questions or messages. This step reflects Microsoft's commitment to improving the functionality and user experience of Bing Chat.

Microsoft has been actively adding features to Bing Chat, demonstrating its commitment to its advancement. Just last week, the long-awaited feature, chat history was made available to all users, addressing a frequent user request. Additionally, new export options were introduced, allowing users to save their chats in Word, text, or PDF files.

More updates on Microsoft's Bing Chat and AI innovations are expected to be announced at the upcoming Build Developers conference, scheduled to commence on May 23. The conference will serve as a platform for Microsoft to share further insights and advancements in their AI technologies.

With the expanded character limits and the continuous efforts to improve Bing Chat's functionalities, Microsoft aims to provide a more comprehensive and user-friendly experience for its chatbot users. These updates reflect the company's commitment to enhancing the capabilities of its AI-driven services.

Read next: The Reliability of AI Detecting Software in Question: ChatGPT Content Goes Undetected
by Ayesha Hasnain via Digital Information World

The Reliability of AI Detecting Software in Question: ChatGPT Content Goes Undetected

Following the release of ChatGPT and the subsequent emergence of detecting software, various developers and companies have introduced their own Artificial Intelligence (AI) algorithms aimed at identifying content produced by other AI systems. These detecting software have been positioned as invaluable tools for educators, journalists, and individuals seeking to uncover instances of misinformation, plagiarism, and academic dishonesty. However, a recent study conducted by scholars at Stanford University has cast doubt on the reliability of these detecting software, particularly when evaluating content generated by non-speaker of the English language.

The study's findings reveal a concerning reality. While the detecting software demonstrated impressive accuracy in assessing written essays by American 8th-grade students, their performance noticeably declined when analyzing essays written by non-speaker of the English language taking the Test of English as a Foreign Language (TOEFL). Surprisingly, the detecting software incorrectly identified a significant portion of the TOEFL essays, falsely categorizing them as AI-generated.

Moreover, the study unveiled a striking revelation: all 7 detecting software randomly labeled a substantial number of the written essays of the students of TOEFL as AI-generated. Astonishingly, at least one detector flagged an overwhelming majority of these essays. James Zou, a senior author of the study and a professor specializing in biomedical data science, explains that this issue arises from the detecting software' heavy reliance on a specific metric associated with writing sophistication.

This metric, intricately linked to language complexity, encompasses various linguistic factors such as lexical richness, diversity, and syntactic and grammatical intricacies. Unfortunately, non-speaker of the English language typically exhibit lower scores on this metric, posing a significant challenge for the detecting software.

The authors of the study, including Zou and his colleagues, emphasize the profound implications of their findings. They draw attention to the potential for unjust accusations and penalties faced by individuals who are foreign-born students or workers due to the inherent unreliability of detecting software. Ethical concerns arise, cautioning against relying solely on existing detecting software as a comprehensive solution to combat AI cheating.

Zou further highlights the vulnerability of detecting software to a phenomenon known as "prompt engineering." This practice involves manipulating generative AI systems by instructing them to revise content using more advanced language, enabling students to easily circumvent the detecting software. Zou provides a straightforward example of how a student could exploit ChatGPT for cheating purposes by inputting the AI-generated text with a prompt to enhance it using sophisticated literary expressions.

To address these challenges, Zou proposes several potential actions. In the short term, he recommends minimizing dependence on detecting software in educational settings with a substantial population of non-speaker of the English language or individuals with limited English proficiency. Developers should explore alternative metrics beyond the one currently used and consider implementing techniques such as embedding subtle clues or watermarks in AI-generated content. Additionally, efforts should be made to enhance the robustness of models against manipulation to improve their overall effectiveness.

As the study raises questions about the reliability and objectivity of detecting software, the search for more robust and equitable solutions to combat AI cheating continues.

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by Ayesha Hasnain via Digital Information World

U.S. Paid Search Spend to Reach $110 Billion in 2023

According to a recent eMarketer/Insiderintelligence projection, US ad search spending would reach a record-breaking $110 billion by the end of 2023.

A recent eMarketer report predicts that this year will mark the first time the U.S. branded search industry surpasses $110 billion. The US is actually experiencing an increase in online marketing spending of 8.2%, which is a little quicker than planned.

Retail media networks (RMNs) are an area of the search industry that is expanding quickly. By 2023, it is predicted that spending on retail media search will have increased by 18.7% from its present levels to close to $30 billion.

If spending keeps up, RMN's digital ad income may reach $106 billion by 2027, from $31 billion in 2021 to $45 billion this year. This number includes more than just searches.

It is clear why digital advertising is so costly, given that sponsored search represents 41.8% of total spending. Businesses spend more on search advertising as customers use the internet for product research and purchasing.

To stay competitive and boost sales, brands have discovered the need for a solid online presence on paid search platforms like Google Ads and Bing Ads. The tendency is expected to remain throughout the years that follow.

Marketers now have more alternatives thanks to the rising popularity of RMNs when optimizing their campaigns for more excellent performance and ROI, enabling them to make even more of their budgets while still getting the intended outcomes.

Be prepared for a tough year in search as innovations connected to generative A.I. and conversation on Google and Microsoft's Bing are anticipated to shake things up. Despite the uncertainties, the good news is that overall paid search spending is increasing, especially in retail.

Despite a drop in digital advertising consuming this year, the United States nevertheless made a record-breaking $84.4 billion from search advertising. The projected rise in media spending from print to digital will reach 11.2% yearly by 2024.

Connected TV advertising is expected to generate over $25 billion in revenue in 2021, or 9.5% of the entire revenue from online advertisements. Online advertising spending has risen significantly thanks to the lucrative promoted search industry.

To remain competitive and maximize ROI, paid search marketers should monitor RMNs and other market changes. Brands can plan for the future and put themselves on a successful path by making the appropriate investment now.

Read next: Neil Mohan Reveals YouTube's Record-Breaking $40 Billion Revenue

by Arooj Ahmed via Digital Information World