Article | September 2, 2021
In 2020, the gaming market generated over 177 billion dollars, marking an astounding 23% growth from 2019. While it may be incredible how much revenue the industry develops, what’s more impressive is the massive amount of data generated by today’s games.
There are more than 2 billion gamers globally, generating over 50 terabytes of data each day. The largest game companies in the world can host 2.5 billion unique gaming sessions in a single month and host 50 billion minutes of gameplay in the same period.
The gaming industry and big data are intrinsically linked. Companies that develop capabilities in using that data to understand their customers will have a sizable advantage in the future. But doing this comes with its own unique challenges.
Games have many permutations, with different game types, devices, user segments, and monetization models. Traditional analytics approaches, which rely on manual processes and interventions by operators viewing dashboards, are insufficient in the face of the sheer volume of complex data generated by games.
Unchecked issues lead to costly incidents or missed opportunities that can significantly impact the user experience or the company’s bottom line. That’s why many leading gaming companies are turning to AI and Machine Learning to address these challenges.
Gaming Analytics AI
Gaming companies have all the data they need to understand who their users are, how they engage with the product, and whether they are likely to churn. The challenge is gaining valuable business insights into the data and taking action before opportunities pass and users leave the game.
AI/ML helps bridge this gap by providing real-time, actionable insights on near limitless data streams so companies can design around these analytics and act more quickly to resolve issues. There are two fundamental categories that companies should hone in on to make the best use of their gaming data:
The revenue generating opportunities in the gaming industry is one reason it’s a highly competitive market. Keeping gamers engaged requires emphasizing the user experience and continuous delivery of high-quality content personalized to a company’s most valued customers.
Customer Engagement and User Experience
Graphics and creative storylines are still vital, and performance issues, in particular, can be a killer for user enjoyment and drive churn. But with a market this competitive, it might not be enough to focus strictly on these issues.
Games can get an edge on the competition by investing in gaming AI analytics to understand user behaviors, likes, dislikes, seasonality impacts and even hone in on what makes them churn or come back to the game after a break.
AI-powered business monitoring solutions deliver value to the customer experience and create actionable insights to drive future business decisions and game designs to acquire new customers and prevent churn.
AI-Enhanced Monetization and Targeted Advertising
All games need a way to monetize. It’s especially true in today’s market, where users expect games to always be on and regularly deliver new content and features. A complex combination of factors influences how monetization practices and models enhance or detract from a user’s experience with a game.
When monetization frustrates users, it’s typically because of aggressive, irrelevant advertising campaigns or models that aren’t well suited to the game itself or its core players. Observe the most successful products in the market, and one thing you will consistently see is highly targeted interactions.
Developers can use metrics gleaned from AI analytics combined with performance marketing to appeal to their existing users and acquire new customers. With AI/ML, games can use personalized ads that cater to users’ or user segments’ behavior in real-time, optimizing the gaming experience and improving monetization outcomes.
Using AI based solutions, gaming studios can also quickly identify growth opportunities and trends with real-time insight into high performing monetization models and promotions.
Mobile Gaming Company Reduces Revenue Losses from Technical Incident
One mobile gaming company suffered a massive loss when a bug in a software update disrupted a marketing promotion in progress. The promotion involved automatically pushing special offers and opportunities for in-app purchases across various gaming and marketing channels. When a bug in an update disrupted the promotions process, the analytics team couldn’t take immediate action because they were unaware of the issue.
Their monitoring process was ad hoc, relying on the manual review of multiple dashboards, and unfortunately, by the time they discovered the problem, it was too late. The result was a massive loss for the company – a loss of users, a loss of installations, and in the end, more than 15% revenue loss from in-app purchases.
The company needed a more efficient and timely way to track its cross-promotional metrics, installations, and revenue. A machine learning-based approach, like Anodot’s AI-powered gaming analytics, provides notifications in real-time to quickly find and react to any breakdowns in the system and would have prevented the worst of the impacts.
Anodot’s AI-Powered Analytics for Gaming
The difference between success and failure is how companies respond to the ocean of data generated by their games and their users. Anodot’s AI-powered Gaming Analytics solutions can learn expected behavior in the complex gaming universe across all permutations of gaming, including devices, levels, user segments, pricing, and ads.
Anodot’s Gaming AI platform is specifically designed to monitor millions of gaming metrics and help ensure a seamless gaming experience. Anodot monitors every critical metric and establishes a baseline of standard behavior patterns to quickly alert teams to anomalies that might represent issues or opportunities.
Analytics teams see how new features impact user behavior, with clear, contextual alerts for spikes, drops, purchases, and app store reviews without the need to comb over dashboards trying to find helpful information.
The online gaming space represents one of the more recent areas where rapid data collection and analysis can provide a competitive differentiation. Studios using AI powered analytics will keep themselves and their players ahead of the game.
Article | September 2, 2021
Decision-makers at consumer brands are finally realizing the full transformative potential of external data - but they’re also realizing how difficult it is to source. Forrester reports that 87% of decision-makers in data and analytics have implemented or are planning initiatives to source more external data. And those initiatives are growing outside of the IT team; 29% of those surveyed say that IT has primary ownership of data sourcing, down from 37% in 2016. To support these projects, organizations are increasingly turning to a new specialist: the data hunter, who identifies and vets external data sources. It’s a lot of work to build external data-focused teams, and many leaders are realizing that external data is difficult to scale as the source list grows. Perhaps that’s why 66% of those decision-makers surveyed by Forrester report that they’re using or planning to use external service providers for data, analytics, and insights.
Article | September 2, 2021
Most businesses do not have contingency or business continuity plans that correlate to the world we see unfold before us—one in which we seem to wake up to an entirely new reality each day. Broad mandates to work at home are now a given. But how do we move beyond this and strategically prepare for—and respond to—business implications resulting from the coronavirus pandemic? Some of our customers are showing us how. These organizations have developed comprehensive, real-time operational intelligence views of their global teams—some in only 24-48 hours—that help them better protect their remote workforces, customers, and business at hand.
BIG DATA MANAGEMENT
Article | September 2, 2021
While digital transformation is proving to have many benefits for businesses, what is perhaps the most significant, is the vast amount of data there is available. And now, with an increasing number of businesses turning their focus to online, there is even more to be collected on competitors and markets than ever before.
Having all this information to hand may seem like any business owner’s dream, as they can now make insightful and informed commercial decisions based on what others are doing, what customers want and where markets are heading.
But according to Nate Burke, CEO of Diginius, a propriety software and solutions provider for ecommerce businesses, data should not be all a company relies upon when making important decisions.
Instead, there is a line to be drawn on where data is required and where human expertise and judgement can provide greater value.
Undeniably, the power of data is unmatched. With an abundance of data collection opportunities available online, and with an increasing number of businesses taking them, the potential and value of such information is richer than ever before.
And businesses are benefiting. Particularly where data concerns customer behaviour and market patterns. For instance, over the recent Christmas period, data was clearly suggesting a preference for ecommerce, with marketplaces such as Amazon leading the way due to greater convenience and price advantages.
Businesses that recognised and understood the trend could better prepare for the digital shopping season, placing greater emphasis on their online marketing tactics to encourage purchases and allocating resources to ensure product availability and on-time delivery.
While on the other hand, businesses who ignored, or simply did not utilise the information available to them, would have been left with overstocked shops and now, out of season items that would have to be heavily discounted or worse, disposed of.
Similarly, search and sales data can be used to understand changing consumer needs, and consequently, what items businesses should be ordering, manufacturing, marketing and selling for the best returns.
For instance, understandably, in 2020, DIY was at its peak, with increases in searches for “DIY facemasks”, “DIY decking” and “DIY garden ideas”. For those who had recognised the trend early on, they had the chance to shift their offerings and marketing in accordance, in turn really reaping the rewards.
So, paying attention to data certainly does pay off. And thanks to smarter and more sophisticated ways of collecting data online, such as cookies, and through AI and machine learning technologies, the value and use of such information is only likely to increase.
The future, therefore, looks bright. But even with all this potential at our fingertips, there are a number of issues businesses may face if their approach relies entirely on a data and insight-driven approach. Just like disregarding its power and potential can be damaging, so can using it as the sole basis upon which important decisions are based.
While the value of data for understanding the market and consumer patterns is undeniable, its value is only as rich as the quality of data being inputted. So, if businesses are collecting and analysing their data on their own activity, and then using this to draw meaningful insight, there should be strong focus on the data gathering phase, with attention given to what needs to be collected, why it should be collected, how it will be collected, and whether in fact this is an accurate representation of what it is you are trying to monitor or measure.
Human error can become an issue when this is done by individuals or teams who do not completely understand the numbers and patterns they are seeing. There is also an obstacle presented when there are various channels and platforms which are generating leads or sales for the business. In this case, any omission can skew results and provide an inaccurate picture. So, when used in decision making, there is the possibility of ineffective and unsuccessful changes.
But while data gathering becomes more and more autonomous, the possibility of human error is lessened. Although, this may add fuel to the next issue.
Drawing a line
The benefits of data and insights are clear, particularly as the tasks of collection and analysis become less of a burden for businesses and their people thanks to automation and AI advancements. But due to how effortless data collection and analysis is becoming, we can only expect more businesses to be doing it, meaning its ability to offer each individual company something unique is also being lessened.
So, businesses need to look elsewhere for their edge. And interestingly, this is where a line should be drawn and human judgement should be used in order to set them apart from the competition and differentiate from what everyone else is doing.
It makes perfect sense when you think about it. Your business is unique for a number of reasons, but mainly because of the brand, its values, reputation and perceptions of the services you are upheld by. And it’s usually these aspects that encourage consumers to choose your business rather than a competitor.
But often, these intangible aspects are much more difficult to measure and monitor through data collection and analysis, especially in the autonomous, number-driven format that many platforms utilise.
Here then, there is a great case for businesses to use their own judgements, expertise and experiences to determine what works well and what does not. For instance, you can begin to determine consumer perceptions towards a change in your product or services, which quantitative data may not be able to pick up until much later when sales figures begin to rise or fall. And while the data will eventually pick it up, it might not necessarily be able to help you decide on what an appropriate alternative solution may be, should the latter occur.
Human judgement, however, can listen to and understand qualitative feedback and consumer sentiments which can often provide much more meaningful insights for businesses to base their decisions on.
So, when it comes to competitor analysis, using insights generated from figure-based data sets and performance metrics is key to ensuring you are doing the same as the competition.
But if you are looking to get ahead, you may want to consider taking a human approach too.