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
Quantum Mechanics created their chapter in the history of the early 20th Century. With its regular binary computing twin going out of style, quantum mechanics led quantum computing to be the new belle of the ball! While the memory used in a classical computer encodes binary ‘bits’ – one and zero, quantum computers use qubits (quantum bits). And Qubit is not confined to a two-state solution, but can also exist in superposition i.e., qubits can be employed at 0, 1 and both 1 and 0 at the same time.
Article | September 2, 2021
In this data-driven age, marketers have access to all the necessary information about their customers. There are different tools they can use to capture the exact data required for specific campaigns. We have come a long way from mass broadcasting campaigns. Growth in digital marketing has given rise to pinpointed targeting.
The marketing industry is still accepting and learning data technology. However, there is more importance given to the content creation side of things when there should be a clear balance between data-driven efforts and content. It can be challenging to re-route pure content creator’s attention to data marketing, but it cannot be ignored for long. It is no longer enough to rely on gut instinct and ‘good content’.
The rise in popularity in data-driven marketing has been lead by the revolution in big data. Big data has enabled massive amounts of data to be collected, analyzed, and organized, which helps in creating a personalized customer experience.
Since the start of the Covid-19 pandemic, more and more people have started to spend time online. As a result, online user behavior has changed in just a matter of months rather than years.
Data-driven marketing efforts can also help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their business online.
We have highlighted the steps you need for successful data-driven marketing.
5 Steps to Take for Successful Data-Driven Content Marketing
For any campaign to succeed, it is imperative to have a list of attainable objectives. You can set these objectives by studying historical data and know-how your marketing campaign will perform.
For a successful content marketing strategy, make sure to concentrate on raising brand awareness, retaining current customers, and tracking sales.
If you're not putting out relevant content in relevant places, you don't exist.
Gary Vaynerchuk, -American entrepreneur, author, speaker, and Internet personality.
Customize Campaigns for Target Audience
Before you create any data-driven campaigns, know your customers well. Then, with the abundance of data at your fingertips, you can easily create personalized campaigns for them.
Figure out and solve any problems they may be facing, if they need any solution, what they’re looking for and where.
Creating user profiles will help you avoid targeting generalized strata rather than help you be more precise in your marketing efforts.
Regular Content Optimization
One of the best ways to ensure successful marketing results is through content optimization. Google algorithms are constantly changing. So what’s ranking on the first page today may not always rank the same next day.
Set campaign-specific KPIs and work towards achieving those targets. Use different tools to track whether your campaign is working according to your goals or needs some serious upliftment.
Keep running SEO audits on your pages regularly to keep your content in the best shape possible.
Repurposing content is the oldest trick in the book to gain a higher ROI on your existing content. For instance, if you have an article published on your company’s website, adapt that blog into an infographic and publish it on various social platforms. Content repurposing will help you boost your SEO, reach a broader and newer audience, help drive traffic to your website, and raise your brand’s awareness.
Every platform has a different reach. Use your platforms according to the KPIs you have set for your business. For example, Twitter can help you raise your brand’s awareness, while LinkedIn will help you generate leads. Different platforms will have different metrics you will need to track.
There are online tools available that help marketers track metrics. Each of these metrics will help you to achieve your marketing objectives.
In today’s competitive market, content marketing will have to be data-driven. The data-first approach will help you and your business in reaching the maximum number of people. In addition, a performance-oriented approach will ensure the success of your campaigns.
Investing in high-quality marketing technologies will help you get balanced, data-driven, and goal-oriented results preparing you to become a content marketer ready to take on any challenges.
Frequently Asked Questions
What is the future of content marketing?
Data-driven content marketing strategies can help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their online business.
What are the top content marketing trends for 2022?
1. Layout Objectives
2. Customize Campaigns for Target Audience
3. Regular Content Optimization
4. Content Repurposing
5. Track Analytics
How is content-based marketing a proven strategy?
Content-based marketing is a marketing strategy designed to attract, engage, and retain target audience. This works by creating and sharing relevant content such as articles, podcasts, infographics, videos, and other content marketing materials. This approach lays down expertise, helps brand awareness.
"name": "What is the future of content marketing?",
"text": "Data-driven content marketing strategies can help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their online business."
"name": "What are the top content marketing trends for 2022?",
A. 1. Layout Objectives
2. Customize Campaigns for Target Audience
3. Regular Content Optimization
4. Content Repurposing
5. Track Analytics"
"name": "How is content-based marketing a proven strategy?",
"text": "Content-based marketing is a marketing strategy designed to attract, engage, and retain target audience. This works by creating and sharing relevant content such as articles, podcasts, infographics, videos, and other content marketing materials. This approach lays down expertise, helps brand awareness."
Article | September 2, 2021
The financial industry has been going through digital transformation for years. Digital technologies have helped to automate manual and tedious tasks like processing and reporting of historical data to forecasting and financial predictive analytics.
The financial services industry owes its success to data. Data is constantly evolving in the form of market trends, client investment, customer service, campaigns. Data gives a boost to banking strategies. As reported by Accenture in a recent survey, 78 percent of banks have made the shift to using data for operations; however, only seven percent of them have extended to using predictive analytics in finance.
Predictive analytics in finance has had a slow but steady start. It is an area of growing interest for banks and other institutions as new newer technologies launch in the market. To complete your company’s digital transformation, data analytics in finance will make a difference in that process.
To be successful, organizations must have the ability to adapt to changes.
Having predictive analytics on your side, your organization can deal with ever-changing circumstances with less to no difficulty.
Understanding Predictive Analytics: What is it?
Predictive analytics is a process of interpreting data to measure any possible future outcomes. It is carried out with the help of statistical modeling, historical data sets, and machine learning. The collected historical data is fed into an algorithm that recognizes patterns and forecast trends and possible future behavior from days to years in advance.
Analyzing historical data and predicting the future has been an old practice in the finance sector. Banks and financial institutions have been evaluating past events or historical data for a long time now.
Making precise forecasts in trends and analyzing data becomes easier due to predictive analytics.
There is a wider scope to predictive efforts with more speed and accuracy and apply them throughout strategic and tactical business practice areas.
Predictive Analytics in the Financial Sector: What are the Benefits?
Many organizations are ready to accept the positive applications of predictive analytics but remain skeptical about the return on investment.
It is worth understanding the potential of predictive analytics to any business big or small. It doesn’t matter if you are not in the banking sector to benefit from taking a peek into the future of financial performance.
Any finance and accounting department can take advantage of advanced predictive analytics for the following reasons:
The technology keeps a regular track of the consistency between expectations and reality to warn you about possible gaps.
Analytics accurately helps you identify any possible threats to your business and warns you.
Enhanced User Experience
Predictive analytics guides you to recognize the strengths of your business and lets you know how to maximize customer satisfaction.
Analyzed Decision Making
You can understand your customers better with predictive analytics. With this information, you can correctly match your customers with the product in a better way.
Importance of Predictive Analytics
Most successful banking and financial institutions depend on predictive analytics because it simplifies and integrates data to increase profits for companies. Predictive analytics can improve different finance processes.
But the importance of analytics goes beyond just banking services and actually goes into a better quality of customer service. Better customer service is only possible because of the advanced technology that shares customer feedback and preferences throughout the organization, in turn giving relevant information to every employee to make necessary product enhancements.
To understand the importance of predictive analytics, below are some of its use cases:
Predictive analytics in financial institutions and banking give you a complete profile of your customer base. It is impossible to contact every customer and interview them about their likes, needs and wants. This is where big data analytics in finance comes into play. It gives you the whole information about your customers regardless of the services they subscribe.
Customers usually don’t have the same needs throughout their lives. As they grow older and have families, their financial needs change accordingly. For instance, a young person considering getting married will always try and save monetarily to buy a house, life insurance, college funds, whereas an older couple will save that money for their retirement.
Apart from enabling different financial services, predictive analytics empowers you to serve individual customers with ease. Let’s take an example. When a customer applies for a loan, predictive financial services can help you analyze if the customer can repay the loan.
Predictive analytics also helps offer alternative services like secured loans to customers who may not qualify for the originally applied services.
Online Banking Made Better
Consumer interest fluctuates in spikes. Predictive analytics informs managers enough in advance so they can set up online infrastructures in those areas. Predictive analytics has made it easier to identify a possible customer base. For example, it can provide metrics to the marketing teams. In turn, the marketing teams can target the customers with ads for probable mortgage loans or business loans in hopes of converting them into their customers.
Data analytics in finance also helps in preventing and detecting fraud and abuse. Although detecting fraud doesn’t necessarily fall under predictive analytics, it can inform the IT department about potential scammers and which online services must be protected.
Foreseeing Market Variations
Predictive analytics can predict market variations and changes. By combining internal and external data, your organization can predict revenue growth in particular market sectors.
For nascent or growing companies, predicting market changes is an important ability. Profitable companies should also be reviewed through predictive analytics to generate demand projections owing to the uncertainties caused by the Covid-19 pandemic. Your return on investment can grow or reduce even with the minutest changes to the growth plans that would seriously impact investor confidence in the future.
Predictive analytics also help to establish which marketing campaigns are working and which strategies need to change.
Predictive Analytics and the Future: What Next?
Technological improvements have allowed predictive analytics in finance to improve and change constantly. Any organization can use customized data solutions to meet your customers’ needs and reach new ones efficiently. Your organization can use predictive analytics to move your business and products ahead and understand how the market will thrive, giving you the much needed heads up you would need to change your strategies and tactics.
Frequently Asked Questions
Is predictive analytics is the future of finance?
Predictive analytics is called the ‘future of financial software,’ which means it can provide accurate planning and cost-effectiveness.
How can analytics be used in finance?
Analytics helps in predicting revenue, improve supply chains, identify trouble spots, understand where the company is bleeding money, and fraud detection.
How do predictive analytics benefit financial institutions?
Predictive analytics can help financial institutions and customers detect fraud, financial management, predicting markets, improving products, better user experience, etc.
"name": "Is predictive analytics is the future of finance?",
"text": "Predictive analytics is called the ‘future of financial software,’ which means it can provide accurate planning and cost-effectiveness."
"name": "How can analytics be used in finance?",
"text": "Analytics helps in predicting revenue, improve supply chains, identify trouble spots, understand where the company is bleeding money, and fraud detection."
"name": "How do predictive analytics benefit financial institutions?",
"text": "Predictive analytics can help financial institutions and customers detect fraud, financial management, predicting markets, improving products, better user experience, etc."