4 Ways Advances In Behavioral Analysis Are Changing The Gaming Industry

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The global games market is expected to reach $ 90.7 billion by 2020. But game companies have a huge problem to overcome: their metrics. They often struggle to collect, interpret, and visualize user interaction data, which is imperative for understanding, learning, and forecasting behavioral trends.

However, it is not for lack of trying. The majority of online gaming companies tinker with data stored in many different places in an attempt to get a unified behavioral view. Unfortunately, this approach only shows chunks of the customer journey – very few companies actually understand the entire customer journey.

Game companies know that behavioral insights are the key to their competitive advantage. If they can make their game more engaging, entertaining, or more addicting than that of the next player, that is their advantage. This is why behavioral analysis is gaining ground in the gaming space. And they are changing the gaming industry in several ways:

1) Help small teams of game developers do more with less

Game development environments must be flexible to adapt to changes in user behavior. Most teams don’t have the luxury of having a full business intelligence and data warehousing team on staff to reconfigure analytics solutions for each release.

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A relevant example that comes to mind is social game company LuckyFish. Their entire business, from c-suite to finance, marketing and development team, has turned to analytics and uses it as a daily barometer to help them understand the success of its games, make decisions and better target and retain customers. The retention team analyzes both retrospective and forward-looking behavioral data, which indicates which user segments are at risk of unsubscribing. The data helps shed light on the issues driving user churn, helping the retention team respond with targeted campaigns. Then they use the same dashboards to understand whether the retention activity is effective or not.

2) Leverage AI to help understand the player’s journey from converting to unsubscribing

When your data is fragmented across multiple platforms, to get the answers you need, you need to manage the data manually. Many teams don’t have the expertise to dig deep into the data and gain meaningful insight. Often, this requires the skills of a university-trained data scientist. To overcome this limitation, companies are now starting to turn to artificial intelligence and machine learning.

In order to avoid user unsubscription, one must understand the real cause. By using AI, and not just click flow data or purchase data, the data goes much deeper. This creates the ability to analyze churn patterns to better predict and predict user turnover. With machine learning in play, product managers are better equipped to predict, diagnose, and correct the game, as often slight nuances in the data reveal what can be a very strategic and material trend.

For example: grouping users according to their behavior so that you can take different and appropriate actions for each one, identifying cohorts of users who are likely to convert to paying customers or, conversely, identifying users who are at risk of becoming paying customers. unsubscribe before that. come.

It used to be a manual, tedious, and error-prone effort, but leveraging AI and automation allows businesses to become much “smarter” and interact more effectively with their customers.

3) Reveal the issues surrounding player behavior

The challenge with traditional analytics is that each platform gives you a different set of data. There are many point solutions to help you understand web and mobile analytics. However, there is a huge range of so-called “unstructured data”, which is often overlooked and under-analyzed. This can include customer chat messages, information about customer success, and more. Natural Language Processing (NLP), a machine learning technique, can be used on chat data to infer the sentiment of a particular user in a conversation. Running an NLP analysis on chats can generate reports that paint a much more nuanced picture of customer satisfaction.

Another way to use behavioral analytics to drive business value is to predict and prevent churn. If you analyze a list of 100,000 users by profile attributes such as gender, age, level of in-app activity, behavior patterns, and historical unsubscribe data, you can generate predictions that rank their likelihood of unsubscribing from high to low. From there, you can group your users based on their behavioral attributes to identify user groups represented in the “highly likely” cohort to unsubscribe.

Analyzing this generated raw event data provides marketers, product managers and executives with countless new insights into the behavior of their players. This, in turn, allows them to run much more effective marketing campaigns and drastically reduce player churn by using this combined set of previously unavailable data.

4) Improve the overall path of the conversion strategy

While there are some pretty intuitive ways to track your game’s success, it can be difficult to convert that information into strategies for growth and retention.

With all of the metrics touted as the best thing you can do to help improve your marketing or monetization, it can be pretty easy to get lost in all the noise. However, there are a few metrics that, when measured and tracked, allow game marketers to create a data-driven conversion strategy.

One of the best metrics for tracking the development of your SEO strategy is called the k factor. The k factor of your game is the number of invitations sent by each customer in your app multiplied by the conversion of each invite.

The k factor can be calculated as:

This metric perfectly sums up the effectiveness of your referral growth strategy, making it a quantifiable metric that can then be compared at different points in your game’s development to measure the effectiveness of your approach.

As a derivative of the k-factor metric, game marketers should measure the number of invitations sent versus daily active users (DAU). Instead of measuring your referral conversion rate, however, this metric gives a more complete picture of how your referral program is retaining users who have downloaded and played your app.

For games with a built-in currency system, they should also consider source, sink, and flow metrics. The source metric indicates the amount of currency a user has earned as they progress through the game. The well metric indicates the stages at which a user must spend the currency to advance or compete with other players. . The flow metric is a measure of both sources and sinks: it is the total currency balance that a player has earned and spent over a period of time. This metric lets you see how you can push a player towards conversions or in-app purchases.

If you know how users spend their in-game currency, you’ll be able to fully understand user engagement and the thought process of people playing your game through source, sink, and stream data.

Looking ahead, the next phase of gaming software advancement will rely heavily on the integration of behavioral analysis to understand player trends and preferences. With this kind of information, games can be calibrated in real time to grab users’ attention, increase purchases, and increase results.

Dan Schoenbaum is CEO of Cooladata, a predictive and behavioral analytics platform, uses cohort analytics to bring advanced analytics solutions to fast-growing digital businesses that rely heavily on behavioral insights from people. users.

Evan Kaeding is Data Strategist at Cooladata. Drawing on a background in finance and investments, Evan has led teams to use data to make investment decisions and improve operational efficiency.

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