Gaming information is among the most valuable sources of data available.
Companies are resting on a wealth of information, given that 16% of U.S. gamers’ weekly leisure time was devoted to gaming in 2018 and there are 2.7 billion video gamers worldwide. An analytics solution and data strategy are imperative to effectively utilize the provided information.
- What narratives do the actions of your participants convey to you?
In what ways are you implementing this knowledge to establish a - prosperous enterprise?
Are you optimizing the utilization of the insights that you are accumulating? - The objective of Indicative is to assist organizations in maximizing the value of their data and understanding how those figures relate to critical performance indicators.
We hope that you will depart with insights on how to construct a successful Customer Analytics strategy for the product and marketing departments of your organization.
Is Gaming Analytics defined?
An effectively defined strategy for gaming analytics enables both teams and individuals to make decisions based on data. Every member of an organization should be cognizant of how their actions impact revenue-oriented results at all times.
The gaming industry is undergoing accelerated change, which places gaming companies under performance pressure. The atmosphere is highly charged, providing participants with a boundless variety of activities to occupy their meager leisure time.
To surmount obstacles to success in the gaming industry, organizational teams must endeavor to uncover enduring narratives that cultivate brand equity and contribute to significant growth metrics by working more intelligently, rather than harder.
One analyst eloquently defines what we mean as follows:
Dan Robinson writes, “As captivating as the narratives, authentic as the visuals, and evocative as the music are, it is the data — the unsung hero — that is prodding the gaming industry forward.”
In light of this context, gaming analytics is informing product, marketing, and business decisions with user behavior data. Users of a gaming company are, in fact, gamers.
Teams can utilize the insights generated by Indicative to improve game design, monetization, and business impact through their decision-making.
An effective gaming analytics strategy will enable all members of your team to ponder and respond to the subsequent inquiries:
- What is the daily active user (DAU) count for a particular game?
- In what month do the most active users participate (MAU)?
- Who joined the user base during the previous month?
- How frequently did they revisit?
- Which level of the game do participants become stuck on?
- Does this result in a reduction in in-game usage?
- Which in-app or in-game transactions occurred?
- What types of circumstances motivated their purchase?
Although feasible, these are nuanced questions that would be difficult to answer programmatically; even proficient data experts struggle to compose queries at the same rate as intuitive decision-making. Consequently, gaming analytics is also concerned with the usability of data. The value of data is proportional to the capacity of an organization to comprehend and respond to it.
Analytics of Customers:
A Location Where Groups Can Discover Common Ground
Customer Analytics aims to improve data accessibility and comprehension, thereby facilitating the recognition of patterns. All employees of an organization can collaborate in pursuit of establishing a more robust enterprise with the assistance of this collective understanding.
Establishing a collaborative interface that links all employees to a solitary variety is among the most influential measures a business can undertake. Individuals will then be able to comprehend how their actions contribute to the achievement of organizational objectives.
Indicative dashboards enable the hosting of a compilation of KPIs and metrics that undergo near-real-time updates, facilitating the monitoring of routine business performance. Initially, it is advantageous to establish a shared comprehension of a fundamental revenue narrative. ARPU (average revenue per user), DAU (daily active users), and MAU (monthly active users) are straightforward KPIs that can be measured with ease using an analytics platform such as Indicative.
Additionally, you can investigate customer journeys in real time using this data. Suppose, for instance, that you wish to examine the pathways through which your most valuable players (those with the highest ARPU) access your email campaigns, registration page, and game download pages. The answers to these inquiries can be visually represented using Indicative.
One might observe an increase in the number of daily active users and wish to investigate the potential ramifications for the revenue of the organization. Indicative can be utilized to comprehend the underlying dynamics of this trend. Your customer analytics strategy is predicated on identifying relationships between revenue, individuals, and their actions.
The Function of Analytics for Big Data in Gaming
With the implementation of a consumer analytics strategy for your gaming company, the term “big data” will almost certainly be mentioned. According to IBM, “big data” refers to datasets that are either too large or too complex for conventional relational databases to efficiently capture, manage, and process in terms of latency. Big data is distinguished by one or more of the subsequent attributes: substantial volume, rapid velocity, or diverse variety.
Initial forays into big data innovation were confined to large enterprises and e-commerce firms. However, startups, small businesses, and mid-market companies spanning diverse industries are now equipped with the means to enhance customer insights, optimize product performance, and conduct more effective marketing campaigns, among other things.
Big data is crucial in the gaming industry for trend monitoring, problem diagnosis, and game design enhancement. A gaming company’s reliance on a clearly defined business intelligence and customer analytics strategy is growing in importance.
As an illustration, consider the history of King Digital Entertainment, the developers of Candy Crush.
“For unknown reasons, users abandoned level 65 in large numbers.” Given the 725 total levels in Candy Crush Saga, this tendency posed a significant obstacle. King enlisted the assistance of data analysts to disclose that the majority of players were discontinuing due to a specific gameplay element that prevented them from reaching level 65. “After removing the element, user retention began to progress once more.”
Big data aims to provide a more comprehensive understanding of your gaming analytics strategy. You will require both back-end technology and a customer analytics solution that renders this data interpretable to attain this level of insight.
Utilizing Predictive Analytics in the Gaming Sector
Microsoft and other enterprise players in the gaming industry have begun to recognize the utility of data analytics in recent years. As a result, they have been engaging in the acquisition of firms that grant them access to participant data.
This results in smaller developers encountering difficulties in matching the substantial financial investments typically allocated to product R&D, marketing, and data science that are typical of large corporations.
For this reason, gaming companies are progressively turning to solutions such as predictive analytics, which forecast player actions, to gain a competitive edge in the marketplace.
Image Source: PwC
The goal of predictive analytics in the gaming industry, according to PwC, is to create statistical models that ingest both historical and current data to calculate scores, risks, and predictions based on an outcome. For instance, predictive models can help gaming companies influence in-game purchases, prevent churn, and optimize lifetime value.
Broadly speaking, there are a few steps that you can expect to take:
- Establishing outcomes — Determine your goals and your vision for your predictive models — what metrics and KPIs are worth predicting, to support monetization for your business?
- Managing data — Build your long-term data collection and data warehouse infrastructure to support passive information absorption.
- Quantitative model development — Determine the right statistical techniques to set up your predictive models.
- Training — Train your models to ensure that they are performing effectively.
- Continuous iteration — Make relevant adjustments.
- Launch — Deploy your model into a live environment.
- Adjusting — Continue adjusting and building upon your model.
Here are several use cases, according to the Data Science blog KDNuggets, for predictive analytics in gaming:
- Game development — identify optimization points for product and marketing teams to make optimizations
- Monetization — make predictions on behavior that lead up to purchases (i.e. freemium to paid subscriptions)
- Game design — use algorithms to determine the best ways to keep players engaged
- Game experience — help determine visual effects and graphics that are most likely to resonate with players
- Personalized marketing — determine the messaging that will best resonate with individual players
- Fraud detection — validate that players are who they say they are and avoid problematic behaviors before they have a chance to happen
Eventually, predictive analytics will become as commonplace in the gaming industry as big data — that’s the future for which we’re preparing at Indicative. Now is a crucial time for all gaming companies to get their data infrastructure right
Selecting a Gaming Data Warehouse
The performance of your long-term gaming analytics strategy depends on the foundation that you establish with your data warehouse. Depending on your analytics stack, your data warehouse may also serve a dual purpose as a business intelligence system
Examples of data warehouses include Google BigQuery, Amazon Redshift, and Snowflake. Over the past few years, data warehouses have transitioned from server-based to cloud-based, making it easier for companies of all types and sizes to retain and share data. The data warehouse makes it possible for other applications, such as Indicative, to ingest and interpret that data.
High-performing data warehouses make it possible to manage data without engineering resources. To learn more about data warehouses for general gaming use cases, check out the above-linked article from Indicative, where we share tips for selecting the right one.
In short, the right data warehouse will help you:
- Manage disparate data sources in the cloud
- Process both structured and semi-structured information
- Support high concurrency and high data volumes to generate constant insights
- Maintain governance over your data to support global data privacy and regulatory standards
- Support fast-moving analyses
- Analyze and enrich customer data
Depending on their products’ complexity, some gaming companies may opt for multiple data warehouses to support different use cases.
One consideration to remember is that your existing BI and customer analytics infrastructure may not reflect your needs in the medium to long term. When designing frameworks and choosing solutions, keep in mind that your business may evolve. Planning for unknowns, as early on as possible, will be essential.
As part of your analytics stack, a data warehouse is also valuable for getting your business future-ready — so that you can build sophisticated predictive analytics capabilities.
Game Analytics Data Pipeline
Your data pipelines describe the flow of information between your data sources and your data warehouse
To understand this concept in greater depth, take a look at this blog post from Ben Weber, a distinguished data scientist at Zynga. He explains that a data pipeline should have the following properties:
- Low event latency: Teams should be able to analyze data within minutes or seconds of an event being sent to your warehouse.
- Scalability: As your gaming product scales, a data pipeline should be able to scale to billions or even trillions of data points.
- Interactive Querying: It should be easy for users within your company to immediately run analyses without an understanding of your database schema or architecture.
- Versioning: You should be able to make changes without the risk of data loss.
- Monitoring: Alerts should be programmable in case a data pipeline ceases to record events
- Testing: You should be able to run tests on events that do not end up in your data warehouse, database, or data lake.
Weber emphasizes that every company will need a person or team to monitor its data pipeline. Centralized process ownership will reduce the potential operational hiccups for errors. In addition, a well-defined process will involve routine inspections of data quality, as well as changes to information.
To learn more about setting up a game analytics pipeline, contact the data warehouse partner that you’re using or considering using.
For example, AWS maintains a Game Analytics Pipeline solution to help developers launch a scalable, serverless data pipeline to ingest, store, and analyze telemetry data. The solution makes it easier to centralize data from across applications into common formats for integration
Game Analytics pipeline tools are also available with Azure, which offers a more holistic development ecosystem for game development. Visual templates for CI and CD pipelines are part of this solution.
Data Analytics Applications in the Gaming and Entertainment Industry
Looking Ahead: Online Gaming Trends
The gaming industry has surpassed the value of the music and movie industries combined. In the United Kingdom alone, as of 2019, the industry has more than doubled its value.
“Growth has been fuelled by the dominance of free content and in-game monetization, which expands the adoption of games but also removes the cap on spending for those gamers that are engaged in the experiences,” says IHS Markit’s head of games research, Piers Harding-Rolls in an interview with the BBC.
“The flexibility of interactive content means it is unique in that it can be monetized in this way, which is an advantage over other forms of entertainment.”
One of the biggest factors driving this trend has been the distribution of games from physical to digital.
With the gaming industry becoming the world’s dominant form of entertainment, there’s a question of what the future gaming experience will look like.
Here are a few trends to keep in mind, according to an article from Forbes:
- Experiences that mix augmented and virtual reality technology
- Incremental improvements to technology rather than big jumps
- Expanded use of AI not just within the game but during the development process
To support this incremental evolution, your gaming analytics strategy will be mission-critical. To capture market share, especially in light of competition for attention spans, a key differentiator for successful game developers will be the ability to deliver unique media experiences.
The Mobile Narrative
Mobile is the platform through which the gaming industry will continue to expand its reach and pursue growth. Here are a few stats to illuminate the scale of this market:
- The mobile gaming industry is expected to hit $159.3 billion in 2020
- It’s estimated that ⅓ of the global population plays mobile games, with the largest concentration of gamers living in Asia
- One forecast says that
With this perspective in mind, best practices for gaming analytics are still being defined. It will be helpful for your entire organization to have a perspective into this fast-moving landscape, in which data is moving faster than industry standards can keep up.
Final Thoughts
The decisions that you make for your gaming analytics strategy today will create the foundation for your company’s longevity.
Getting your data infrastructure right is critical. That means making sure that your data is easy for anyone at your company to understand. That also means making sure that everything behind the scenes is functioning as it should.