Gaming Analytics: How to Leverage Your Customer Data for Sustained Business Growth

Written by Indicative Team


Gaming data is one of the most valuable sources of information out there. 

With 2.7 billion video gamers around the world — and 16% of U.S. gamers’ weekly leisure time going to playing games as of 2018 — companies are sitting on treasure troves of insight. You need an analytics solution and data strategy that can help make that information useful. 

  • What are the stories that your players’ behaviors are telling you? 
  • How are you using this information to build a high-performing business? 
  • Are you getting the most out of the insights that you’re collecting?

Indicative aims to help companies find more value in their data — and how those numbers tie back to key performance metrics — in your business. 

By the end, we hope you’ll come away with tips for building an effective Customer Analytics strategy for your company’s product and marketing teams. 

What Is Gaming Analytics?

A well-defined gaming analytics strategy empowers teams and individuals to make data-informed decisions. At any moment, everyone at a company should know how their actions contribute to revenue-focused outcomes.

The video game industry is evolving at a rapid pace, which puts pressure on gaming companies to perform. The climate is an intense one, with players having a seemingly infinite number of options for ways to spend their limited free time.

To overcome barriers to success in the industry, teams within gaming companies need to look beyond the immediate moment — to work smarter, not harder, to uncover timeless stories that build brand equity while contributing to valuable growth metrics.

Here’s how one analyst eloquently explains what we mean:

“As compelling as the storylines are, as realistic as the graphics are, and as atmospheric as the music is, it is the data – the unsung hero – that is making waves in the gaming industry,” writes Dan Robinson.

With this context in mind, gaming analytics is the process of applying user behavior data to guide marketing, product, and business decisions. For a gaming company, the users are gamers. The insights that Indicative generates can help teams make decisions to enhance game design, monetization, and business impact.

A high-performing gaming analytics strategy will help everyone on your team ask and answer the following questions:

  • What is the number of daily active users (DAU) in a game? 
  • How many active players are there in a month (MAU)? 
  • Who were the new users last month? How often did they return? 
  • At which game level do players get stuck? Does this cause a drop-off in-game usage?
  • What in-app or in-game purchases were made? What kind of events pushed them to purchase?

These are nuanced questions that, although possible, would be tough to answer programmatically — very few people, even skilled data experts, are able to write queries at the speed of intuitive decision-making. In this regard, gaming analytics is also about data usability. 

Data is only as valuable as your company’s ability to understand and act upon it.

Customer Analytics: a Place for Teams to Find Common Ground

The goal of Customer Analytics is to increase access to and understanding of data, making it easy to identify patterns. With this shared insight, everyone at a company can work towards building a healthier business.

One of the most impactful steps that your company can take is to create a shared dashboard that connects employees across your organization to a single source of truth. That way, everyone can understand how their individual actions lead up to shared organizational goals

Indicative dashboards allow you to host a collection of KPIs and metrics that update in near real-time so you can keep track of regular business performance.

To start, it will be helpful to create a common understanding of a core revenue story. DAU (daily active users), MAU (monthly active users), and ARPU (average revenue per user) are simple KPIs that are easily measured using an analytics platform like Indicative.

Using this information, you can also explore customer journeys in real-time. Let’s say, for example, that you want to analyze how your most valuable players (those with the highest ARPU) are finding their way to your registration, game download, email campaigns, etc. You can visualize the answer to these questions within Indicative.

Maybe you notice a spike in daily active users and want to explore the impact on company revenue. You can use Indicative to understand the dynamics behind this trend. 

Your customer analytics strategy is built on finding common threads between people, their behavior, and revenue.

The Role of Big Data Analytics in Gaming 

As you implement a customer analytics strategy for your gaming company, you’ll likely come across the phrase “big data.” As IBM puts it:

“Big data is a term applied to datasets whose size or type is beyond the ability of traditional relational databases to capture, manage and process the data with low latency. Big data has one or more of the following characteristics: high volume, high velocity or high variety.”

While early innovation around big data took place within large enterprises and e-commerce companies, capabilities are now available to startups, small businesses, and mid-market companies across a range of industries to better understand customers, improve product performance, optimize marketing, and more.

In gaming, big data is particularly important for tracking trends, diagnosing problems, and improving game design. The role of a well-defined business intelligence and customer analytics strategy is becoming increasingly valuable for gaming companies.

Consider the story of King Digital Entertainment, the makers of Candy Crush, as an example

“Users were massively abandoning level 65, reasons unknown. With 725 levels in total, for Candy Crush Saga such a tendency was quite a trouble. King turned to data analysts to reveal that most people were abandoning because of a particular gaming element that didn’t let users make it past level 65. The element was deleted, and user retention got moving again.”

Candy crush game developer

The goal of big data is to bring a deeper level of perspective to your gaming analytics strategy. To gain this deeper level of insight, you’ll need both back-end technology and a customer analytics solution to make that information interpretable.

Predictive Analytics in the Gaming Industry 

Over the last several years, enterprise players in the gaming industry — for instance, Microsoft— have been recognizing the value of data analytics. So, they have been acquiring companies that provide access to player data.

The resulting challenge is that smaller developers find themselves struggling to keep up with the data science, marketing, and product R&D budgets that are commonplace within large companies.

That’s why gaming companies are increasingly looking towards solutions like predictive analytics that anticipate actions that players will take — to get ahead of potential competition in the market.

analytics maturity curve displayed on a graph

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

Some gaming companies, depending on the complexity of their products, 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 over time. 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 to 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 really 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:

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.