For a game studio, data stopped being an end-of-month report. Whether a player stays, how much they spend and where they get stuck on a level is decided in the events the game sends every second. The catch is that player analytics rarely fails at the level of the idea, and almost always at the level of engineering: scale, data quality and whether the team keeps pace with production.
In our experience, the hard part is not deciding what to measure but delivering a pipeline that carries traffic from the whole player base and hands the team trustworthy numbers on time. Even if we do not show a case study here with a finished retention dashboard, we have the people who do this and real competence in stream processing, including a gaming project built on Apache Kafka. This article shows how we approach the problem.
Why is game data so hard to tame?
A game is one of the most demanding data sources an analytics team will meet. The scale can be extreme: a popular mobile title may emit billions of events a day, and each player session is dozens of events, from level start to microtransaction. On top of that comes variety: a purchase event looks nothing like a combat event, and the schema shifts with every patch.
Game data is also dense in time and context. A single tap means little; only a sequence of events in a session tells the player’s story. So raw telemetry needs a model that organises events into sessions, levels and cohorts before anyone sees a chart. Without it, the team drowns in data that compares to nothing.
How do you build the event pipeline and player telemetry?
The foundation is game telemetry, the event contract between the game client and the backend. We design it to be versioned and change-tolerant, because today’s level_completed schema has to coexist with a version from three patches ago. Events flow from the client and the game server into an ingestion layer, and from there into stream processing.
The heart of that layer is usually streaming built on Apache Kafka. Kafka buffers the incoming events, splits them into topics (purchases, progression, sessions) and lets several consumers read the same stream independently. On that base we build:
- the event ingestion layer (client SDK, collection API, schema validation),
- stream processing (Kafka, in-flight aggregations, event enrichment),
- an analytical store (a warehouse or data lake for ad hoc queries),
- a serving layer feeding dashboards and models.
By separating the stream from the store, the same events feed a live report and a model training run in parallel, without blocking each other.
Which funnel and retention dashboards give the team a real picture?
Once data flows, the team needs it in a form that leads to decisions. We build dashboards around the two questions that keep a product director up at night: where we lose players and whether they come back. The onboarding funnel shows at which step of the first session players drop off, often a single tutorial screen decides the fate of an install worth a marketing campaign.
The second axis is cohort retention. We cut the standard D1, D7 and D30 figures by install date, acquisition channel and game version, to tell a patch effect apart from a campaign effect. That view turns a vague “retention dropped” into a concrete “the cohort from the last update drops at D7 on the new boss”.
How do you stand up A/B testing infrastructure?
In-game decisions deserve to be settled by data rather than instinct, which makes A/B testing infrastructure a separate engineering concern. You need a layer that assigns players to variants (deterministic and stable across sessions), a feature-flag mechanism that switches a variant without shipping a new build, and statistical rigour on the analysis side. We wire this into the event pipeline so that exposure to a variant is itself an event, which lets you honestly measure the change’s effect on conversion or session length. Without that discipline, tests produce numbers you cannot trust.
How do you report game balance and economy?
A free-to-play game is a living economy, and its economy balance reports are as much analytics as a designer’s tool. We track the flows of in-game currency, the pace of earning and spending resources, reward inflation, and the points where players hit a wall. We break currency sources and sinks down by segment, because the game plays one way for a paying whale and another for a non-payer. A good economy report lets a designer tune a drop rate or an item price before broken balance drags down retention and revenue.
How do churn prediction models work?
The most interesting part begins when you move from description to prediction. Churn prediction is a machine learning model that estimates, from a player’s behaviour, the probability that they will stop playing. We build features from the telemetry stream: session frequency, the trend in playtime, progression pace, reaction to defeats, purchase history. The model (from regression and gradient boosting to networks over event sequences) returns a score that the CRM team turns into action: an offer, a notification, a challenge matched to the moment.
Such a model is worth something only when it is operational, not a slide in a deck. So we design it for deployment from the start: regular retraining, data-drift monitoring, and a loop in which the success of a retention action feeds back into the model as another feature.
Why does data quality decide everything?
Every dashboard and every model is worth only as much as the events that feed it. That is why we treat data quality as its own layer, not an afterthought. We watch a few things:
- schema validation on ingestion, so a broken event from a new build does not poison the reports,
- handling duplicates and late events, common on an unstable mobile connection,
- consistent metric definitions, so “active player” means the same thing in every team,
- volume monitoring that alerts when the event stream suddenly goes quiet or balloons.
It is thankless, unglamorous work, and it decides whether leadership trusts the numbers.
Which skills do we provide for player analytics?
We shape the team around the specific stage, not “just in case”. On data and streaming projects we most often staff:
- a data engineer building the event pipeline and streaming (Kafka, stream processing),
- a warehouse / analytics engineer modelling data for reporting,
- a data scientist / ML engineer owning the churn prediction models,
- a product analyst connecting the data to the studio’s business questions.
The strength behind this line-up is the RITS Group: Big Data and stream-processing competence, plus our AI/LLM/data teams, which deliver AI, LLM and machine learning projects. That backing lets us staff both the data engineering and the model layer.
How do we do it at RITS?
We work as a nearshore partner from Poland that adds competence where the studio’s team lacks it or cannot keep up. In practice, our engineers plug into the existing pipeline and develop it further rather than rewriting everything from scratch. We have real experience building event streaming on Apache Kafka, including a gaming project, and in Big Data and ML projects across the Group. We verify skills before the start and shape the team around the specific stage: standing up a pipeline from the ground up calls for a different line-up than adding a churn model to working analytics. We work in a close time zone, so the team joins your code reviews and meetings without a long ramp-up.
Where to start with player analytics and telemetry?
Start from the single business question that hurts most, for example “why do we lose players in the first week”, and from the events you need to answer it. That defines the shape of your telemetry, the pipeline architecture and the roles you need, before you invest in models. We covered the differences between engagement models in our article on IT engagement models.
Building player analytics or telemetry in your game? Let’s talk, we’ll match a team and an architecture to your data scale.