How well a game is in bringing in, retaining and monetizing players is often easy to improve when you carefully analyse what happens in these stages of the lifecycle. Measuring and analysing player behaviour and -flows brings along new insights that can be used to improve the game.
Assistance with all steps of the analytics and telemetry implementation. The most important question here is: what measurements will deliver the most meaningful information? After this is translated to what should be measured, we pick the best fitting tool for the job. Honestly and independently. When implementing analytics in the game we assist with a clear overview of which events should be measured.
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The abbreviation ‘KPI’ is frequently used nowadays. These important indicators (Key Performance Indicators) are a numeric summary of your game and its commercial success. Summarizing data correctly, like for KPI’s, can be a real challenge. A mistake is easily made. Aggregation data is the first step of any kind of analysis. And it has to be done right. When we look at the aggregated data together, points of improvement are quickly discovered.
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With explanatory analysis we look at the data you’ve collected on player behaviour within your game. Which conclusions can we draw from that, and which points of improvement do they bring to light?
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Experiments like A/B and multi-variate testing are the best ways to test changes and to ensure that whatever you imagine to be an improvement actually has a positive impact on players. In an ideal situation you always have experiments running, from buttons, to menu's and difficulty. It's easy to draw a conclusion, but that conclusion is just as reliable as the data itself.
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There are countless opportunities on the intersection of data and communication. When you use behavioural data to enrich the conversation with individual players, CTR will go up, conversions will improve and more players remain engaged. Reach out to all your players with the perfect message in terms of personalisation, timing and content. Commonly used channels for this are e-mail, push notifications, in-app messaging systems and social media.
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There is much more to read from player data than what they have done. It can be very lucerative to look at what they are about to do. By means of regression analysis we can predict for example which players are likely to leave your game or spend money. This gives you the opportunity to adequately anticipate this.
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