Using the Explore Tool
Explore is an enhanced, isolated workbench for your data.
With it you can:
- view core metrics, event metrics and custom metrics tracked by the GameAnalytics servers
- change visualisation options
- apply multidimensional filtering
- export data to CSV
Select any predefined core and custom metrics to visualize them as you see fit. After selecting your metrics, pick the aggregation, visualisation methods and filters that you want. Note that depending on the metric, the aggregations methods you can chose will be limited.
Event MetricsCopy link to clipboard
Win/loss ratios are great ways to figure out how players are progressing through the game, but what if you want to tell how many times players performed a certain action in a level?
You can now easily get this information by using events as metrics.
Combined with filtering and splitting, this will enable you to compare the rate at which certain actions are triggered by users who start a specific level.
- Any event type (business, design, error, progression, resources) can be used as a metric.
- Filters can be used to select a specific – or a combination of – events you want to measure
- Some event types include numerical values, such as scores, virtual currency counts, etc., and sum and mean calculations can be applied to them. Events without numerical values attached won’t show a sum or mean, but you can get their count.
FiltersCopy link to clipboard
Filtering works by picking a dimension, event or config type (like Design Events) and then fine tuning the specific values on what you want to see (like only viewing Tutorial events).
- You can access filters by selecting the plus icon in the filter bar, next to the date picker:
- The filter picker will then drop down, allowing you to search for and select dimensions, events or configs
- Select the values that you want to filter on and click apply
Multidimensional FiltersCopy link to clipboard
With multidimensional filtering, you’ll be able to add as many dimension and event types as you like (like Country and Platform, or Device and Build…), and then be able to add as many values that are available for these dimensions (like selecting US, UK, and DE).
This will result in 1 line chart being displayed containing all of the data matched by all of the filters applied. It will not automatically split the data.
Note: You can only add one event type filter at a time. For example, you can’t filter on both Design Events and Progression Events at the same time.
How To Use It:
Let’s say you wanted to create a filter that includes certain iOS devices andplayers who live in the US. You can set this up by:
- Select dimension type = Device, and then press all of the iOS devices needed
- Go back, and select dimension type = Country
- Press United States and hit apply
You can see the multidimensional filter we just created in the below image, and the data is displayed all in one line. This will work on all dashboards, as well as the explorer tool.
Graph OptionsCopy link to clipboard
There are several ways in which you can manipulate your data when visualising it. Depending on the data, you can choose to look at it as a timeline, dimension – if any is selected – or value, if a dimension or event is chosen.
The toolbar above the graph allows for tweaking the data plotted. You can sort by either the x or y-axis, the values displayed or de- or ascending.
If displaying unit-alike values on the two y-axis, Explore allows you to pair these to make the graph more apprehensible.
Comparing MetricsCopy link to clipboard
Seeing how many times users triggered an event is great, but to get the full story you also need to know how that behavior impacts other metrics.
Do players who meet a special character convert better or worse? How does that change for each character type they meet? These questions can now finally be answered, with Explore’s new comparison mode.
The comparison mode enables you to add a second graph, and select any metric you want to plot on it. All filters applied will then influence both graphs, so you can keep your tracking consistent without doing everything twice.
In this example, we used a combination of progression events, conversion rate, and custom dimensions as filters to compare how two characters influence conversion.
Although in the first graph one of the characters, the journalist, is clearly more popular, the florist sometimes has better conversion, though not consistently so.
Why is this important? Well, perhaps polishing up the character a bit would increase conversion further, and keep it stable. But, more importantly, it’s now possible to learn these types of insights in just a few clicks.